From United Nations, “Why women and girls are still not equal under the law”

“International Women’s Day 2026 shines a spotlight on a critical issue: inequality in justice systems around the world. Despite decades of progress, women and girls are still not equal under the law anywhere on Earth. Globally, they hold only 64% of the legal rights that men enjoy, according to the latest UN data. This gap affects every stage of life — from safety and education to work, family life and economic opportunity.”

From UN Women, “No country in the world has reached full legal equality for women and girls”

“On 8 March 2026, International Women’s Day, UN Women issues a global alert: Justice systems meant to uphold rights and the rule of law are failing women and girls everywhere. Women globally hold just 64 per cent of the legal rights of men, exposing them to discrimination, violence, and exclusion at every stage of their lives.”

There are more details in the United Nations Secretary-General’s report, “Ensuring and Strengthening Access to Justice for All Women and Girls”.

This is (still) not ok.

So by all means, let’s celebrate International Women’s Day and highlight women and girls who are persisting and excelling. I’m seeing so many good callouts in Notes the past few days (like this one initiated by ), and it’s uplifting. But let’s not forget that the work towards equality needs to continue tomorrow, in AI and in every other field and part of life.

And it’s starting to take off. We are already the #1 Hot New Release in Social Aspects of the Internet and in Human-Computer Interaction! (Thank you to for letting me know)

You can pre-order your ebook copy now at the special, limited-time intro price of only US$0.99 via universal link mybook.to/aieverywhere1. We already have pre-orders from Mexico, India, Canada, Italy, Germany, and the UK, in addition to the main .com store. Preorders will also be available soon for print editions. Find out what everyone’s so excited about, and please tell your friends before this special introductory offer expires!

Preorder your ebook for US$0.99

Our book is 100% written by 26 women and nonbinary authors from our SheWritesAI community, spanning 14 countries and 24 topics in AI. It’s the first of its kind and shares fresh perspectives and tips you won’t find anywhere else. Learn more about the chapters and the authors here: aiEverywhereBooks.com (Kudos to for the website design and development!)

Learn more about "AI Everywhere"

Coming soon: interviews with the chapter authors by . (To start, I’ll be interviewing Farida next week about her two chapters, on AI in cybersecurity and agriculture. And she will be interviewing me about the book.) The interviews will be posted to the newsletter. Subscribe there (free) to be in the loop, and stay tuned!

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Subscribers and followers know I’m deeply committed to making AI work for everyone, not just the 8-figure tech bros, and doing what I can to amplify the voices of under-represented folks in tech. Here’s what I’ve been up to.

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Since July 2025, I’ve been curating the first collaborative book, AI Everywhere (Vol. 1). Chapters are now in and we’re working on reviews, editing, layout, and cover design. It’s coming soon — we’re aiming for International Women’s Day, March 8 — and we can’t wait to share it with you all!

All 24 chapters are written by 26 women or nonbinary authors from the She Writes AI Community. They represent 14 countries and cover a wide range of topics in AI and data. (For a sneak preview of who’s writing on what topic for the book, see shewritesai.substack.com/p/book1 or the table below.)

I want to take this opportunity to thank our chapter authors for their exceptional collaboration. Not only are they contributing amazing fresh content for the book, they’re supporting each other to improve the quality of the book.🙌🏼 A few call-outs:

• , , , and have been especially generous with their time on peer reviews

• is working with me extensively behind the scenes to help make the book a reality.

• is supporting us on marketing.

This book is going to be an AMAZING resource on AI for people of all genders, thanks to everyone who’s contributing!

🙋🏼 Allies who are interested in supporting our release and launch week to help make this book a success: we invite you to📧reply to this email, 🗨️drop a comment on this post, join us in the 👥 SheWritesAI Subscriber Chat, or 📤DM me or She Writes AI.

Stay tuned for the cover reveal and more details about the book release!

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Art: Aisha Belarbi

Aisha creates furry art (animals with human characteristics). Now that “people can just generate whatever they want”, she now earns her living by writing books about how to draw. “This is my livelihood at stake, and a lot of other people’s livelihoods,” she adds.

Video: JP Allard / MirrorMe

MirrorMe’s product, he says, replaces “every form of corporate media.” He acknowledges the impact on other videographers. “The problem is the velocity of change. In the past, we had five or six years to take typewriters out and replace them with word processors and PCs. Now it’s happening in months.”

Music: Ross Stewart

His mum sent him an “AI album” of blues music to listen to — “one of about 30 albums that have been released just this year by that one artist“. He sees this speed as “posing a danger — it’s affecting songwriters, producers, musicians”.

Writing: Niki Tibble

Three years after having children, on returning to the workforce, this copywriter found “AI had taken my role” on “smaller jobs” which are now “largely gone”. For now, she works on checking and improving on AI-generated copy, but questions how long her job will last.

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Image credit:

To learn more about impact of AI on lives and livelihoods, see Everyday Ethical AI: A Guide For Families & Small Businesses. This is one of the five key concerns covered in my book, with practical tips for five things we can do.

Bonus link: an interview and review by :

Busy Book Builder ☕️

How to Navigate AI in an Ethical Way

AI is everywhere these days with the majority of social media content being either written or influenced by generative AI apps like ChatGPT, Claude, Gemini & Co. The imagery we see everywhere shows a mixed world as it is hard to spot the difference between generated and reality. But how can readers build trust with content creators when all we see is a …

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4 months ago · 7 likes · 8 comments · Gunnar Habitz and Karen Smiley

This video pundit claimed that data labeling work for AI pays $20-40/hour (ha!) and only mentioned among the downsides that the work might be “kind of boring”.

• Nothing about having to watch horrific content for hours on end and cope with emotional stress or even PTSD.

• Nothing about how companies offshore this work, pay pennies per piece labeled (not per hour), and bilk people out of pay.

To learn more about data labeling work, and why it’s currently an ethical concern for how many AI companies do business, see AI Ethics: Beyond Not Stealing Data and Everyday Ethical AI: A Guide For Families & Small Businesses.

In the current terrible job market, people seeing videos like this might be tempted to try for these kinds of jobs. They’re likely in for a huge disappointment. I don’t yet know of any ethical data work company that pays fairly and treats their data workers respectfully. If you do, please share!

Leave a comment

A guest article by on ’s AI Supremacy newsletter caught my eye this week.1 It’s excellent, and worth a full read if you haven’t seen it yet. One point that caught my attention was an implicit assumption that solopreneurs and startups using AI would be ‘young’. I raised this point in a Note, which and engaged on. (Thank you both for the thoughtful comments that got this train of thought going tonight :)

The excerpt I restacked from the article: “Being adaptable, agile and being able to pick up from scratch and create a new business is going to become important and many young people in countries where higher unemployment rates exist, might be attracted to this path where the revenue ceiling is higher than a traditional job.”

My note:

“Excellent article, Luan Doan and Michael Spencer. These observations on startups and solopreneurs make sense and I appreciate the insights. I want to gently question the assumption that these startup founders are, in general, young.

Ageist bias in hiring and employment is pervasive. And it’s especially bad in AI roles; I cited a study on this in my Everyday Ethical AI book. Many people pushed out of the workforce by ageism in their 50’s and 60’s (and sometimes even younger for women, due to gendered ageism) are turning to solopreneurship and startup life. They (we) are using AI as well.

Solopreneur age distributions might vary quite a bit in different regions of the world, of course. I’m wondering if it might be a bathtub curve. Do any of you folks know of any reliable, recent world data on this?”

In the comment threads, I see two perspectives which seem contradictory, but I think both are valid:

• that small shops and solopreneurs of all ages are capitalizing on AI, and

• that AI-native businesses may come mostly from other demographics.

Solopreneur update on AI

I’ve completed 80+ interviews with people in a mixture of roles from solopreneurs to huge corporations. I’m not seeing any shortage of AI tool uptake from the one-person shops, regardless of age. And one of the studies I cited in my book Everyday Ethical AI: A Guide For Families & Small Businesses indicated that freelancers in the USA, UK, Canada, and Australia are benefiting more from AI than corporate workers.2

These people are solving problems for their customers and using AI freely when and where it fits their business, without corporate bureaucracy to impede them. They make their own AI tool choices (and take their own risks). They might not be “AI-native”, but I’d call this “AI-wise”. I’m a strong believer in defining the problem before choosing the solution, and AI isn’t a silver bullet that solves all problems. So this problem-first approach to when to use AI (or not) makes sense to me.

AI-native Startups

If we’re talking about people starting AI-native businesses with the specific goal of using AI as the core of their business, and building an AI-based platform or system to sell services to others, I could see that profile as being different.

Older workers are being pushed out of corporate life early by ageism, especially for AI-related roles.3 I would still posit that they could be well represented in that group of AI-native founders. Many of them (us) have been agile and already adapted to multiple tsunamis of technological change. Why would we stop now?

I also called this out in my book:

Assumptions that older people are tech-averse don’t even make sense. Many not only use AI, they’re often self-taught and power users. As Sheila Callaham wrote in Forbes last year:

“How is it so easily assumed that the generation of people who created the internet, computers and cell phones–and have adapted to the endless changes over the decades–suddenly stopped embracing technology?”4

Key success factors for software startups (and AI is software) include:

• building a solution for a problem domain the founder knows well, and

• knowing and using solid engineering practices.

Older tech workers often have both of those assets. (The recent fast launch and quick shutdown of the Neon app due to cybersecurity problems and data leakage is a great example of what can go wrong with an AI-native business when the engineering practices are skimped upon.)5

I know of excellent technical people in their 40s, 50s, 60s, and beyond who are highly capable of starting and running AI-native businesses, and some are. They may be fewer because they prefer solving specific problems (AI-wise style, rather than AI-native). I’d be curious to see recent data, preferably global, on this. If you know of any, please pass it along!

Bottom Line

Whether their business is AI-native or AI-wise, freelancers and one-person shops of all ages can benefit from AI — used carefully.

I’d love to hear from any of you who are using AI for your business, or considering launching an AI-native startup. What are you doing with AI that’s working well for you, or not?

Leave a comment

And if you’d like to share your AI stories and experiences with the world, I invite you to be featured as a guest on my “AI, Software, & Wetware” interview series. Read this guest FAQ and get in touch if you’re curious to know more!

Article References

For more references and coverage on these topics, see my Everyday Ethical AI book and the book website bonus materials.

1

AI Supremacy

How Solo Entrepreneurs Are Rewiring Their Work Habits with AI

One of the big questions I have in terms of the democratization of AI is whether solopreneurs and entrepreneurship as a whole will see a sustained big increased due to Generative AI, specifically vibe coding, AI agents and AI automating more administrative and product related workflows…

Read more

7 months ago · 97 likes · 1 comment · Michael Spencer and Luan Doan

Much of the discussion around AI is polarized and not always constructive. (More heat than light.)

When it comes to AI, folks should be able to:

✅ Shine light on the dark underbelly of how most AI platforms are currently built, and advocate for less-harmful alternatives, without being disparaged or dismissed as fear-driven Luddites.

✅ Use AI tools selectively to help themselves overcome obstacles or keep food on their table without being shamed or cast as immoral. (And no one owes anyone else a disclosure of their disabilities or other perhaps-personal reasons for using AI or any other tool.)

✅ Call out the hype of the 8-figure tech bros as self-serving propaganda or as scientifically unsound without being smeared as envious.

✅ Share ways they’re using AI platforms constructively without being branded as sellouts.

✅ Point out privacy risks and other potential harms without being dismissed as laggards who are out of touch with the modern reality that AI is firmly entrenched in our daily lives.

👉 LESS leveraging and celebrating AI tools without caring for the millions (billions?) of people being exploited to create those tools. Instead of closing our eyes or simply fretting over internal conflict about our use of unethically-developed tools, we can do MORE to offset the harm by taking concrete actions to help affected communities.

👉 LESS bashing anyone who touches an AI tool and MORE allowing that their decision might be ethical and they might already be working to solve the underlying systemic issues. Being the ‘AI police’ can impede potential allies from acknowledging the problems and joining the work.

Fixing our broken, exploitative global ecosystem will be hard, no question. AI is hard too, right? But AI is progressing because people care enough (even if for self-serving reasons, in some cases) to work on it. We can do more than one hard thing at a time as a society. And we must. (Not only in AI ethics … in all of the other ways our current society is unjust, too.)

No one person can do all the things, but we all can do something. We have more collective power than we often realize, and we can’t afford to abdicate it. AI impacts all of us nowadays. Even people who don’t seek out AI tools to use are affected by the risks.

“Nearly all Americans use products that involve artificial intelligence (AI) features, but nearly two-thirds (64%) don’t realize it.” - Gallup poll, Jan. 2025 1

A key first step to building a more ethical AI ecosystem is to help more people to see the issues and acknowledge their existence & importance.

🎁 To learn more about the everyday risks and uses of AI & data today, free:

• Subscribe to my newsletter

• Pick up my 33-page ebook “Five Everyday AI & Data Risks” (giveaway offer expires August 25)

• Get a free Advance Review Copy of my upcoming book “Everyday Ethical AI: A Guide For Families & Small Businesses”, in exchange for some candid feedback — DM me or comment here:

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The directory began here on , my personal newsletter, in December 2024 as a simple bullet list of 34 writers I knew of. The directory has grown tremendously to 400+ in the past 6 months, which is awesome. And I began hearing from volunteers who wanted to help it grow.

So I’ve been looking into ways more people can participate & we can offer more community benefits to directory members. I have two announcements to share:

This permanent post always links to the complete directory and the latest maps:

From now on, ~monthly updates to the directory will be published as separate posts in the She Writes AI newsletter. Here’s the first one:

For an update on the first 6 months of directory entries, see:

To search & use the complete directory, you can continue to use:

💻searchable, filterable directory table

🌐interactive world map & USA state map

To automatically receive updates to the directory and be notified of future community features, please subscribe to ! See you there 😊

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• companies with diverse executive teams make more money (38% in one study)

• diverse teams make better decisions and deliver more innovative products (e.g., 19% more revenue from innovation by diverse teams)

• firms prioritizing ESG (environmental, social, and governance) and DEI efforts tend to be rewarded over the long term with higher stock valuations and shareholder confidence

• inclusive environments expand hiring pools and support happier employees and higher retention rates, reducing turnover costs

Some folks have argued that it shouldn’t be necessary to show financial benefits and ROI from inclusive practices. Some even feel it’s demeaning to try — that people ought to be inclusive and respectful to others just because it’s the morally right and decent thing to do.

Others have argued that since most businesses only care about short-term financial performance, showing that DEI is actually profitable would help to motivate more firms to take inclusion seriously and improve life for more people. And we’ve seen many articles on how to make the business case for diversity, e.g. 23.

We’ve seen similar sentiments and arguments about the Earth’s environment and sustainability 4 5.

I feel like we’re seeing the same pressures on values vs. money in the world of AI ethics … but not yet seeing the same emphasis on building the business case for AI ethics. (I found one exception, from writers in Finland: 6)

Most of the folks in the AI ethics community (including me) have mostly focused on raising awareness about AI ethics and communicating clearly about the ethical concerns around AI. Underlying this focus is optimism that awareness will be enough to convince people to ‘do the right thing’ and take ethics seriously when making decisions about AI-based tools.

However, like with DEI and sustainability, financial drivers still outweigh any concerns about AI ethics for many businesses and people. Until it provably costs them less money or makes them more money to ‘do the right thing’, they won’t.

I’m not saying all businesses and people are wrong to prioritize financial impact.

Granted, some mega-firms are crassly exploiting hype and greed, building on stolen content & with total disregard for ethics, to their immense profit and to the detriment of the rest of us.

But reality is that many households, entrepreneurs, and small businesses are struggling to survive, especially in the current turbulent business weather we’re in. They literally may not be able to afford the effort and tradeoffs required to find and use ethical AI tools - or to avoid using AI tools which are, or may be, unethical.

However, that reality doesn’t mean we should throw up our hands and say, “Oh well, AI ethics are a lost cause”. Instead, like we’ve seen with DEI and environmental sustainability:

1. We can work on making it easier for people to find and use the few ethically developed AI tools (or ethical alternatives) that are out there, and advocate for companies who are already trying to do the right thing.

2. Those of us who can afford to do it can avoid subsidizing unethical companies; we can vote with our money and our devices, and not use or pay for their products and services.

3. We can lobby our governments to create financial incentives for businesses to do the right things — i.e., regulatory structures that make businesses who are reaping huge profits from AI bear the true costs of the content they’ve stolen, the livelihoods being destroyed, the labor they’ve exploited, or the environmental impacts their work methods and products are causing.

4. We can highlight why it’s in people’s own best interests to consider AI ethics when selecting the tools they use and when designing and developing AI-based tools and solutions — i.e., show them the business case.

These four actions have already happened somewhat for DEI and sustainability. Progress and success have been uneven, and at some risk in various regions due to political turmoil, but overall, we’ve still moved forward.

So perhaps we need to adopt a similar 4-part strategy for AI ethics, and work on the business case for ethical AI (too).

This topic was bouncing around in my head last weekend while I finished the first draft of my upcoming book, “Ethical AI In A Nutshell”. Thanks to for the engaging discussion which crystallized some thoughts on the topic and prompted this post! (All of my subscribers will automatically be notified of near-future book release announcements 😊)

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“To rebut the claim that few women write about AI, Karen Smiley located and connected 280 women who write on the subject--including Women of a Certain Age. Read her guest blog to learn more.”

Thanks to for encouraging me to share the origin story for our directory on her blog! SO important for people of all genders and backgrounds worldwide to participate and be heard on developing and using AI and data.

We have found some amazing writers here across a wide range of ages. I highlighted two experienced women in my blog post: and . Check out the full story here: stellafosse.com/women-in-artificial-intelligence.

And we haven’t found them all yet. The next batch of updates to the directory should put us over 330 women and nonbinary folks in 43+ countries who write about AI and data on Substack. Some of the newest additions started their newsletters just this year, which is great to see!

The latest directory, maps, and tables can always be found in this post:

What’s Next?

A few people have expressed interest in expanding beyond a directory to a more active, supportive “She Writes AI” community. and I are working on it — we welcome your involvement and contributions.

If you’re a woman or nonbinary person who writes here about AI and data, and you have ideas on how a She Writes AI community could help you, and/or you would like to participate in building it, please message me, comment on this post, or vote on the poll below. (Substack limits polls to subscribers only, but subscriptions are free.)

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“In many cases, the research suggests women are concerned about the ethics of using the tools and may fear they will be judged harshly in the workplace for relying on them”

A close reading of the study shows a nuanced summary that thoughtfully considers these and other systemic factors and recommends addressing them.

However, the bottom line of the article was basically to recommend that companies encourage women to ‘try harder’ to use AI so their careers won’t suffer.

This article & its title & overall attitude has been chafing me. “Leaning In” is not the answer. I’ll explain why and share what we ought to be doing instead.

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What the Article and Study Said

No surprise to anyone who reads my writing here: I’m all for women (everyone) getting full support to try new tools, like genAI, where they make sense.

The HBS article mentions two systemic root causes of lower genAI use by women (bolded above): concerns about tool ethics, and fears of being judged harshly in the workplace for using AI.

Other root causes for lower use by women, pointed out by the full study, include the fact that current AI tools are trained on biased content.

However, in the conclusions of the article, the authors don’t push for actions to be taken on these root causes. They mostly encourage “addressing the usage gap”.

To me, that feels like blaming women and telling them to suck it up and just use unethical, biased AI tools anyway, or their careers will suffer. I’m not on board with any of that.

I do agree that it’s absolutely important for people of all genders and demographics to be able to participate fully in developing and shaping AI tools, the sooner the better. And the recommendations in the study are fine. What I don’t agree with is the advice in the article about how to achieve it.

The Real Answer for Two Root Causes

➡️ When AI-based tools aren’t ethical, forcing concerned women to override their moral compasses to use AI isn’t the answer. Getting the AI companies to prioritize bias mitigation and develop & deliver AI tools ethically is the answer.

Bonuses:

• Everyone (including men who also care about ethics and biases) will get more ethical, less-biased tools to use, and

• Everyone currently being exploited in how most AI tools are developed & used would be treated fairly. [For insights on exploitations and unfairness in AI tool development, see my March 11 article, “Top 5 things to know about AI ethics”.]

➡️ When women do hesitate more than men to use AI because they fear — with good reason! — that their competence will be judged more harshly than men’s, the answer isn’t (just) to tell women to use AI anyway and deal with the harsh judgments. The answer is to address the underlying biases that cause people of all genders to judge women more harshly than men for similar actions.

Bonuses:

• Women will be treated more fairly in areas beyond AI too, and

• Society will reap the benefits of greater involvement and contributions of women in our economy. [From the full study: “a persistent 25% usage gap could result in hundreds-of-billions of dollars of lost productivity and growth in the US alone”.]

I’m not saying these two root causes are easy to address. Clearly they’re not.

But that’s no excuse for mostly ignoring the root causes, and simply pushing women to compromise their values and get ready for more undeserved criticism. The full study report covers this, but the summary article glosses over it. We need to address these root causes along with other factors that may be holding women back from using AI. We’ll all be better for it.

Limitations of the Study

I also have questions on the relevance of the findings. The study covers 17 prior studies and adds an 18th new study based in Kenya. The report correctly points out that since “women tend to work in different types of firms, jobs, and occupations than men, they may be less exposed to this new technology.” Some of the 17 source studies did not compare AI attitudes among men and women within similar roles at similar experience levels, though.

One of the summaries compares 5017 “US Working Professionals” found via Glassdoor. Absent information about the composition of the group, that’s unlikely to be a well-balanced group with respect to roles and levels of experience for US-based people of all genders.

In one study by BCG of 6558 “Tech Workers”, women were found to be more likely to use generative AI than men, specifically more senior technical women.

“The exception is a Boston Consulting Group (BCG) study showing that women are 3% more likely to use generative AI than men in a convenience sample of 6,558 tech employees (Barisano et al. (2024)). This positive gap is driven primarily by women working in senior technical roles, whereas junior women are significantly less likely to use generative AI than junior men (see Appendix A). This suggests that experience working in and using technology may play a critical role in eliminating the gender gap.”

Other Considerations

Aside from ethics, biases, and being judged harshly for using AI, there may be other underlying reasons why women are reported as using generative AI tools less often.

Privacy: One example that comes readily to mind is being cautious about sharing photos or personal information. The prevalence and consequences of deepfakes and misuse of photos of women and girls could — again quite understandably — make them more reluctant to use genAI tools on their phones.

Free Time: Another reason might simply be that women, who often work an unpaid “second shift” on responsibilities at home, may have less free time to use genAI tools (or other apps!) on their phones, or to learn & use genAI tools ‘on their own time’ for their jobs.

Cost: Women are generally paid less than men across the board, and worldwide to varying degrees. Not all genAI tools are free, and for those that offer free plans, they are often limited.

Appeal: I also wonder about the Meta ads Berkeley used to attract the Kenyans when evaluating their interest in learning use ChatGPT. Given the low percentage of women who responded (73.9% male), I’m curious about what care was taken to ensure the ads weren’t slanted towards stereotypically male interests. [It reminded me of older studies claiming that girls simply don’t like to play computer games as much as boys do; but when girls are offered games and characters that resonate with their interests and values, they play games just as much. And harassment in social game environments is recognized to impact gameplay and experiences.]

Workload and Productivity: It’s also worth noting that there’s a big disconnect between CEO expectations of AI benefits and people’s experiences with AI use, in terms of impact on productivity. Over half of people of all genders frequently experience burnout due to overload at work. Since it’s still common for women to be expected to handle ‘extra’ tasks at work, their workloads may already be even heavier. Those who decline to use AI may be trying to manage its impact on their workload!

"Despite 96% of C-suite leaders expressing high expectations that AI will enhance productivity, 77% of employees using AI say these tools have added to their workload, and nearly half (47%) of employees using AI report they do not know how to achieve the expected productivity gains."

I didn’t see any mentions of privacy, deepfakes, overall phone app usage, cost, or workloads in the study report.

My Experience

I’ve interviewed over 50 people worldwide since August 1 about how and why they do & don’t use AI in a variety of roles. This interview series wasn’t intended to be a rigorous research study; my goal is to find & amplify the voices of real people whose experiences with using AI - or not - deserve to be more widely heard. It’s obviously a much smaller sample, but it’s about 60% women, in a mix of technical and non-technical roles and across a wide range of ages & experience levels. And there’s obviously potential for some self-selection bias in who volunteers or agrees to be interviewed. But qualitatively, I’m not hearing gender-based hesitancy with genAI at the levels these studies are reporting.

Bottom Line: 3 Recommendations

1. If you’re interested in this topic, read the full study, and not just the article.

2. Regardless of your gender, I encourage you to look for ethically developed AI tools by companies who have shown that they care about addressing biases. The more we as consumers use our attention and money to support companies with the ethical behavior we want, the more likely we are to get ethical tools.

3. Stay tuned for the results of some data analysis on the first 50+ interviews from me and in . In the meantime, check out my 2024 year-end retrospective on the first 36 AISW interviews.

Drop a tip in the jar!

References

This article is an updated version of this LinkedIn post, written after I read the full HBS study and some of the studies it evaluated. It also incorporates thoughts from a recent post on Substack Notes.

“Women Are Avoiding AI. Will Their Careers Suffer?”, featuring Rembrand M. Koning. By Michael Blanding / Harvard Business School, 2025-02-20. [full study: “Global Evidence on Gender Gap”]

“Upwork Study Finds Employee Workloads Rising Despite Increased C-Suite Investment in Artificial Intelligence”, 2024-07-23. [full study: “From Burnout to Balance: AI-Enhanced Work Models”]

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If you’re more of an AI power user or developer, you might prefer this deeper dive, with extensive references: “Five Concerns in Ethical and Responsible AI (and how most tech companies fall short)”, 6 'P's in AI Pods (AI6P).

Top five things to know about AI ethics

Table of Contents

Overview

Concern 1. Adverse Environmental Impacts

Concern 2. Unethical Data Sourcing

Concern 3. Exploitation of Data Workers

Concern 4. Harmful Model Biases

Concern 5. Impact on Lives and Livelihoods

The Bottom Line: What We Can Do

References

Overview

AI algorithms and companies need data and computing power. For those who use AI, but aren’t familiar with how the tools are built, here’s a simplified view.

Here’s a quick overview on these 5 steps in delivering AI tools. AI companies:

1. Build huge, high-capacity data centers. (Or contract with a company that has these data centers.)

2. Acquire large volumes of data for ‘training’ AI models in those data centers. Data sources include: ‘scraping’ the public web; ‘data brokers’ who collect and sell our data; licensing data from other companies (such as book publishers or music labels); and people who already use their tools.

3. Process the data to get it ready to use for AI. (For instance, a song might be labeled with its genre, or a video might be labeled as offensive.) This is done partly by human ‘data workers’, partly by using AI models for labeling.

4. Have AI developers build models with the data, evaluate them for accuracy and fairness, and tune them.

5. Offer AI tools with features that use these models, and collect more data on how people use the tool and features.

As shown in the diagram above, ethical concerns arise in each of these 5 steps. Let’s look at each one.

1. Adverse Environmental Impacts

AI algorithms need data and computing power. Inefficient algorithms or wasteful use of AI drive demand for even more data, power, or both. Environmental and climate impacts are a key factor in cost-benefit analyses of AI technologies, especially generative AI.

On the plus side, AI (mostly not generative AI) has the potential to help humans find more efficient ways to design and operate all kinds of systems.

However, current AI algorithms, systems, and levels of demand need massive data centers. Data center construction and operation consume water, power, and rare earth minerals, and create hazardous waste. When data center power is generated unsustainably, CO2 emissions or nuclear power risks increase, and the impact & risks to the environment are even higher.

You may have heard buzz about AI training costs. While they can be steep, people’s high levels of using AI tools (‘inferencing’) actually consume far more power and water than training.

Environmental concerns can potentially be addressed in several ways:

• Developers can make AI itself more efficient.

• Companies can increase power capacity for data centers in sustainable, low-impact ways, e.g. solar, wind, water, or geothermal

• People can use AI tools more judiciously or more efficiently, reducing demand

2. Unethical Data Sourcing

AI tools are developed and run on massive amounts of data. This can include written texts, images, videos, or other ‘modes’ of data. Most of this data comes from us humans!

Most people worldwide believe we all have inherent rights to the “3Cs: Consent. Credit. Compensation.”2 for our data. However, ownership rights can get complicated when people’s data is:

• stored in online platforms that tech companies control,

• released under a legal agreement (e.g. a contract between a musician and a label),

• collected from people without their full, free, and informed consent.

Many concerns have arisen on whether companies who obtain data for AI are sourcing it ethically - i.e. whether consent is obtained and whether the people are credited and / or compensated.

Most AI companies are not open about where they get data. However, most AI tools are built on stolen data. Even worse, some datasets used for AI training (e.g. LAION-5B) are polluted with harmful content, such as CSAM material.

There are many ways to give creators consent (opt-IN), and to credit and compensate them for use of their works. We can and must expect AI companies to do better in order to earn our business.

3. Exploitation of Data Workers

Raw data generally needs processing to be used for training AI and in operations of AI-based systems. While much data processing is automated, some isn’t, and human labor is needed to make the data and tool useful.

Data work can offer people in job-scarce areas remote opportunities to support themselves and their families. However, some companies mistreat data workers. For more information, see our recent article in about this: “AI ethics: beyond not stealing data”

4. Harmful Model Biases

Biases permeate our society and lives; 12 common types are recognized. (Example: biased performance feedback on the job.) Many social biases are ‘unconscious’ or implicit.

Well-trained and carefully-developed tools, including AI tools, could help to identify and mitigate biases in many everyday work and life situations. For instance, a recent article by Zieminski highlighted how use of data and AI tools could help to improve objectivity in business decision-making and reduce friction in prioritizing product features.

When developing an AI-based tool, biases can be introduced, worsened, or mitigated in steps 2-4: data sourcing, data labeling (by humans or AI models), and model building. At best, an AI tool can only be as good and as fair as:

• the diversity of society represented in the data,

• priorities for the tool’s design,

• developers’ awareness and abilities to identify and address bias risks.

People build tool designs and models with the data, priorities, and risks in mind. Datasets tend to be biased because our society (from which data is drawn) is biased. Without care in sourcing, selecting, and processing data for use in AI, biased AI tools will reinforce and worsen existing harmful biases in our society. (Mistakes made by AI tools — sometimes called ‘hallucinations’ — can be harmful, too.)

Stories abound of AI algorithms that are biased and have caused harm. (Examples: Machine learning vision tools fail to recognize people with darker skin; generative AI creates only pictures of white men in suits when asked to picture a CEO; predictive policing algorithms show high racial disparities.)

Even when companies ensure well-selected data, hire diverse teams, and give high priority to detecting and fixing biases, it’s still not easy to do. That’s no excuse for not trying, though, and some companies are trying.

5. Impact on Lives and Livelihoods

Some people question the harm of AI companies harvesting or scraping data to build generative tools that “we all” can use, sometimes even “for free”. Like other aspects of ethics, there are pros and cons.

AI-based tools can potentially help people work more efficiently or enable them to create new businesses. AI tools can also improve accessibility and global communications. [If you’re using Substack’s “read aloud” feature to listen to this article, that’s AI-based voice cloning in action.] Several AISW interview guests have touted these benefits.

AI and ML can also be used wisely and ethically to improve people’s lives in many ways. For instance, AI tools may detect lung cancer earlier (a cause that’s long been close to my heart).

But most AI companies aren’t altruistic. They’re commercial, profit-driven enterprises. And “we all” will not benefit from AI tools to the same degree; some people’s lives and livelihoods will be severely harmed.

To be blunt, data scraping for AI is stealing: human creators worldwide lose potential income from selling rights to their existing works. Selling or using generative AI tools trained on that stolen content benefits other people, and deprives those creators of income for new works. And markets for creative works by musicians, artists, writers, and other professionals are being overrun by competing AI-generated content (e.g. Spotify and Amazon books).

This impact to creators’ livelihoods is already happening and is projected to get much worse for many industries. Some AISW interview guests have reported this happening to them. AI tools which were unfairly built on stolen professional-quality work are being used to compete against them. Their businesses are withering as a result.

Keep in mind that even “free” tools aren’t really free. Every person who uses those AI tools (and every person on whom the AI tools are used) is giving the tool companies even more data — sometimes very personal data! — that the companies will likely use for AI training and tuning. Through interactions, feedback, and prompt sequences while using the tool, human users create computing workloads and give free data and labor to the tool companies. This is reflected by the arrows in the reference diagram, looping back from human use of the tool to data centers, data sourcing, data processing, and model building:

Many people view providing this labor and data as part of their cost for being able to use the tools (whether they are free or not). However, without knowing how AI companies will use their data, people may not know the actual price they pay. It’s not ‘informed consent’, and may not be a fair exchange of value.

The Bottom Line: What We Can Do

When creators’ prior work is stolen for use in AI, without the 3Cs, they lose the opportunity to earn money from their hard efforts. Other people benefit, perhaps unknowingly, at the expense of the creators whose work was stolen. As HBR presciently noted in 2022, a month after ChatGPT was released:

“The key to adjusting is figuring out how to redesign our economic systems to fully engage the working population. That will require system solutions that don’t just shift the same tasks between people and machines.”

We’re not there yet.

In the meantime, here are three tips on things we can do to become more ‘data literate’, stay informed about responsible AI, and use AI more wisely and ethically.

Tip 1: Choose AI tools carefully.

1. Check for responsible AI certifications and assessments when choosing an AI tool, such as ISO-42001 (Claude) or Fairly Trained (19 companies).

2. Look for an “AI Usage Policy” and “Terms of Use”. (This 2023 podcast by Mark Miller gives plain-English explanations by a lawyer of EULAs from 10 major companies.)

3. Look into the site’s privacy and data protection notices and whether the tool shares data with an underlying AI cloud platform or partner. Prefer AI platforms and tools that publicly affirm their adherence to regional data protection standards (even if you don’t live in a covered region).

4. Prefer tools that can trace the results they give you to specific citations or sources.

5. Be alert for news on how the tool provider addresses biases, where they get their training data, and how they treat data workers.

Tip 2: Protect your data.

Be thoughtful about which tools and companies you choose to allow to use your personal data - or the data of children, friends, patients, clients, colleagues, partners, or businesses. Some tips:

• Before you enter detailed prompts or responses, or upload files to an AI tool, know how that data might be used to train the company’s tool, or by a platform or partner it uses.

• If you post images of your creative work online, consider using one of the ‘data poisoning’ tools to protect your images (e.g. Nightshade, Glaze, or Kudurru).

Tip 3: Use genAI with care, not by default.

Many needs do not require AI or ML, or genAI, to be well met. Less generation required = less impact on the environment, less time and cost to you, lower risks, and less harm to lives and livelihoods of human creators and data workers.

1. Before reaching for that chosen AI-based tool, consider whether the needs could be met with a non-AI search, image repository, tool, etc. or by an original human creator. (For example, many public domain and free-to-use sources of images are readily available; the list of image sources I use is here).

2. If not, use the genAI tool efficiently. Learn to prompt it better so that you get good results with fewer runs. Try searching the table in this directory of 240+ women and nonbinary folks in 37+ countries who write about AI and data here on Substack. You’ll probably find people in your industry and role who are using AI and data well, and writing about how they do it!

3. Be alert for biases in what you get from AI tools, especially generative AI tools. Not all medical advice cited by AI is based on studies that include women, and not all CEOs are white males in suits!

Many AI tool users may be unaware of the risks they are taking with unethical tools, such as loss of privacy, copyright infringement, or unintentionally generating biased or harmful content. One goal of this article is to help more people become aware of the ethical risks and what they can do to use AI safely. If you enjoyed this article, please add a heart, share, restack, or post a Note! This helps others to find the article, and it lets me know you value this work.

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What questions do you have about finding and using AI tools ethically and responsibly?

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Credits

The ethical AI tool ecosystem diagram used in this article is © 2025 Karen Smiley CC BY-NC-ND.

No AI tools were used in the creation of this article.

References

For a much deeper dive on these ethical concerns, with extensive references, please see “Five Concerns in Ethical and Responsible AI (and how most tech companies fall short)”, 6 'P's in AI Pods (AI6P), by Karen Smiley.

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#SheLeadsAI Saturday Social

Every Saturday in February
10:00 am - Noon Pacific
ZOOM

👩‍💻 If you're a woman in AI—or curious about AI—this is for you. A casual, come-as-you-are space to connect, share, and collaborate.

What to expect:

✨ Easy-flowing conversation—just show up and join in
💫 Promote yourself, find collabs, and build connections
📸 Camera ON—because presence matters
🛡 Safe and brave space—no perfectionism here

WHEN: Saturday, February 8 | 10:00 AM - Noon (PT)
WHERE: Virtual (Zoom)

DM Anne Murphy on LinkedIn for the link—anytime, even during the event!

Let’s talk AI!

Apologies to all for the short notice: I completed the submission form for my own nomination tonight, and just got this shareable announcement in my email confirmation. I’m pushing it out right away with the boilerplate text so you can nominate yourself OR share it ASAP with awesome women in AI (or a male ally to women in AI, for one of the 12 award categories) who deserve to be recognized!

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The 2025 awards categories include: (details are on the website)

1. Trailblazer Award - Women in AI of the Year

2. Executive Leader Award - Women Executive of the Year

3. Rising Star in AI Award - Young Role Model of the Year

4. Ally Award - Ally for AI of the Year

5. Women in AI Champion Award - DEI Champion of the Year

6. AI in Startup Award - AI Entrepreneur of the Year

7. AI in Research Award - AI Researcher of the Year

8. AI in Industry Award - AI Innovator of the Year Public Sector

9. AI in Industry Award - AI Innovator of the Year Private Sector

10. AI for Good Award - Responsible AI Leader of the Year

11. AI for Good Award - AI for Social Impact Leader of the Year

12. WAI Volunteer Award - WAI Community Catalyst of the Year

My understanding is that award nominations in other regions are on a different schedule. I’ll share more info after I confirm this.

✨ I’m thrilled to announce that I’ve been nominated for the Women in Artificial Intelligence Awards North America 2025! ✨

This prestigious recognition celebrates the achievements of women driving innovation, leadership, and impact in AI across Canada, the USA, and Mexico. It’s not just an honor but also a vital platform to amplify the voices and contributions of women in AI.

Are you a female leader, innovator, researcher, policymaker, or practitioner in AI? Whether you're advancing AI in startups, industry, academia, or working on AI for good initiatives, I encourage you to apply!

Know an inspiring woman making waves in AI? Don’t miss the chance to nominate her and shine a spotlight on her accomplishments.

👉 Learn more and submit nominations by the extended deadline January 31, 2025, at www.womeninai.co/waiawardsna.

Let’s continue to build a stronger, more inclusive AI community together! 💡🌍

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From now on, monthly updates to the directory will be published as separate posts in the newsletter. Here’s the first one:

To automatically receive updates to the directory and be notified of future community features, please subscribe to SheWritesAI!

Subscribe to SheWritesAI

The first 6 months of additions to the SheWritesAI directory are below.

254+ writers in 39+ known US locations to date: AK, AL, AZ, CA, CO, CT, DC, DE, FL, GA, Guam (not on map), IA, ID, IL, IN, KS, LA, MA, MD, MI, MN, MO, MS, MT, NC, NH, NJ, NM, NV, NY, OH, OR, PA, SC, TN, TX, UT, VA, VT, WA

Substack Profile & Publication Links

This section provides direct Substack-native links to writer profiles and publications. Mouse over the links in your browser to pop up a preview.

These initial writers are roughly in the somewhat-alphabetical order that Substack shows them in my ‘Reads’ list.

Additions on Dec. 19, 2024

• (USA) -

• (Australia) -

• (unspecified) -

• (Hong Kong) -

• (USA) -

• (Israel) -

• (USA) -

• Ngo (USA) -

• Diana Dovgopol (Austria) - The

• (Brazil) -

• (USA) -

• (France) -

• (USA) -

• (UK) -

• (USA) -

• (Denmark) -

• (Australia) -

• Emily and Giulia at - :

  • Emily Uematsu Banzhaf (Sweden)

  • Giulia van den Winkel (Germany)

• (USA) -

• (USA) -

• (England, UK) and others -

• (USA) -

• (India) -

• (India) - (with me)

• (USA) -

• (USA) -

• (UK) -

• (USA) -

• (USA) -

• (USA) - “On AI” section in

• (USA) -

• (USA) -

• (USA) -

• (USA) -

Additions Dec. 2024 - Jan. 2025:

• (she/they) (USA) - , (thank you to Catherine for pointing out that my own name should be here too ;)

• (UAE) -

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• Adrienne as (USA) -

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• Lord (USA) -

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• (USA) - (also writes on Health Bytes)

• (USA) -

• Bess Auer (USA) -

• Bajra (England) -

• (France) -

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• Charlotte Tarrant as (England, UK) -

• (France) -

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• Elissa Malespina as (USA) -

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• (Italy) - ,

• Jessica and Kimberly as (USA)

• Tracy Mendolia, PhD as (USA) -

• (USA) - Algorithms Are Personal ()

• (UK) -

• (USA) - writes on

• (USA & France) -

• (USA) -

• (UK, Switzerland) - writes on

• (USA) -

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• (Poland) -

• (Guam, USA) -

• (USA) - , ,

• (removed)

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• Sarah Freye as (USA) -

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• Eryn Travis - (USA) -

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• Debby Kruzic of (USA) -

• Ameena Ansari (USA) -

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• Zeynep (England, UK) - AI

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Added 2025-02-10: newly added countries are 🇪🇸 Spain and 🇷🇴 Romania

• Minhui Zhou as (China) -

• (USA) -

• (Spain) -

• (Switzerland) -

• (Italy) - (The Scenarionist)

• (USA) -

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Added 2025-02-22: new locations - 🇪🇪 Estonia, 🇪🇺 EU

• (USA)

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Added 2025-03-02: new country locations - none

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• removed (USA)

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Added 2025-03-14: New country locations - 🇮🇲 Isle of Man, 🇯🇵 Japan, 🇵🇪 Peru

• (India) -

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• (Isle of Man) -

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• -

  • Ruby Hoenderdos (Netherlands)

  • Emily Birch (UK)

• (Poland) -

• (USA) -

• (Canada) -

• (Ireland) -

• (USA) -

• (USA) -

• (Brazil) - (in Portuguese)

• (Brazil) - (in English)

• (USA) -

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• Robin Lake at (USA) -

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Added 2025-03-26: New country location - 🇬🇪 Georgia

• (USA)

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• (Canada) - ,

• (USA) -

• (USA) -

• (France) - (in French)

• (UK) -

• (Turkey) - (in Turkish)

• (USA) -

• (Georgia) -

Added 2025-04-05: New country locations - 🇨🇩 Democratic Republic of the Congo (DRC) and 🇸🇬 Singapore

• (UK - England) -

• (UK) -

• (Democratic Republic of the Congo) -

• (Canada) -

• (France) -

• (India) -

• (France) -

• Mythri M. at (Canada) -

• Somya Sinha at (India) -

• Henna Vij at (India) -

• (Singapore) -

• (Netherlands) -

• (Spain) -

• (USA) -

• (USA) -

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Added 2025-04-16: No new country locations (2 new US states: Oregon and Alaska)

• (USA) -

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Added 2025-04-26: No new country locations (1 new US state: Indiana)

• (USA) -

• (UK) -

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• (Australia) -

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• Shukla (USA) -

• (UK) -

Added 2025-05-10: 2 new country locations (🇨🇿 Czech Republic and 🇹🇳 Tunisia) ; 1 new US state (South Carolina)

• (UK) -

• (UK) -

• (Tunisia) -

• (USA) -

• (USA) -

• (USA) -

• (USA/France) - (in French)

• (USA) -

• (Sweden) -

• (Germany) - (in German)

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Added 2025-05-20: 2 new country locations (🇭🇷 Croatia and 🇳🇬 Nigeria) ; 1 new US state (Louisiana)

• (USA) -

• (Nigeria) -

• (USA) -

• (USA) -

• (Finland) -

• (USA) -

• (USA) -

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• (Romania) -

• (USA) -

• (USA) -

• (USA) - Infinite Me Reality

• (France) - (The Plot Media)

• (Croatia) -

• (USA) -

• (USA) -

• (Canada) -

• (USA) -

• (UK) -

• (USA) - supercyberlicious

• (USA) - Learn Analytics Engineering

• (USA) -

• (removed) (USA)

• (Italy) -

• (USA) -

Added 2025-06-15:

More additions are expected soon - please subscribe to for updates!

To anyone listed: I’ll be glad to include or correct any annotations, demographics, locations, flags, pronouns, Bluesky or podcast column links, etc. you’d like to share here. Just comment or DM me.

Or if for some reason you don’t want your name or publication or a link in this directory, let me know and I’ll remove you.

Help: Who else should be in this list?

Backstory: origin of this directory

A recent [Dec. 19, 2024] article on Substackers who write about AI profiled 10 men — and 0 non-men. I messaged the author privately to express my disappointment that the list was all men. He responded kindly and quickly that the oversight wasn’t intentional; he just wasn’t connected yet to women writing here on Substack about AI.

I’ve seen starter lists of women in STEM on sites like Bluesky. But I realized I don’t know of any lists for women in AI on Substack yet. So I’m creating one here to make it easier for people of all genders to find women and non-binary people who write on Substack about AI, ML, ethics, governance, data privacy, and related technology. Writer locations are generally based on LinkedIn profiles, where known, or confirmed via DM with the writers.

For more background on this directory, see my April 12, 2025 article or my guest post in ’s blog, “Women in Artificial Intelligence”.

History: how it’s evolved

I seeded this directory in December 2024 with a list of 34 women I already followed and their publications. I update it as suggestions come in or I discover more or new writers. Please suggest others, or nominate yourself if appropriate!

Leave a comment

Update Dec. 21, 2024: I used Datawrapper.de to create an interactive map to show the countries, where known, for the writers listed here. I also created a searchable, filterable table to make it easier to find a person or country in the list. (The bulleted lists below the map and table also give live links to Substack.)

Update Feb. 4, 2025: The table now includes live hyperlinks for writer names, newsletters, and LinkedIn profiles. New linked columns for books, podcasts, and Bluesky handles are gradually being populated. A column for Topics is also being filled in.

Update Feb. 20, 2025: New world map created with more granular locations. Table and this page updated to use people’s preferred country/flag identities.

Update March 2, 2025: Created US state map (state is not known for about 10% of US writers). Newsletter topics are now included for all writers.

Thanks for reading Agile Analytics and Beyond! I’d love to hear if you find this directory useful, or have ideas on ways to make it more useful for you. Shares, hearts, comments, & restacks are all awesome 😊. (One-time tips or voluntary donations via paid subscription are always welcome & appreciated, too!)

Drop a tip in the jar!

Although the piece makes some good points, it reminds me of the observation that we shouldn’t let our dismay over HOW someone is protesting take priority over WHAT they are protesting. This neotoddlerism piece is all about the HOWs, and mostly ignores the more important WHATs - as if they’re all equally unimportant. The WHATs deserve more of our attention.

Example (original source unknown 1):

“You keep saying, “It’s horrible that an innocent black man was killed, but destroying property has to stop”.

Try saying, “lt’s horrible that property is being destroyed, but killing innocent black men has to stop.”

You’re prioritizing the wrong part.”

When an adult isn’t listening to a child, and the child gets frustrated and acts out, instead of whining about HOW the child is misbehaving (or punishing the child), the better solution is for the adult to listen to WHAT the child is upset about and address it.

When it comes to reporting on protests, that’s on the media - which means it’s on all of us. They are publicizing what we reward with our clicks and our comments.

As so aptly commented:

“Here is the first step to what ails us - stop feeding the black wolf that lives in us all. We are attracted to these shock stories because it appeals to our own inner toddler. And every click feeds the beast. The only way to move forward is to learn to grow up ourselves and to join the strong, silent majority who are actually engaged in making the world function at all.”

We the people are “the system” that rewards this type of pandering by the media. We need to own growing up (ourselves) and fixing the system. We can start by paying more attention to real issues that protestors are raising than we do to the forms of their protests.

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My friend Stella Fosse recently reposted her 2020 article “Dare to Dream Madam Vice President”, and this quote particularly resonated with me.

“Our country has been divided, not just these past four years, but since its inception. It has been divided by the belief in a zero-sum game: that whatever gains are made by women, or people of color, or people with disabilities, or people who love the same gender, that those gains are made at the expense of someone else. That one person’s ability to marry somehow threatens another person’s marriage. That one person’s access to education somehow limits another person’s ability to learn. None of this is real.”

DEI advocates often counter this view of life as a zero-sum game by saying:

“Equal rights for others does not mean less rights for you. It’s not pie.”

There’s also pushback on that idea, asserting that it IS pie; that others gaining more rights to resources will take away from others when resources are constrained. This fear of losing, of life being a “zero-sum game”, shows up over and over in equity discussions and the DEI space. Why?

Inequality and Inequity

For a more in-depth discussion of the differences between equality and equity, see this July 23 article on “Baseball, Bicycles, and Crosswalks”. (The differences aren’t important for this article, so don’t let terminology derail you.)

Many cartoons on gender inequality show a man and a woman at the starting line of a racetrack.

Cartoon Variation 1

In one viral variation of the cartoon (this post on ICLD.se 1 is one example):

• The man’s track has two small hurdles. They are low, spaced apart, and do not span his whole lane. He could easily jump them (even in his suit), or just go around.

• The woman’s track shows land mines, barbed wire, an alligator, part of a tall stone wall, and spikes. And she has a big weighted ball chained to her right ankle.

• The cartoon caption shows the man saying to the woman, “What’s the matter? It’s the same distance.”

The obvious point is that the woman faces far more barriers than the man. Their tracks are not equitable or fair. And pretending they are is ridiculous.

Cartoon Variation 2

A variation by Peruvian political cartoonist Carlin titled “Meritocracia” shows 3 women and 3 men. The men’s tracks have no obstacles and the women’s tracks have many, mostly related to household work. It went viral in Feb. 2019 when shared by billionaire Anand Mahindra. (←- Click that link to see the cartoon.)

This Upworthy post 2 describes the cartoon: “It depicts three men and three women positioned at the starting line of a track that presents a clear path to the finish line for only one gender. While the women are blocked by a never-ending pile of housework, including laundry, cooking and ironing, the men are presented with a path that's completely unobstructed. And just to make things harder for the women, they are dressed skirts and heels in contrast to the suits and comfortable shoes worn by men.”

The Upworthy article also includes a version of the cartoon marked up by a man to add obstacles (in text boxes) to the men’s paths. This reflects backlash and a sense of resentment that life isn’t easy for men, either.

But no one’s claiming it IS easy for men, in absolute terms. The point is that men’s gender isn’t making things even harder for them. Not being of male gender does make it harder.

It’s similar to talking about ‘white privilege’. Yes, many white people like me face challenges too, but my skin color hasn’t made my life even harder.

Cartoon Variation 3

A cartoon I saw the other day (but didn’t save, unfortunately) showed three panels on this topic.

1. The first panel shows a man and a woman at the starting line of a race track, where the woman’s lane shows obstacles and the man’s doesn’t. It’s captioned “What Women Face”.

2. The second panel shows the man at the lane with the obstacles and the woman at the lane without obstacles. It’s captioned “What Men Fear”.

3. The third panel shows both the man and the woman at lanes with no obstacles. It’s captioned “What DEI Is”.

The key insight is that the second panel ISN’T at all what women or advocates for equity want. The goal isn’t to create more obstacles for anyone. Equity advocates want to remove systemic obstacles for the benefit of everyone.

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But Aren’t We Past Sexism and Racism By Now?

No. We’re absolutely not.

Stella’s article, which I quoted at the start of this post, is about US VP Kamala Harris. As an American woman with ancestors from Jamaica and India, she has deep personal experience with intersectional biases and inequities. VP Harris has clearly succeeded in spite of the many barriers she has faced in her life.

Yet, in some ways, Harris is the “exception that proves the rule”. Gender bias is far from gone in this country, and in some ways, it’s gotten worse lately.

And setting aside her gender for the moment, racist bias is deep-rooted and still pervasive in the US. If anyone somehow thinks that people of color in this country are blessed with open lanes with no obstacles, they are not living in reality.

Some examples:

• The list of unarmed Black people killed by US law enforcement officers is appallingly long - and incomplete. The latest known example as of July 25, 2024 is Sonya Massey’s murder on July 6. Black people in the US are not safe even in their homes, even when they have called for help. Yes, the policeman who shot Massey has been dismissed and charged with murder, and that’s good; but that’s not good enough. She should be alive.

• Discrimination against Asian Americans in the US is long-standing and even escalated during the COVID-19 pandemic, inflamed by racist rhetoric. A 2023 study found that “32% of Asian adults say they know another Asian person in the U.S. who has been threatened or attacked because of their race or ethnicity since the beginning of the COVID-19 pandemic.” And 9 out of 10 Asian Americans personally experienced at least one of the 17 discrimination incidents in the survey. (It wasn’t an exhaustive list.)

• Hispanic people are hardly exempt from bias and inequity in the US. Discrimination against them in the US dates back to the 1840s, and hate crimes against Hispanics have spiked in recent years. On the job front, as noted in this LinkedIn article about 4 barriers Hispanic workers face, Jenn Tardy quotes the Center for American Progress as asserting that “systemic factors continue to prevent Hispanics from achieving equal employment outcomes to Whites.”

• This Health Services Research report confirms that indigenous peoples in the US experience greater bias than whites in health care and other domains for over 500 years. And it’s still a problem. “In adjusted models, Native Americans had higher odds than whites of reporting discrimination across several domains, including health care and interactions with the police/courts.” (See the report for more details.)

All of this (waves hands) has to stop.

Trading Places and the Golden Rule

Educator Jane Elliott is famous for her blue-eyes / brown-eyes experiment with students 3. She is also well-known for her seminar question to a mixed-race audience. “She asks “How many of you white folks would trade places with a black person today?” And she waits. The only sound is that of the people of color in the room turning in their chairs to see if anyone will raise their hand. She continues to wait and not a single white person raises their hand. She says, “You know what you just admitted? That this is happening, and you don’t want it for you. So why are you so willing for it to be the case for someone else?”” 4

I have a feeling that the raised hands would be similarly missing if cis men were asked if they would trade places and be a woman.

People who know that others are already being treated badly, and don’t want to be treated badly themselves, fear the possibility of ‘trading places’. Perhaps they don’t want any more competition for the pie that they believe to be limited. The real question, as Jane Elliott asks, is: If we don't want it for ourselves, why are we so willing for it to be the case for someone else?

• If we wouldn’t want to be impeded or underpaid in our careers, why are we ok with others being impeded or underpaid?

• If we wouldn’t want ourselves or our family members to be at greater risk of dying or being killed because of our skin color or gender status, why are we ok with others bearing those risks?

• If we wouldn’t want to be prevented from marrying who we chose (or not marrying), or divorcing, or bearing children or not, why are we ok with others being blocked from having those choices?

• etc.

What exactly do we fear we are losing by others having the same rights to earn a living and make these choices for themselves?

On what basis could we believe that those potential losses to us OUTWEIGH their rights to the same autonomy we enjoy?

Every major religious tradition - and some non-religious ethical traditions - support some form of the “Golden Rule”: “Do unto others as you would have them do unto you”. This includes Baha’i Faith, Buddhism, Christianity, Confucianism, Hinduism, Islam, Jainism, Judaism, Taoism, and Wicca.

If we consider ourselves adherents of any of those 10 traditions, and we wouldn’t want some thing to happen to us, how can any of us rationalize being willing for it to happen to someone else?

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What We Can Do

The bad news: All of these problems with inequity are largely systemic.

The good news: WE are the system, and WE have the power to fix it.

When I talk about the “system” that needs barriers and biases removed, I’m not referring only to biased computer systems, hardware, firmware, software, or AI algorithms. I’m talking about us and our “wetware” (brains).

We, the people of the world, collectively ARE “the system”. Our bodies and brains drive the social systems in our communities, and create the physical and virtual systems we all use.

• We, the people, design and build computer systems and software.

• We, the people, select data and train & design AI algorithms.

• We, the people, design crosswalks, fences, bicycles and other transportation, educational courses, and so much more.

• We, the people, define laws, policies, and processes, and we make decisions.

• We, the people, write, speak, and take actions (or don’t).

We ARE the system. And WE can & must own fixing it, together.

How To Move Forward

Here are two things each of us can do.

Action 1. No matter where we live or work, we all have work to do. We all grow up with biases we don’t even know we have. We’re like fish swimming in the ocean, and we don’t see the water. Learning to see is the first step.

The sooner more of us can come to terms with the answers to these questions about fears, and reconcile our belief traditions with moving towards equity, the faster our progress will be towards a fairer world without obstacles for all.

Yes, these biases are systemic. They’ve persisted for centuries and aren’t rooted out yet. But we ARE the system, and there is no excuse for not working on these biases and making progress.

“I am only one, but I am one. I can't do everything, but I can do something. The something I ought to do, I can do. And by the grace of God, I will.” (Edward Everett Hale, 1902)

Action 2. We need to focus on electing (and holding accountable) officials who have the power to prioritize equity for all in the systems they influence. I wrote about this on March 30 with regard to ageism (including gendered ageism and misogynoir). It’s even more critical now. For those of us in the USA, it’s one of the reasons this upcoming election feels so monumental. This 2024 election is our opportunity to elect leaders at all levels who believe in equity & will prioritize it.

Consider VP Harris as an example. While her record is not perfect, she has already spoken up and walked the talk to remove barriers, and not just for other women or people of color. From her 2020 acceptance speech as VP-elect (quoted in Stella’s article):

“While I may be the first woman in this office, I won’t be the last. … And to the children of our country, regardless of your gender, our country has sent you a clear message: Dream with ambition, lead with conviction, and see yourself in a way that others might not see you, simply because they’ve never seen it before. And we will applaud you every step of the way. Now is when the real work begins. The hard work. The necessary work. The good work. … To rebuild our economy so it works for working people. To root out systemic racism in our justice system and society. To combat the climate crisis. To unite our country and heal the soul of our nation. The road ahead will not be easy. But America is ready.”

VP Harris was and is right that we need the real work, the hard work, the necessary work, the good work. (Applause is great, but it’s not enough.) We need to reverse the damage that’s already been done to civil, voting, marriage, and healthcare rights in recent years. We need to solidify our rights so they can’t be jeopardized again by fearful people who believe that life is a zero-sum game. It’s time to focus on tearing down systemic barriers, rebuilding & improving, to have true liberty and justice for ALL.

To achieve this, we need leaders up and down and across our country at all levels who care about removing systemic barriers and who will pitch in to ‘make it so’. Harris’s platform isn’t set yet, but her positions appear likely to be stronger on equity than Biden’s. And her speeches and actions to date support hope that she will prioritize equity as President.

NOW is the time, before the convention (Aug. 19-22), to speak up about making equity a priority in her platform! (And keep speaking up and acting.)

Like planting a tree and so many other things, the best time to push together for equity was 20 years ago, and the next best time is now. I believe America IS more ready today than ever before. LFG.

Credits

Thank you to Catherine Louis, Rose G, and Stella Fosse for their kind feedback on drafts of this article!

References

Other Agile Analytics and Beyond posts on equality and equity

Working Definitions (from RWJF 1):

• Equality: “Everyone gets the same - regardless if it’s needed or right for them”

• Equity: “Everyone gets what they need - understanding the barriers, circumstances, and conditions.”

Side-by-side graphics are commonly used to illustrate the difference between equality and equity. Here’s a quick look at how these graphics have evolved and improved over time, and why I love the latest one!

Version 1: Baseball games and boxes

One analogy that has circulated for years shows 3 people of different heights trying to see a baseball game over a fence.

Description: One person is taller than the fence and doesn’t have an impaired view. The second person is at eye level with the fence and can’t see the whole field. The third person can’t see the game at all; the fence completely blocks their view.

The ‘equality’ view shows giving each person one box to stand on. This helps the second person, but is unnecessary for the first person, and still leaves the third person unable to see over the fence.

In the ‘equity’ view, each person has been given 0, 1, or 2 boxes to stand on, and now all 3 can see the game over the fence.

There are many variations of this image. In one alternate version, a third ‘justice’ view shows that the fence has been removed, or no longer blocks viewing. So no boxes are needed, and everyone can see.

Here’s an example of the 3-pane baseball graphic showing equality, equity, and justice.

Version 2: Bicycles

In 2017 2, the Robert Wood Johnson Foundation (RWJF) came up with a better illustration based on bicycles.

Description: Their graphic shows 4 people who want to ride a bike.

For the equality side, everyone is given the same standard bike. But the wheelchair user can’t use that bike at all. The tall person is uncomfortably hunched over and their knees bump the handlebars, because the bike’s too small for them. Someone would have had to boost the child onto the bike because the crossbar is too high, and the child can’t sit on the seat and still have their feet reach the pedals. The standard bike only works for 1 of the 4 people.

In the equity view, each person has a bike that fits them. The wheelchair user has a recumbent bike with hand controls, not requiring foot power. The tall person has a big bike that they don’t have to hunch over. The child has a small bike that is the right size for them. Everyone can ride.

To their credit, RWJF sought feedback on how to improve the new bicycle analogy. They came out with not 1, but 2, new and improved graphics in Nov. 2022 3.

Version 3a: Bicycles and Terrain

In the first new graphic, RWJF improved the bicycle analogy to show differences in terrain for 3 people.

Description: The equality view shows a child on bumpy terrain with a standard bike that’s taller than they are; it’s impossible for them to even get on it. A person with a standard bike and flat pavement is able to ride. A wheelchair user needing to go up a ramp can’t use the standard bike at all.

The equity view shows that the child who is riding over bumpy terrain gets a small mountain-style bike with bigger tires. The person riding on flat pavement gets a standard road bike their size; it works for them. And the right equipment for the wheelchair user isn’t a bike at all, but a power chair that can safely go up the ramp. Everyone can get to where they need to go.

Version 3b: Crosswalks and curb cuts

RWJF also came up with a completely different analogy. Instead of a baseball game or bicycles, it shows people at a crosswalk.

In the original view, it shows that the street curb is high, and there is a sign at the intersection to let sighted people know where to cross. One person easily manages to step off the sidewalk curb to the street level. The curb is clearly too high for a child’s shorter legs; the child is shown jumping down to the street. The tall curb prevents a wheelchair user and a blind person with a cane from safely accessing the street to cross. And none of the people can easily tell when it’s safe to try to cross.

In the equity view, the street curb and crosswalk are reshaped.

• A ‘curb cut’ and a ramp with ridges have been added. These help the wheelchair user and the blind person safely reach street level. The child can now get to street level without jumping, and the ramp works well for the taller person too.

• In addition to the static crosswalk sign, the intersection now has:

  • lighted indicators to show when it’s safe to walk and when not to start, and

  • a speaker which sounds when it’s safe to cross, and stops when it isn’t.

I love this last new analogy and graphic. Not only does this new intersection design help the 4 people pictured, removing the systemic barriers will help others not shown. Examples: people pushing strollers, using a rolling walker, or walking dogs. The changes to the crosswalk system make the street accessible to everyone without special equipment. (One downside might be that the crosswalk sound could be disturbing for people with auditory sensitivities, though.)

I also love that RWJF solicited input from a diverse group of people, gathered data, and used it to guide their improvement iterations. That is true little-a agile behavior at its finest!

What do you think? Could the graphics be further improved?

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What’s Next?

With equality, equity, and DEI initiatives, it’s important to keep in mind that the real goal is to be fair to everyone, and that the focus is fixing the system, not ‘fixing’ the people. The people aren’t broken or deficient. The system that creates the barriers for people to varying degrees is what “needs fixed”.

I’ve had requests for future articles that share personal stories of how inequity has caused real harms to real people, and how changes to the systems were the best solutions. I’m giving some thought to which of my own inequity stories I might be willing to share.

I know many who have experienced inequitable situations, at work or elsewhere. (In fact, I probably know more people who have than people who haven’t.) If YOU have, AND you would be comfortable sharing in a guest post in this newsletter section, or if you already have an article on this topic that could be cross-posted, please message me!

Credits

• This article was inspired by a LinkedIn post by Paul Ladipo about the newest graphics from RWJF (Robert Wood Johnson Foundation). I learn so much from following Paul!

• Infographic on equality vs. equity is by Raven Solomon Enterprises, used with permission. Her insights and perspectives add value to my life, and I highly recommend following Raven.

• All RWJF images are shared in accordance with their terms of use 4 and linked to their original images if a larger view is needed.

• Thank you to Catherine Louis and Rose G for their kind feedback on drafts of this article!

End Notes

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For the list of AI and non-AI tools and image sources I do use, see ‘Ethical Shoestrings’:

The Trigger

Late Wednesday night, I saw a post on LinkedIn by Axel Coustere about Casey Newton’s experience with pushback from potential subscribers about his use of generative AI for images (video):

Casey’s success with Platformer is impressive, and I certainly appreciate that people can have genuine intentions with using genAI tools to generate artwork for a newsletter (his has moved off Substack, unfortunately). I’m definitely not an artist myself, either, and I’ve found that it takes a lot of my ‘writing’ time to find suitable affordable/free images for my newsletter posts (since I’m operating on a shoestring at present, while getting started here on Substack).

👎 However, I align with the people who shared feedback with him that they refused to subscribe to a publication using AI-generated imagery, based on objections that the tools that don’t respect artists’ copyright and ownership.

If I can avoid it (and it’s likely a bigger IF than even I realize), I will not be a willing participant, even if indirectly, in an exploitative genAI tool ecosystem.

And at this point, the vast majority of genAI tools aren’t using ethically sourced images or content for training their models - i.e., they are exploitative.  

Being a Non-Participant

By ‘participant’ in a genAI tool ecosystem, I mean:

• being a tool user, 

• working for/with a tool provider developing the tool, or 

• engaging or even paying for content from someone who used the tool. 

All of these are choices I can control & can make, provided there’s transparency - about whether a genAI tool is used to create content, whether the tool used is “fairly trained”, etc. - in short, transparency about whether the content was generated ethically or exploitatively.

I’d also rather not be a participant as a contributor whose work or characteristics are exploited against their will. Even though I don’t think of myself an artist, I certainly don’t want my photographs, writing, speaking or singing voice, appearance, etc. to be stolen and used without my consent. (Others may feel differently about their work and characteristics; that’s their prerogative.)

Unfortunately, in our present ecosystem, whether or not my work or personal characteristics get scraped or stolen (and then exploited) is not a choice I as a mere individual get to make. That’s sad. 😞 So I am focusing on choices I can control.

My Reasons Why

Three principles underpin my refusal to engage with exploitative genAI tools:

1. Golden Rule: Since I wouldn’t want MY creations and personal characteristics to be exploited this way, how could I possibly (ethically) justify being complicit in the exploitation of others? (i.e., by using these tools, helping to develop them, or supporting creators who use them)

2. “Pro-choice” view: Even if I were ok with MY work being ripped off without consent or compensation (and I’m not), I still would have no right to make that choice for others. Only THEY should decide about use of THEIR work. (This pro-choice view was advocated in our Part 2 article on ethics of generative AI for music.) As a believer in pro-choice, ethical behavior requires me not to directly or indirectly encourage toolmakers to deprive people of that choice (i.e. I should not use, support, or help build tools that deprive them). 

3. Legal vs Ethical: Laws vary worldwide and over time, and some people might say that this type of theft and exploitation isn’t illegal in their region. However:

• In our global economy, it’s highly likely that content scraped and used to train genAI models came from regions other than our own. If some of the base creative works used in training the model and building the tool were stolen from people in other regions with different laws, the fact that it’s legal to steal in your region is irrelevant.

• I don’t actually care if it’s legal. Even if it’s legal to scrape and exploit, my moral compass tells me it’s still unethical. Unethical behavior shouldn’t have to also be illegal for me to not do it. 

Is it inconvenient to not use cool new tools to create images to enhance my work and save me time? Oh, absolutely. It costs me hours, and even then I don’t always find great free images that fit my needs well. But “Principles only mean something when you stick to them when it’s inconvenient.” (Laine Hanson, “The Contender”). And these are my principles. 

Current State

To me, living with integrity requires me to stick to these principles despite the cost. I’m resigned to the inconvenience of continuing to either use my own works or spend the time to find verifiably free/public domain works. In the meantime, I’ll carry on with my quest to find fairly trained genAI tools that I can feel good about using. (Findings will be published on along with our other deep dives on AI.)

And although I might miss detecting some, I do intend to try to avoid or minimize supporting people, publications, companies, etc. who develop unfairly trained tools or who generate content with unfairly trained tools. This is where transparency (covered in more depth here in Part 2) would come in super handy. 

As I advocated last week, I believe every content creator should have an AI usage policy (that page is my AI usage policy, plus explanations on why). An AI usage policy can be simple: just say what you do/don’t do with AI, and do/don’t do what you say you will/won’t! Then link or embed the policy on an About page, or Ethics page (for those who have one), or in one’s bio. 

If you’re hesitant to post an AI usage policy publicly, please consider:

• If you’re not ashamed of using (possibly unfairly trained) AI tools, why would you not be transparent about it? 

• If you are ashamed, shouldn’t that shame be a trigger for reconsidering using the tools, or choosing different (ethically developed) tools?

Please comment or DM me if chatting would be of help!

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Looking Ahead

So what would have to happen for me to switch to being an active & willing genAI tool participant in future? 

Ultimately, I’m looking forward to the day when all:

1. Artists and content creators can freely choose how, when, and where their works are used to train genAI tools, and are compensated appropriately

2. GenAI tool providers are transparent about the source of their training data and whether their tools are Fairly Trained; provide traceability; support consent and compensation for content creators; and enable tool users to give informed consent when they choose to use the tool 

3. Sites and content producers post clear AI usage policies and transparently label their works that have AI-generated content

I realize I might not live to see that day, or it may never come - but a girl can dream. And my experience is that changing it all starts with awareness. 

“I did then what I knew how to do. Now that I know better, I do better.”
(Maya Angelou)

I’ve learned a lot about this whole aspect of ethics in the past 12-18 months. I’m more aware and can now ask better questions about ethical sourcing of the underlying training data. And I’ve gotten to know many people who are doing their best, and trying to know better, and helping others to know and do better. That’s how we’ll get there: by learning and caring and helping each other.

“Unless someone like you cares a whole awful lot, nothing is going to get better. It's not.” (Dr. Seuss, The Lorax)

It’s great that Casey took the feedback from potential Platformer subscribers seriously. I commend them all collectively for caring and speaking up about use of unfair AI tools! This is how things change.

I do hope to live long enough to at least see meaningful progress on all 3 of these goals, and a healthy ecosystem that includes at least a few ethical genAI tools for each artifact type (including multi-modal tools). I’ll eagerly jump to try out the fairly trained genAI tools I do find (hoping this will happen sooner than later)!

🔎 And now, I’m off to find a suitable public domain or royalty-free image to use for this post …

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Update 2024-06-20: Check out ’s cool new “created with HUMAN INTELLIGENCE” images! Here’s an example:

Update 2025-12-22: Thank you to for finding ethically-trained AI image generation tool Blunge.ai. It’s up next on my evaluation list, right after chat.publicai.co (the Swiss-based ethically-trained chat tool). If you’re reading this, you probably care about ethical AI - these two are worth a look!

Related pages and posts:

“AI Without AI” (my AI Usage Policy on not using LLMs for my writing)

Thanks for reading Everyday Ethical AI! I’d love to hear if you found this article useful. (Shares, hearts, comments, & restacks are awesome 😊, and one-time tips or voluntary donations via paid subscription are welcome & appreciated!)

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Geese, Software Development, and DEI

Scouting, Leading, or in the Flock?

Adding the pond photo above as the background on my new laptop in recent weeks got me thinking about when (under what circumstances) it's better to be:

• the scout goose way out in front,

• the lead goose at the front of the flock, or

• a goose in the flock!

I’ve held all 3 goose roles at different times in my career. Once, over 20 years ago at a telecom startup, we managers were discussing rotating the ‘Release Manager’ responsibility on our flagship product. Our HR VP compared this to flying geese taking turns being at the front of the V (she also kept a poster illustrating this in her office). Flying is more tiring for the lead goose, who literally ‘lifts’ the flock. The flock can go farther if the point duty is rotated, and their honks are encouragement to the current leader!1 (Ducks swim in V’s or lines for similar reasons, with the flock ‘drafting’ off the lead.2)

From the first reference above, I learned that still photos of goose or duck flocks sometimes look like they aren’t in well-formed V’s or lines, but that’s misleading. The article explained that the reason is due to the flocks frequently re-shaping their formation “on the fly” (literally!) and changing positions, even within the V.

A photo I took at the National Mall in DC, below, shows this: the flock had just taken off from the Reflecting Pool, and was not yet in formation.

Here are my thoughts on each of the goose roles, prompted by the pond photo.

Role: Scout Goose

Being the scout goose, alone way out front, is like doing research or technology scouting, alpha or beta testing, or building an early spike for a proof of concept. I did this kind of work in Corporate Research for a while (for an example of technology scouting, see e.g. my imported posts on Leap Motion sensors3). Although it can be frustrating at times, it’s a lot of fun to experiment with new technologies when they’re not yet mission-critical, and even more fun to come up with creative ways of applying them!

When the tech doesn’t work out or simply isn’t ready for prime time, an agile tech scout can pivot and know they’ve saved the larger flock of engineers from wasting their energy paddling in the wrong direction. And when the tech is worthwhile, but has pitfalls to be aware of, the scout can help guide the flock of engineers safely through ‘choppy water’.

One downside of being the scout goose is that people who watch the flock’s progress might miss seeing your trail in the water, and may not recognize your contributions to the flock’s success.

Role: Lead Goose

I’ve been the lead goose for the flock (long term, or ‘taking turns’ at the front when needed) in advocating for constructive change or in a product development environment. Making innovative software products that solve real problems, or achieving positive lasting change in the world, is generally best as a team effort.

In social justice activism, I also like the “parable of the choir” analogy to sharing the load: “A friend once shared what she called the Parable of the Choir: A choir can sing a beautiful note impossibly long because singers can individually drop out to breathe as necessary and the note goes on. Social justice activism should be like that, she said. That's stuck with me.” 4

This only works, of course, if the singers take turns dropping out & back in.

“If you want to go fast, go alone; but if you want to go far, go together.”5

Role: Goose in the Flock

When I need to use tech to get normal things done, or to build a standard software system element, I tend to be strictly a ‘goose in the flock’. I want the ‘lift’.

For example, as a laptop user, I don’t want to be on the bleeding edge of OS updates with bloated features I don’t need. I just want the OS to be reliable & fast & let me efficiently do what I need to get done. Please.

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Flock Behavior and Dynamic Reteaming

A final thought on geese formations and software development or change initiatives: still ‘photos’ can be misleading. A ‘snapshot’ view of a team may appear disorganized or chaotic. This might simply mean the flock is reorganizing on the fly.

In software, this can be due to “dynamic reteaming” (credit to Heidi Helfand for the phrase6). It doesn’t mean the team is dysfunctional. Perhaps they’re simply being agile. 😊

Executives and managers need to learn to recognize that occasional sampling or views may lead them to inaccurate conclusions about their teams. Self-organized change by teams can be healthy, achieve a sustainable pace, reduce burnout, and enable them to “go far” together - all good things IMHO.

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Credits and References

Thank you to and for their kind feedback on drafts of this post, and to Arman reminding me of the flying geese analogy’s relevance 😊

Camera info links:

• Nikon Coolpix L10 camera

• Panasonic Lumix DMC-FZ30

End Notes: