AI Bias Issues

Women are losing their voices on this platform and the data is right in front of us... There has been a clear shift on LinkedIn in the last twelve months and I have watched my reach fall from millions of views per post to a few thousand. I thought it was just me. Then I started talking to other women and realised it is happening everywhere. Women who lead teams. Women who run companies. Women who speak with honesty and softness and emotional depth. It is the same pattern - their content is being pushed down. Posts written with emotional depth, vulnerability, or soft honesty are receiving far less reach than before. Yet when men speak about the same topics, in the same emotional tone, the algorithm seems to reward it. This is not because women are writing less powerful content. It is because the algorithm is rewarding what it interprets as authority language, often coded as male. Posts that use agentic words, direct statements and a more assertive tone are being pushed out more widely. Empathy based content is being limited. The data is visible on thousands of accounts. The irony is that men like Jake Humphrey Steven Bartlett and Daniel Priestley talk about these things often, but their delivery is still framed as leadership, advice and direction. When women speak from the same emotional space, the algorithm reads it as personal reflection and deprioritises it. When women communicate with nuance, reflection or emotional truth, the reach drops. This is bias in design. Even in 2025. I refuse to believe that women’s perspectives are less valuable. I refuse to believe that softness is less important than strength. I refuse to believe that emotion belongs only to men with podcasts. So I am running some experiments over the next few weeks. Different styles of writing. Different types of images. Even a different version of myself generated through AI to see how the platform responds. Because if a male version of me receives more reach than the real me, then we have a bigger problem than an algorithm update. If you are a woman who has noticed the same thing, I would love you to share this post. The more voices we bring together, the harder this becomes to ignore. Visibility should not depend on gendered language patterns - it should depend on the value behind the message. Our voices matter. They always have. And they will continue to, even if the system needs reminding. Yes, your number of followers have a significant impact, but when my impressions drop from 7 million to 900 consistently, something is clearly off. Megan Cornish, LICSW Katie Langdon Women in Pharma (WiP), 💥 Amy Kean 💥 Chantal Cox Katrina McGuire CertRP Deirdre O'Neill Cindy Gallop Jane Evans Be keen to know your thoughts in the comments… 👇

I came across research last week that I genuinely cannot stop thinking about. In the logic of AI, "man" is to "programmer" as "woman" is to "homemaker." No one explicitly coded that bias into the system; the machines simply learned it from us. They mirrored our job postings, our articles, and our casual conversations and billions of our own blind spots fed into a black box until the algorithm started reflecting our worst habits back at us. Bias in AI isn't always malicious. But sometimes it feels like AI is being weaponized against women's safety at a scale. On platforms like X, a woman posts a photo and the replies are filled with prompts for AI tools to undress her (see the links in comments).These tools then publicly generate explicit, non-consensual images of real women who are students, mothers, leaders. We want to use AI. We must use AI but thoughtfully. And the information it is sharing is just a mere unfortunate reflection of our society. A society where women have fought their way up as they have been historically been reduced, objectified, and pushed to the margins but now those patterns are being encoded into new systems. When a tool can be used to violate a woman's dignity in seconds, that's a design and policy failure. My question is: Can we build AI that doesn't inherit the worst of us? I think we can. But only if the people building it are asking that question out loud before the product ships. #AI #GenderBias #WomenSafety

💃🏽 “𝗪𝗲 𝗼𝘄𝗲 𝘄𝗼𝗺𝗲𝗻 𝗮 𝗰𝗲𝗻𝘁𝘂𝗿𝘆 𝗼𝗳 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵.” – 𝗟𝗶𝘀𝗮 𝗠𝗼𝘀𝗰𝗼𝗻𝗶 Until 1993, women were largely excluded from clinical trials. Not by accident, but by design 👀 Women were left out of research because our biology was seen as disruptive. Hormones made the data harder to control, so the answer was to exclude us 🤷🏽♀️ The default became male & the consequences followed. What worked in the lab didn’t always work in the real world & it still doesn’t ❌ That choice didn’t stay in the past 🔙 You can still see it in drugs that fail to accurately recognise women’s symptoms, in the medtech equipment that doesn’t quite fit in a female surgeon's hand, in the research that skips over the questions that matter to half the population 🌎 As we move into an AI-first future, we’re building on data that never really saw women to begin with. The risk isn’t just bias, it’s getting things wrong at scale 📈 If women aren’t included in the data, the systems we rely on won’t just miss us, they’ll misrepresent us. We need women shaping the research, the trials, the tech – not just for fairness, but so it actually works 📊 If we want healthcare that works for women, we need to start with research that sees us clearly, not as complications, but as standard 💭 𝗪𝗲’𝗿𝗲 𝗻𝗼𝘁 𝗹𝗼𝗼𝗸𝗶𝗻𝗴 𝗳𝗼𝗿 𝘀𝗽𝗲𝗰𝗶𝗮𝗹 𝘁𝗿𝗲𝗮𝘁𝗺𝗲𝗻𝘁. 𝗪𝗲’𝗿𝗲 𝗮𝘀𝗸𝗶𝗻𝗴 𝗳𝗼𝗿 𝘀𝗰𝗶𝗲𝗻𝗰𝗲 𝘁𝗵𝗮𝘁 𝗿𝗲𝗳𝗹𝗲𝗰𝘁𝘀 𝗿𝗲𝗮𝗹𝗶𝘁𝘆. 𝗧𝗵𝗲𝗿𝗲’𝘀 𝗻𝗼 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺 𝘁𝗵𝗮𝘁 𝗰𝗮𝗻 𝗳𝗶𝘅 𝘄𝗵𝗮𝘁 𝘄𝗲 𝗿𝗲𝗳𝘂𝘀𝗲 𝘁𝗼 𝗺𝗲𝗮𝘀𝘂𝗿𝗲 📏 -- ♻ Re-share if this resonated with you. 👩🏽⚕️ Follow Dr Fiona Pathiraja-Møller for more. #womenshealth #AI #science #clinicaltrials

AI just told women to accept 20% less pay A new study from the Technical University of Würzburg-Schweinfurt (linked in comments) just confirmed what many of us suspected: ChatGPT and other AI models systematically recommend lower salaries for women than men with identical qualifications. Up to 20% lower. In some cases, that's a $120,000 difference just by changing "he" to "she" in the prompt. 😵💫 Let that sink in for a moment. As someone who's spent years helping women negotiate their worth, this doesn't shock me. These AI models are trained on data that reflects decades of systemic bias - the same bias that created the gender pay gap in the first place. But here's what concerns me most: women are increasingly turning to AI for career advice, including salary negotiation guidance. And now we know these tools are literally programming women to undervalue themselves. So let me be crystal clear about this: ⚡ Stop outsourcing your worth to machines that don't understand your value! ⚡ Your salary negotiation shouldn't be guided by an algorithm trained on historical inequality. It should be based on your actual market value, the specific problems you solve & the measurable impact you create and linking that to what companies truly need. The real issue isn't just biased AI - it's that many women lack the confidence and skills to negotiate effectively in the first place. And now AI is reinforcing those insecurities with "data-driven" advice that's actually discrimination-driven. Here's what you should do instead: 💪 Learn to negotiate as a core professional skill, focusing on advocating for yourself rather than others (which women tend to struggle more with than men) 💪 Research salary data from multiple sources, including human ones 💪 Build confidence through practice and preparation 💪 Focus on the value you bring, not what others "think" you deserve Because here's the truth: if we don't learn to advocate for ourselves effectively, we'll always be at the mercy of systems - human or artificial - that undervalue us.

AI systems built without women's voices miss half the world and actively distort reality for everyone. On International Women's Day - and every day - this truth demands our attention. After more than two decades working at the intersection of technological innovation and human rights, I've observed a consistent pattern: systems designed without inclusive input inevitably encode the inequalities of the world we have today, incorporating biases in data, algorithms, and even policy. Building technology that works requires our shared participation as the foundation of effective innovation. The data is sobering: women represent only 30% of the AI workforce and a mere 12% of AI research and development positions according to UNESCO's Gender and AI Outlook. This absence shapes the technology itself. And a UNESCO study on Large Language Models (LLMs) found persistent gender biases - where female names were disproportionately linked to domestic roles, while male names were associated with leadership and executive careers. UNESCO's @women4EthicalAI initiative, led by the visionary and inspiring Gabriela Ramos and Dr. Alessandra Sala, is fighting this pattern by developing frameworks for non-discriminatory AI and pushing for gender equity in technology leadership. Their work extends the UNESCO Recommendation on the Ethics of AI, a powerful global standard centering human rights in AI governance. Today's decision is whether AI will transform our world into one that replicates today's inequities or helps us build something better. Examine your AI teams and processes today. Where are the gaps in representation affecting your outcomes? Document these blind spots, set measurable inclusion targets, and build accountability systems that outlast good intentions. The technology we create reflects who creates it - and gives us a path to a better world. #InternationalWomensDay #AI #GenderBias #EthicalAI #WomenInAI #UNESCO #ArtificialIntelligence The Patrick J. McGovern Foundation Mariagrazia Squicciarini Miriam Vogel Vivian Schiller Karen Gill Mary Rodriguez, MBA Erika Quada Mathilde Barge Gwen Hotaling Yolanda Botti-Lodovico

Facial recognition software used to misidentify dark-skinned women 47% of the time. Until Joy Buolamwini forced Big Tech to fix it. In 2015, Dr. Joy Buolamwini was building an art project at the MIT Media Lab. It was supposed to use facial recognition to project the face of an inspiring figure onto the user’s reflection. But the software couldn’t detect her face. Joy is a dark-skinned woman. And to be seen by the system, she had to put on a white mask. She wondered: Why? She launched Gender Shades, a research project that audited commercial facial recognition systems from IBM, Microsoft, and Face++. The systems could identify lighter-skinned men with 99.2% accuracy. But for darker-skinned women, the error rate jumped as high as 47%. The problem? AI was being trained on biased datasets: over 75% male, 80% lighter-skinned. So Joy introduced the Pilot Parliaments Benchmark, a new training dataset with diverse representation by gender and skin tone. It became a model for how to test facial recognition fairly. Her research prompted Microsoft and IBM to revise their algorithms. Amazon tried to discredit her work. But she kept going. In 2016, she founded the Algorithmic Justice League, a nonprofit dedicated to challenging bias in AI through research, advocacy, and art. She called it the Coded Gaze, the embedded bias of the people behind the code. Her spoken-word film “AI, Ain’t I A Woman?”, which shows facial recognition software misidentifying icons like Michelle Obama, has been screened around the world. And her work was featured in the award-winning documentary Coded Bias, now on Netflix. In 2019, she testified before Congress about the dangers of facial recognition. She warned that even if accuracy improves, the tech can still be abused. For surveillance, racial profiling, and discrimination in hiring, housing, and criminal justice. To counter it, she co-founded the Safe Face Pledge, which demands ethical boundaries for facial recognition. No weaponization. No use by law enforcement without oversight. After years of activism, major players (IBM, Microsoft, Amazon) paused facial recognition sales to law enforcement. In 2023, she published her best-selling book “Unmasking AI: My Mission to Protect What Is Human in a World of Machines.” She advocated for inclusive datasets, independent audits, and laws that protect marginalized communities. She consulted with the White House ahead of Executive Order 14110 on “Safe, Secure, and Trustworthy AI.” But she didn’t stop at facial recognition. She launched Voicing Erasure, a project exposing bias in voice AI systems like Siri and Alexa. Especially their failure to recognize African-American Vernacular English. Her message is clear: AI doesn’t just reflect society. It amplifies its flaws. Fortune calls her “the conscience of the AI revolution.” 💡 In 2025, I’m sharing 365 stories of women entrepreneurs in 365 days. Follow Justine Juillard for daily #femalefounder spotlights.

Last weekend, I read a deeply unsettling but absolutely critical article in the Financial Times by Laura Bates about how technology, especially AI, is reinforcing long-standing patterns of misogyny and inequality. It’s been very present in my mind these past few days.   The piece lays bare how digital tools are already being weaponized against women, from deepfakes to harassment at scale. And at the same time, women are being left out as both architects and beneficiaries of this next wave of innovation.   And it’s no wonder that this is a blind spot for the tech industry: women make up just 12% of AI researchers, and women founders receive only 2% of venture capital. As Laura put it, “We are not the ones building this new world. But we will have no choice about living in it.”   I felt both proud and sobered to see our research at Women's World Banking cited, specifically our findings on how credit-scoring algorithms can discriminate against women, at a moment when the global credit gap for women has grown to nearly $2 trillion.   Technology holds extraordinary promise. But without intention, safeguards, and accountability, it can replicate and worsen the inequalities already baked into our systems. As a result, technology-facilitated gender-based violence creates new and often unavoidable risks for the 1 million women who have just entered or are about to enter the financial system for the first time.   That’s why we focus on designing financial systems that actually work for women’s real lives. And it’s why we also must be clear-eyed about the dangers, and protect people from harm as new tools are rolled out at speed.   Opportunity and responsibility go hand in hand, and I hope this article sparks action for many more of us. https://lnkd.in/dqhem-Vz

"This report developed by UNESCO and in collaboration with the Women for Ethical AI (W4EAI) platform, is based on and inspired by the gender chapter of UNESCO’s Recommendation on the Ethics of Artificial Intelligence. This concrete commitment, adopted by 194 Member States, is the first and only recommendation to incorporate provisions to advance gender equality within the AI ecosystem. The primary motivation for this study lies in the realization that, despite progress in technology and AI, women remain significantly underrepresented in its development and leadership, particularly in the field of AI. For instance, currently, women reportedly make up only 29% of researchers in the field of science and development (R&D),1 while this drops to 12% in specific AI research positions.2 Additionally, only 16% of the faculty in universities conducting AI research are women, reflecting a significant lack of diversity in academic and research spaces.3 Moreover, only 30% of professionals in the AI sector are women,4 and the gender gap increases further in leadership roles, with only 18% of in C-Suite positions at AI startups being held by women.5 Another crucial finding of the study is the lack of inclusion of gender perspectives in regulatory frameworks and AI-related policies. Of the 138 countries assessed by the Global Index for Responsible AI, only 24 have frameworks that mention gender aspects, and of these, only 18 make any significant reference to gender issues in relation to AI. Even in these cases, mentions of gender equality are often superficial and do not include concrete plans or resources to address existing inequalities. The study also reveals a concerning lack of genderdisaggregated data in the fields of technology and AI, which hinders accurate measurement of progress and persistent inequalities. It highlights that in many countries, statistics on female participation are based on general STEM or ICT data, which may mask broader disparities in specific fields like AI. For example, there is a reported 44% gender gap in software development roles,6 in contrast to a 15% gap in general ICT professions.7 Furthermore, the report identifies significant risks for women due to bias in, and misuse of, AI systems. Recruitment algorithms, for instance, have shown a tendency to favor male candidates. Additionally, voice and facial recognition systems perform poorly when dealing with female voices and faces, increasing the risk of exclusion and discrimination in accessing services and technologies. Women are also disproportionately likely to be the victims of AI-enabled online harassment. The document also highlights the intersectionality of these issues, pointing out that women with additional marginalized identities (such as race, sexual orientation, socioeconomic status, or disability) face even greater barriers to accessing and participating in the AI field."