Importance of Inclusivity in AI Development

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

🔥 Can We Build Inclusive Agentic Systems Without Inclusive Training Data? When I first heard people talk about agentic AI — machines that can reason, decide, and act on our behalf — I was fascinated. But then one question hit me hard: How can we expect inclusive intelligence from exclusive data? I see this every day. The more I use these systems, the clearer it becomes — they speak one cultural language fluently, and stumble on the rest. The logic feels Western. The tone feels corporate. The empathy feels… selective. Let’s break down why that matters: → Most training data still comes from English-speaking, digitally rich nations. → The behaviors encoded reflect a small slice of humanity. → The missing perspectives aren’t “edge cases” — they’re billions of people. Now imagine giving that system agency. A biased chatbot can misinform. A biased agent can act — negotiate, reject, decide — without ever seeing the full picture of humanity it represents. So what’s the way forward? In my opinion, we can’t fix this with PR statements or prompt engineering — we need infrastructure-level inclusion: ✅ Build decentralized data pipelines where local communities own their voice and context. ✅ Incentivize global annotation networks that reflect cultural nuance. ✅ Create regulatory sandboxes for testing fairness dynamically, not statistically. ✅ And most importantly — give non-English regions a stake in how foundational models evolve. Because inclusion isn’t a “nice to have.” It’s an engineering challenge. If we don’t solve it now, agentic AI won’t just replicate bias — it’ll automate it. So I’ll ask again: can an AI truly act for everyone if it was only ever trained to understand some? #AIethics #AgenticAI #BiasInAI #Inclusion #ResponsibleAI #FutureofAI

DEI Isn’t About Being “Woke”—It’s About Existing This holiday, I shared a beautiful photo of me and my sister standing in front of a Christmas tree, full of joy and warmth. Curious about AI tools, I used one to describe our photo and generate a “similar” version. But what I got back wasn’t us. The generated image erased everything unique about me and my sister. It replaced our individuality and vibrant presence with generic, stereotyped versions of people who didn’t look like us. This wasn’t just a technical glitch—it was a reminder of the deeply ingrained biases in AI. This experience hit hard. It’s not just about this one tool. It’s about the larger message: without inclusive practices, people like me are literally erased. DEI (diversity, equity, and inclusion) isn’t about being “woke.” It’s about ensuring that all of us—our identities, our experiences, and our existence—are represented and valued. When AI fails to represent people accurately, it highlights a systemic issue: Diversity in AI Development: AI tools must be built with diverse data sets and teams to reflect the richness of humanity. Equity in Representation: It’s not enough for AI to be accurate for some—it must work for all. Inclusion as a Core Value: This is not optional. If systems and practices aren’t inclusive, they exclude. Period. The gap between the original photo of me and my sister and the AI-generated result made it painfully clear: without inclusive practices, some of us are left out entirely. This isn’t about being trendy—it’s about existing in a world that sees us. We need better. We deserve better. #AI #DEI #InclusionMatters #Representation #BiasInTech #DiversityInAI

Can Artificial Intelligence be racist? Rona Wang, an inspiring MIT graduate, recently shared her thought-provoking experience with artificial intelligence (AI). In her encounter with an AI image creator, Rona, an Asian-American, sought to generate a "professional" headshot for her LinkedIn profile. However, she was taken aback when the AI-generated image presented her with features that resembled those of a Caucasian individual, with lighter skin and blue eyes. This incident has prompted quite a few conversations about whether the AI was racist... However, I think we need to realise that AI today is merely a reflection of the biases of its human creators! AI is not intelligent on its own, rather, it operates as an aggregator of the data it is given by humans. Currently, most AI algorithms and models are built on datasets from majority white countries, which inadvertently perpetuate their biases. This leads to the biased outcomes shown in Roma's experience, where "professionalism" is associated with specific skin and eye colours. I don't think that technology is inherently evil. Instead, I believe that Rona's experience highlights the need for increased diversity and inclusion in the development of AI and future technologies. All humans have biases and it's only by working and learning together that we can effectively address them. Especially as AI is becoming more and more mainstream, we need to recognise incidents like Rona's as cautionary tales, as biases like these can lead to far greater consequences as AI grows. By centring inclusion and diversity, we can mitigate the risk of perpetuating unconscious biases and ensure that technology serves everyone equitably. I believe that together we have the power to shape AI into a force for positive change. #AI #Diversity #Inclusion #Equity #Equality #Intersectional #Culture #Technology #Discrimination #Race #Racism #ArtificialIntelligence #ChatGPT #OpenAI #Midjourney #StableDiffusion #Tech #Ethics #Bias [Image Description] A square image with text and two photos beneath the text. The text reads "An MIT student asked an AI to make her LinkedIn headshot more "professional". It gave her lighter skin and blue eyes." The two photos are of Rona Wang, an Asian-American girl wearing a maroon t-shirt. The left photo is her original photo where she has dark brown eyes and hair. The right the AI-generated photo where she is edited to have lighter skin and blue eyes.

𝗔𝗜 𝗶𝘀 𝗼𝗻𝗹𝘆 𝗮𝘀 𝗳𝗮𝗶𝗿 𝗮𝘀 𝘁𝗵𝗲 𝘄𝗼𝗿𝗹𝗱 𝗶𝘁 𝗹𝗲𝗮𝗿𝗻𝘀 𝗳𝗿𝗼𝗺. Artificial Intelligence isn’t created in a vacuum - it’s trained on data that reflects the world we’ve built. And that world carries deep, historic inequities. If the training data includes patterns of exclusion, such as who gets promoted, who gets paid more, whose CVs are ‘successful’, then AI systems learn those patterns and replicate them. At scale and at pace. We’re already seeing the consequences: 🔹Hiring tools that favour men over women 🔹Voice assistants that misunderstand female voices 🔹Algorithms that promote sexist content more widely and more often This isn’t about a rogue line of code. It’s about systems that reflect the values and blind spots of the people who build them. Yet women make up just 35% of the US tech workforce. And only 28% of people even know AI can be gender biased. That gap in awareness is dangerous. Because what gets built, and how it behaves, depends on who’s in the room. So what are some practical actions we can take? Tech leaders: 🔹 Build systems that are in tune with women’s real needs 🔹 Invest in diverse design and development teams 🔹 Audit your tools and data for bias 🔹 Put ethics and gender equality at the core of AI development, not as an afterthought Everyone else: 🔹 Don’t scroll past the problem 🔹 Call out gender bias when you see it 🔹 Report misogynistic and sexist content 🔹 Demand tech that works for all women and girls This isn’t just about better tech. It is fundamentally about fairer futures. #GenderEquality #InclusiveTech #EthicalAI Attached in the comments is a helpful UN article.

New Stanford research found that generative AI tools, including ChatGPT, consistently portrayed older women as younger and less experienced, while rating older men more highly, even when given the exact same inputs. This isn’t just a glitch. It’s a mirror reflecting long-standing social biases and a warning. As organizations increasingly rely on AI for hiring, performance reviews, and talent development, we need to ask tough questions: Are our tools reinforcing outdated stereotypes? Who is being disadvantaged by “automated” decisions? How do we build systems that elevate rather than erase experience? Diversity and inclusion cannot stop at policy or training. It must extend into the data and models shaping the future of work. #AIEthics #FutureOfWork #WomenInTech #DEI #ResponsibleAI #AIBias #Leadership #TechEquity #AIFairness #DigitalTransformation

#AiDays2025 Round Table : #Community Sourcing for low resource languages In an era where AI is fast shaping the contours of our digital future, VISWAM.AI initiative stands as a timely and transformational one. Their mission to build community-sourced Large Language Models (LLMs), grounded in India’s rich linguistic and cultural diversity, is not just pioneering—it’s redefining how inclusive and ethical AI should be built. By anchoring their work in community participation, linguistic preservation, and ethical co-creation, Viswam.ai offers a people-first approach to AI—moving beyond data extraction to cultural stewardship. Their ambition to mobilize 1 lakh community interns to collect data from underrepresented geographies across India is both bold and brilliant. This isn’t just about building better AI—it’s about building equity, agency, and cultural resilience through AI. 1. Linguistic Equity by Design In India, where linguistic hegemony often privileges English and Hindi, AI systems risk reinforcing this imbalance. The solution? Intentional design. Allocate equal engineering and validation efforts to low-resource languages. Ethical AI must be built on informed consent, community ownership, and fair compensation—because data is not just input, it’s identity and heritage. 2. Decentralized Internship Model By decentralizing AI development, we bridge the urban-rural digital divide. This model should focus on: Capacity building through training in ethics and digital literacy Inclusivity by involving women, Dalit and Adivasi youth Localized platforms using mobile-first tools in native languages Partnerships with Swecha, local NGOs, and institutions serve as trust bridges to ensure mentorship and sustainability. 3. Tools for Low-Resource Languages Many Indian languages are oral-first, with complex dialects and sparse corpora. Community-driven solutions—like collecting voice datasets from folklore, and crowdsourcing annotation—are key. Elders, poets, and storytellers become linguistic technologists, preserving not just language but legacy. 4. Trust & Transparency Bias in AI is structural. To mitigate it: Include diverse dialects and accents in training Conduct bias testing and community validation Promote explainable AI with local language dashboards and storytelling What’s Next? A living white paper on ethics, governance, and technical guidelines A roadmap for the internship program, with toolkits and impact metrics Collaboration with literary and linguistic organizations to enrich model depth VISWAM.AI is planting seeds for an AI movement rooted in language justice, data sovereignty, and community wisdom. Let’s co-create systems that don’t just understand our languages—but respect our voices. DC* Chaitanya Chokkareddy Kiran Chandra Ramesh Loganathan Centific

Designing AI for Outliers: Why Inclusion is Key to Equitable AI. In a recent podcast discussion I had with Debra Ruh, Neil Milliken, and David Banes (Chairperson of the Equitable AI Alliance), we explored an important question:  How can we ensure that AI includes everyone—especially the outliers? David shared an insightful perspective: "If you build to include the outliers, you include everybody in your planning." This concept challenges developers to focus on those who don’t fit the “average” or “mainstream” patterns, ensuring that AI systems are inclusive, equitable, and free from bias. The Equitable AI Alliance, an initiative by Zero Project and Seneca Trust, is working to: - Amplify opportunities that AI offers to people with disabilities. - Address risks and biases that may exclude or disadvantage certain groups. - Promote co-design by involving people with lived experience of disabilities from the start—not just as testers, but as collaborators in AI development. David also highlighted the importance of breaking out of the echo chamber and placing disability and inclusion on the agendas of mainstream conferences in technology, education, employment, and healthcare. While progress has been made at events like MWC Barcelona and SuperAI & Robotic Tech Conference, inclusion often remains a lower priority, even at diversity and inclusion conferences. Key challenges discussed: 1 - Transparency: Understanding how AI processes data and makes decisions is essential to identifying and addressing bias. 2 - Data and Privacy: Balancing the privacy of individuals with disabilities while ensuring datasets fairly represent them is a complex but vital task. 3 - Global Perspectives: Definitions of “inclusion” vary by culture, and AI must account for these differences to create solutions that work for everyone. The Equitable AI Alliance has created a freely available Resource Hub to help organisations build capacity and advocate for accessible AI. Their webinars and LinkedIn Disability Inclusive AI group are additional ways to connect and collaborate. As David said, "AI can amplify existing problems if we’re not intentional about inclusion." Let’s work to ensure AI benefits everyone—especially those historically left out. #AI #Inclusion #Accessibility #EquitableAI #AIEthics

If AI is learning from biased data, what happens to candidates with disabilities? The rise of automated hiring tools may be locking out millions, and no one is noticing, because it's silent. AI now scans resumes and analyzes video interviews, and companies are adopting it faster than ever. A late-2023 IBM survey of over 8,500 global IT professionals found that 𝟰𝟮% 𝗼𝗳 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀𝗲𝘀 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝘂𝘀𝗲 𝗔𝗜 𝗶𝗻 𝗿𝗲𝗰𝗿𝘂𝗶𝘁𝗶𝗻𝗴, and 𝗮𝗻𝗼𝘁𝗵𝗲𝗿 𝟰𝟬% 𝗮𝗿𝗲 𝗰𝗼𝗻𝘀𝗶𝗱𝗲𝗿𝗶𝗻𝗴 𝗶𝘁. The hope was that AI would reduce hiring bias. But in many cases, the opposite is happening. When trained on data that excludes people with disabilities, it learns to overlook them, too. In June 2025, the New York City Bar Association released a report on The Impact of the Use of AI on People with Disabilities (linked in the comments). Findings show that the statistical nature of AI often leads to discrimination, especially against people with disabilities who fall outside the "average" profiles these systems are built around. The scale of the issue is hard to ignore. Some might argue that one biased hiring manager could affect dozens of candidates in a year. But, as Hilke Schellmann points out, a flawed algorithm deployed across a major employer could impact hundreds of thousands. And because many vendors are rushing underdeveloped tools to market (driven by demand and profit, of course), there's little transparency or accountability. Companies using them often avoid admitting potential harm, fearing legal risk. So what can be done? Making AI inclusive requires a complete shift in how it's developed and implemented, with disability inclusion embedded from the start: ▶️ Use better data. Train AI using datasets that reflect the full range of human experiences, including physical, sensory, cognitive, and mental health disabilities, collected ethically and with consent. ▶️ Design with accessibility in mind. Build tools that work for everyone from the beginning. That includes compatibility with screen readers, voice recognition, and adjustable visual environments and formats. ▶️ Co-create with disabled people. Involve people with disabilities at every stage, from ideation to testing to launch. Feedback should be continuous, not one-off. ▶️ Test for bias. Run regular audits to detect and address bias. Create clear pathways for users to report issues and request improvements. One promising tool is the Conditional Demographic Disparity test, co-developed in 2020 by Sandra Wachter, Professor of Technology and Regulation at the University of Oxford. This public framework helps detect bias in hiring algorithms and pinpoint decision criteria driving inequality - enabling fairer, more accurate systems. Amazon and IBM are already using it. Be honest - how confident are we in the tools we're using to screen talent? #InclusiveHiring #HiringBias #AIRegulation #DisabilityInclusion