How to Build Inclusive AI Ecosystems

#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

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

AI is only as inclusive as the voices driving its development. The way we build and implement AI today will determine how it serves tomorrow. The choice is ours. It has the potential to reshape industries, but if left unchecked, it risks deepening societal divides and the inclusion gap. While we see progress. Western-centric AI development has perpetuated biases by relying on incomplete data and overlooking underserved regions. To shift this narrative, we need to move beyond the buzzwords and focus on tangible actions. Here’s how: → Diversify the data: We must actively collect and incorporate data from underrepresented regions, ensuring AI systems reflect diverse needs and experiences. → Empower diverse talent: AI development must include voices from all communities. We need initiatives that nurture talent in underserved populations to bring fresh perspectives into tech. → Engage globally: Policymakers, tech companies, and healthcare providers must collaborate, ensuring AI solutions are designed for global accessibility. → Hold ourselves accountable: Regular audits for bias in AI systems should become the norm. → Rethink governance: We need inclusive AI governance that prioritizes representation, particularly when it comes to health and social welfare. → Learn from local experts: Before implementing AI in new regions, tech developers must work alongside local experts to understand cultural nuances and real-world needs. Moreover, applying the 4D Framework: Develop, De-identify, Decipher, De-bias. We can create AI systems that are not just smarter, but also fairer, more inclusive, and global. It’s time to change the conversation. But this isn't just about building better tech. It's about expanding access, education, and funding to communities that have been left behind. It’s about ensuring that every person, no matter where they live, has a seat at the table. AI’s future doesn’t belong to one group. It belongs to all of us. The real question is: Will we design it for everyone?

💡AI isn’t just a tech revolution. It’s a values test. Because if we’re not careful, the AI “advantage” becomes just another privilege some get by birth, proximity, or budget. While we’re hosting keynotes, building agents, and debating prompt frameworks…. millions of brilliant minds still lack access to AI tools, training, infrastructure—or even a seat at the table. That’s not innovation. That’s exclusion with better branding. So here’s what I’m asking companies, leaders, and AI evangelists: • Are you only building for people who already have access? • Is your AI strategy inclusive of rural talent, frontline workers, older employees, or communities historically left out of tech revolutions? • Have you audited your AI training data for bias—and your AI enablement plans for gatekeeping? This movement is really about building a future where only a few get to build at all. So what can we actually do? • Fund access: AI tools, not just licenses. Community hubs. • Build in local context: Train models for language, culture, and use cases outside the tech bubble. • Democratize training: Pay people to learn AI. Certify them. Coach them. • Reward inclusive design: Add equity to your success metrics, not just speed or savings. • Co-create: Bring those impacted by AI into your build process—early and often. If AI is meant to “scale humans,” let’s make sure all humans are invited to scale. Not just the ones with fancy degrees, high-speed Wi-Fi, and an invite to the beta. – LC Just a thought.

"When only a handful of actors define how AI systems are built and used, public oversight erodes. These systems increasingly reflect the values and economic incentives of their creators, often at the expense of inclusion, accountability and democratic oversight. Without intervention, these trends risk entrenching structural inequities and shrinking the space for alternative approaches. This white paper outlines a strategic countervision: Public AI. It proposes a model of AI development and deployment grounded in transparency, democratic governance and open access to critical infrastructure. Public AI refers to systems that are accountable to the public, where foundational resources such as compute, data and models are openly accessible and every initiative serves a clearly defined public purpose. Grounded in a realistic analysis of the constraints across the AI stack – compute, data and models – the paper translates the concept of Public AI into a concrete policy framework with actionable steps. Central to this framework is the conviction that public AI strategies must ensure the continued availability of at least one fully open-source model with capabilities approaching those of proprietary state-of-theart systems. Achieving this goal requires three key actions: coordinated investing in the open-source ecosystem, providing public compute infrastructure, and building a robust talent base and institutional capacity. It calls for the continued existence of at least one fully open-source model near the frontier of capability and lays out three imperatives to achieve this: strengthening open-source ecosystems, investing in public compute infrastructure, and building the talent base to develop and use open models. To guide implementation, the paper introduces the concept of a “gradient of publicness” to AI policy – a tool for assessing and shaping AI initiatives based on their openness, governance structures, and alignment with public values. This framework enables policymakers to evaluate where a given initiative falls on the spectrum from private to public and to identify actionable steps to increase public benefit"

🔥 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

How do we scale Generative AI without compromising ethics, sustainability, or data integrity? Here are my ten principles: 🔹 Strong Data Foundation: Ensure clean, reliable, and well-structured data to build effective AI systems. 🔹 Bias Mitigation: AI must fairly represent all voices through diverse datasets and rigorous testing. 🔹 Energy Efficiency: Consider the full environmental footprint—carbon, water, and energy consumption—to minimize AI’s impact. 🔹 Transparency: Explainable AI is key to earning user trust by making decisions understandable. 🔹 Data Privacy: Privacy-first design must be prioritized to respect users’ growing data concerns. 🔹 Human Oversight: AI should enhance human judgment, with human-in-the-loop systems ensuring responsible outcomes. 🔹 Guardrails: Implement ethical guardrails to prevent misuse and ensure AI aligns with societal values. 🔹 Collaboration with Regulators: Work closely with regulators like the EU AI Act to ensure compliance and trust. 🔹 Continuous Monitoring and Auditing: Regularly audit AI systems to catch biases and inefficiencies, ensuring ongoing alignment with ethical goals. 🔹 Inclusive Development: Diverse, inclusive teams bring varied perspectives, helping avoid blind spots and foster fair AI. These principles offer a roadmap for scaling AI that is both innovative and responsible, ensuring a balance between growth and ethical standards. #ai #generativeai #responsibleai #genai #ethicalai

Much has been said about what AI can enable. But being able to unlock the potential remains a distant dream for many — as we face a widening divide in access to essential infrastructure, computing capabilities, and high-quality data. How then, can we best create sustainable AI systems that serve all people equitably and uplift all communities? [1] Sustainable infrastructure is non-negotiable: Path forward requires environmentally responsible AI infrastructure. [2] Data equity is a fundamental right: Diverse and inclusive datasets aren't just nice-to-have. When AI systems are trained predominantly with data that is western-centric, we risk perpetuating culture biases. [3] Responsible AI must be built in: We must ensure that technology improves the human condition, not just efficiency gains. We must strive for a future where the impact of AI is guided by robust ethical guardrails, safety and security controls. [4] Collaboration is key: No single nation can navigate this transformation journey alone; rather, it will require unprecedented collaboration between governments, academia, private and public sectors. Success cannot be measured in just tech achievements alone, but in how we can ensure AI's benefits can reach every corner of our society. #AI #FinancialServices #ResponsibleAI #BankingOnAI

My nonprofit leaders, when you think of AI and try to focus on efficiency – I want you to remember something for those times: Efficiency isn't the same as equity. And efficiency at the expense of fairness, inclusivity, or trust is a cost we do not want to pay. A tool is only as good as the questions you ask it. When thinking of AI, focus not on what is easy to automate or measurable but on what truly matters. What is easy to automate is a great starting point, but think deeper: ● Will your AI solution amplify your community's voices—or exclude them? ● Will your AI tools be designed to reduce bias—or are they built on biased datasets? ● Can you use those AI solutions to ask better questions as a team, or are you using them to avoid asking hard questions? Here's the thing: AI will follow your lead. If you don't prioritize ethics in using these tools, AI will perpetuate the same inequities baked into the systems you are working to change. Let's take an example. Imagine you are using AI to analyze donor behavior. Your algorithm flags certain groups as "high potential" and others as "low priority." Ask this tool: "But why?" Did the AI learn this from historical data where certain communities were underrepresented? Did it factor in systemic barriers those groups face? Or is it just rewarding the status quo? So, when you think of AI and try to focus on efficiency – I want you to remember something for those times: ● Ask who is missing from your data Before deploying any AI tool, ask yourself: Who might be excluded or misrepresented in the data? For example, are you analyzing responses from communities that typically don't engage because they've been overlooked in the past? ● Ask your tech vendors for biases, not just accurate outputs Don't just ask if your AI is producing correct results. Ask if it's producing fair results. Work with your tech vendors to see if certain groups are systematically excluded or misclassified. ● Use AI to start conversations in your team, not end them AI outputs are just the beginning. Use them to start deeper discussions with your team and your community. For example, if your tool highlights a trend, validate it with lived experiences and direct feedback. Your leadership with AI isn't about adopting tools faster. It's about asking better questions and bringing people together to address the answers, even when the questions might make us all uncomfortable. #nonprofits #nonprofitleadership #community

UNESCO for the People – Driving Ethical and Inclusive AI for Humanity Artificial Intelligence is transforming our world. It shapes how we learn, work, and govern – yet billions of people remain excluded from its benefits. At the same time, the risks are mounting: biased systems, opaque algorithms, growing inequalities, and job displacement. This is not only a technological challenge; it is a human rights challenge.   UNESCO has taken the lead by adopting the first global Recommendation on the Ethics of AI – a landmark framework establishing universal principles for fairness, transparency, and accountability. But adoption is only the beginning. The real challenge is inclusive, equitable implementation: turning principles into action so AI serves humanity, not the other way around. At the UNESCO Global Forum on the Ethics of AI in June, scientists, policymakers, and innovators delivered a clear message: ethical AI cannot exist without strong investment in education, infrastructure, and global cooperation.   Throughout my campaign, one lesson stood out: AI must serve people – but first, we must imagine the societies we want, before technology decides for us. “UNESCO for the People” envisions a future where AI promotes peace, equity, and sustainability. Acting with courage, knowledge, and cooperation, we can make AI humanity’s greatest ally by: •Supporting Member States in implementing the 2021 Recommendation on the Ethics of AI, the UNGA resolution adopted in March 2024 on “Seizing the opportunities of safe, secure, and trustworthy AI systems for sustainable development,” and the Pact for the Future. This includes embedding human rights into AI governance so that every system upholds human dignity, freedom of expression, non-discrimination, social justice, international law, and respect for cultural diversity. •Reducing disparities by supporting developing countries through knowledge-sharing, capacity-building programs, innovative financing mechanisms, and the development of infrastructure, multilingual AI systems, and open educational resources – ensuring no community is left behind. • Fostering international solidarity through inclusive dialogue and joint research initiatives that unite governments, academia, industry, and civil society, while promoting human-centered and sustainable AI, rooted in open science. • Making AI a driver of inclusion by leveraging its potential in education, teacher training, youth engagement, local innovation ecosystems, and cultural heritage management. • Anticipating future challenges through a Global Foresight Mechanism to monitor technological trends and prepare societies for their implications, while developing ethical frameworks for frontier technologies such as neurotechnology, quantum sciences, and synthetic biology – ensuring a balance between risks and opportunities before risks outpace regulation.