Abstract: How should we design AI/ML technologies to benefit the world's poorest m who are invisible in mainstream datasets and not only experiencing the disproportionate impact of climate change and structural inequalities but also algorithmic harms? AI/ML models, trained and evaluated with highly curated datasets and standard benchmarks, demonstrate remarkable computational efficiency in lab settings or online human-subject studies but are ineffective when deployed in the real world. Designing Ethical AI/ML systems for scientifically informing high-stake policy decisions is unquestionably one of the most difficult challenges. In this talk, I present a research program focused on Socially and Ethically Responsible AI for Sustainable Development. By bringing digitally invisible at-risk communities to the center of human-AI collaboration, the scholarship ensures fairness, accountability, transparency, and ethics as an intrinsic part of human-centered AI rather than afterthought optimization. With real-world deployments, I demonstrate what Responsible AI looks like from the perspective of the most vulnerable (e.g., smallholder farmers, racial minorities, gig workers, etc.). This publicly engaged research program shifts the paradigm of the AI revolution to make a newly realized sociotechnical world more inclusive and sustainable.

Bio: Neil Gaikwad is a Ph.D. candidate at MIT, where he is a Human Rights and Technology Fellow and Social and Ethical Responsibilities of Computing Scholar. His research in computational sustainability straddles the interface of Ethical AI and Policy for promoting global inclusion, equity, and societal development. This work has been published in premier artificial intelligence and human-computer interaction conferences (AAAI, KDD, CHI, CSCW, UIST), journals (PNAS), and featured in venues such as The New York Times, New Scientist, Bloomberg, WIRED, and Wall Street Journal. Neil has been recognized with numerous awards in science and engineering, including Facebook Ph.D. Fellowship, MIT Graduate Teaching Award, and Rising Star in Data Science by the University of Chicago, and the Karl Taylor Compton Prize, MIT’s highest student award. He has mentored more than 20 students who published impactful papers, won prestigious fellowship awards, pursued careers in research, and shifted the discourse on AI fairness and racial equity. Neil holds a master’s degree from the School of Computer Science at Carnegie Mellon University. Before MIT, he was a data scientist on Wall Street.

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