Neil Gaikwad is a final-year Ph.D. candidate at the Massachusetts Institute of Technology, specializing in Ethical and Responsible AI and Policy for Sustainable Development. He extensively works with policymakers and marginalized communities to tackle the adverse risks posed by climate change, algorithmic harms, and institutional breakdowns. By harnessing the collective intelligence of humans and machines, his research provides value-sensitive methods to design ethical AI systems for high-stake decision-making. This work addresses the sociotechnical challenges that come with this, such as sparse datasets with poor data quality and imbalance, biases in satellite observation data, distribution shifts across geographies, and algorithmic aversion. Neil holds a master's degree from the School of Computer Science at Carnegie Mellon University, where he worked at the Robotics Institute and Software and Societal Systems Department. He was also a part of the Complex Systems Summer School at the Santa Fe Institute. Before MIT, Neil worked extensively on machine learning and financial decision-making research in his job as a data scientist on Wall Street.

Neil leads the development of Data-driven Humanitarian Mapping and Policymaking, an emerging data science research program (Gaikwad et al., 2020, 2021, 2022). His research has been published in artificial intelligence and human-computer interaction conferences (AAAI, ACM CSCW, ACM UIST, ACM CHI) and a scientific journal (PNAS), and also featured in the New York Times, Bloomberg, WIRED, and The Wall Street Journal. Neil is one of the principal creators and founding members of Daemo, a self-governed AI data market, and EteRNA, a social+crowd computing system that enables massive scale human and machine collaboration to realize scientific discoveries neither of them can accomplish in isolation. He led the development of Daemo's sociotechnical architecture that demonstrates mechanisms for designing sustainable AI data market systems (Gaikwad et al., 2015, 2016, 2017). His foundational contributions draw on incentive-compatible mechanisms from game theory (reputation systems for AI data markets), portfolio management (tranches-based guilds for the future of work), and human-centered design (shared mental model of task composition in AI data markets).

Neil's research has resulted in deployable open-source systems, environmental art exhibitions, and technical talks at global policymaking forums of the United Nations and European Union. His scholarship continues to benefit researchers and practitioners worldwide. Daemo has been cited in the International Labour Organization's (ILO) report on Digital Labour Platforms and the Future of Work and deployed to create SQuAD, Stanford's Natural Language Processing dataset. Besides, Boomerang (Gaikwad et al. 2016) has been a part of the advanced HCI/human-computation course curriculum at leading universities. EteRNA has reached over 250,000 citizen scientists as of 2016; it was also one of the 25 great ideas from CMU's School of Computer Science. Neil's TEDx talk presents his vision for just market institutions in the era of AI.

Neil is committed to increasing diversity and belonging in science, technology, and policy. He was one of the core members of the Stanford Crowd Research Initiative, MIT's Innovating for Billions in Emerging Worlds Leadership Council, and Computational Sustainability Open Graduate Seminar organizers. He has mentored over 20 students, who have gone on to win prestigious awards (e.g., Apple Ph.D. Fellowship), pursue careers in research, and publish influential scholarship that has shifted the discourse on AI fairness. Neil's research, teaching, and professional service have been widely recognized with the Facebook Research Ph.D. Fellowship, William Asbjornsen Albert Memorial Fellowship (awarded to one MIT student conducting research in modern science and engineering), UChicago Rising Stars in Data Science, MIT GSC Graduate Teaching Award (presented annually to one MIT professor or teaching assistant from each school for excellence in teaching a graduate-level course), the Karl Taylor Compton Prize (MIT's highest student award).