AI is now being applied widely in society, including to support decision-making in important, resource-constrained efforts in conservation and public health. Such real-world use cases introduce new challenges, like noisy, limited data and human-in-the-loop decision-making. I show that ignoring these challenges can lead to suboptimal results in AI for social impact systems, which has led me to holistically model such systems to improve results. In addition to modelling such real-world efforts holistically, I believe we must also work with all stakeholders in this research, including by making our field more inclusive through efforts like my non-profit, Try AI.

Speaker bio

Elizabeth Bondi-Kelly is currently a Postdoctoral Fellow at MIT through the CSAIL METEOR Fellowship and an incoming Assistant Professor of Electrical Engineering and Computer Science at the University of Michigan. She has a PhD in Computer Science at Harvard University, where she was advised by Prof. Milind Tambe. Her research interests include multi-agent systems, remote sensing, computer vision, and machine learning, especially applied to conservation and public health. She has been recognized as an MIT EECS Rising Star in 2021, and has been awarded the Best Paper Runner Up at AAAI 2021, Best Application Demo Award at AAMAS 2019, Best Paper Award at SPIE DCS 2016, and an Honourable Mention for the NSF Graduate Research Fellowship Program in 2017. She has also founded Try AI, a non-profit devoted to increasing diversity, equity, and inclusion in the field of AI.