Lucius EJ Bynum
I am a PhD Candidate at the NYU Center for Data Science co-advised by Julia Stoyanovich as part of the Center for Responsible AI and by Joshua Loftus at the London School of Economics.
In my research, I use causal inference and statistics to better understand bias and inequality in AI systems, machine learning, and algorithmic decision making. This work includes developing tools for inequality-aware decision making and more wholistic algorithmic fairness, leveraging counterfactual reasoning to improve model explainability and reduce pre-existing disparities, and reimagining how we use causal modeling formalisms to reason about social categories like race and gender.
I am also passionate about teaching via public outreach and making educational material in these areas.
My research is generously supported by the Microsoft Research PhD Fellowship.
news
Apr 11, 2024 | Nominated for the 2024 Future Leaders Summit on Responsible Data Science and AI! Thank you to the Michigan Institute for Data Science (MIDAS) and to all attendees for a great symposium! |
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Jan 25, 2024 | New preprint release: A New Paradigm for Counterfactual Reasoning in Fairness and Recourse. |
Dec 16, 2023 | Oral presentation at the NeurIPS Workshop on Algorithmic Fairness Through the Lens of Time (NeurIPS AFT2023) on Backtracking Counterfactual Fairness. Thanks to the organizers for a great workshop! |
Aug 15, 2023 | Gave a presentation at the KAIST Data Intelligence Lab in Daejeon, Korea as part of the NYU-KAIST Inclusive AI Workshop. Thanks to everyone at KAIST and especially Professor Stephen Whang and his lab for being such wonderful hosts! |
Jul 29, 2023 | Poster presentations at the ICML 2023 Workshop on Counterfactuals in Minds and Machines on Counterfactuals for the Future as well as Causal Dependence Plots. |