activity
20202024
most citedFairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations

55 citations · 75 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CY2024

Beyond Personhood: Agency, Accountability, and the Limits of Anthropomorphic Ethical Analysis

Jessica Dai

What is agency, and why does it matter? In this work, we draw from the political science and philosophy literature and give two competing visions of what it means to be an (ethical…

cs.AI2024

Mapping Social Choice Theory to RLHF

Jessica Dai, Eve Fleisig

Recent work on the limitations of using reinforcement learning from human feedback (RLHF) to incorporate human preferences into model behavior often raises social choice theory as…

stat.ME2023★ 4 cited

Clip-OGD: An Experimental Design for Adaptive Neyman Allocation in Sequential Experiments

Jessica Dai, Paula Gradu, Christopher Harshaw

From clinical development of cancer therapies to investigations into partisan bias, adaptive sequential designs have become increasingly popular method for causal inference, as the…

cs.LG2022★ 55 cited

Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations

Jessica Dai, Sohini Upadhyay, Ulrich Aivodji +2

As post hoc explanation methods are increasingly being leveraged to explain complex models in high-stakes settings, it becomes critical to ensure that the quality of the resulting…

cs.LG2022★ 1 cited

Repairing Regressors for Fair Binary Classification at Any Decision Threshold

Kweku Kwegyir-Aggrey, A. Feder Cooper, Jessica Dai +3

We study the problem of post-processing a supervised machine-learned regressor to maximize fair binary classification at all decision thresholds. By decreasing the statistical dist…

cs.LG2021★ 6 cited

What will it take to generate fairness-preserving explanations?

Jessica Dai, Sohini Upadhyay, Stephen H. Bach +1

In situations where explanations of black-box models may be useful, the fairness of the black-box is also often a relevant concern. However, the link between the fairness of the bl…

cs.CY2020★ 9 cited

Fair Machine Learning Under Partial Compliance

Jessica Dai, Sina Fazelpour, Zachary C. Lipton

Typically, fair machine learning research focuses on a single decisionmaker and assumes that the underlying population is stationary. However, many of the critical domains motivati…