most citedAccountability in an Algorithmic Society: Relationality, Responsibility, and Robustness in Machine Learning

125 citations · 162 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CY2023★ 2 cited

Talkin' 'Bout AI Generation: Copyright and the Generative-AI Supply Chain

Katherine Lee, A. Feder Cooper, James Grimmelmann

"Does generative AI infringe copyright?" is an urgent question. It is also a difficult question, for two reasons. First, "generative AI" is not just one product from one company. I…

cs.LG2023★ 1 cited

Coordinating Distributed Example Orders for Provably Accelerated Training

A. Feder Cooper, Wentao Guo, Khiem Pham +4

Recent research on online Gradient Balancing (GraB) has revealed that there exist permutation-based example orderings for SGD that are guaranteed to outperform random reshuffling (…

cs.LG2023★ 4 cited

Arbitrariness and Social Prediction: The Confounding Role of Variance in Fair Classification

A. Feder Cooper, Katherine Lee, Madiha Zahrah Choksi +6

Variance in predictions across different trained models is a significant, under-explored source of error in fair binary classification. In practice, the variance on some data examp…

cs.CY2022★ 4 cited

Fast or Accurate? Governing Conflicting Goals in Highly Autonomous Vehicles

A. Feder Cooper, Karen Levy

The tremendous excitement around the deployment of autonomous vehicles (AVs) comes from their purported promise. In addition to decreasing accidents, AVs are projected to usher in…

cs.CY2022★ 16 cited

Non-Determinism and the Lawlessness of Machine Learning Code

A. Feder Cooper, Jonathan Frankle, Christopher De Sa

Legal literature on machine learning (ML) tends to focus on harms, and thus tends to reason about individual model outcomes and summary error rates. This focus has masked important…

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.CY2022★ 125 cited

Accountability in an Algorithmic Society: Relationality, Responsibility, and Robustness in Machine Learning

A. Feder Cooper, Emanuel Moss, Benjamin Laufer +1

In 1996, Accountability in a Computerized Society [95] issued a clarion call concerning the erosion of accountability in society due to the ubiquitous delegation of consequential f…

cs.CY2022★ 9 cited

Making the Unaccountable Internet: The Changing Meaning of Accounting in the Early ARPANET

A. Feder Cooper, Gili Vidan

Contemporary concerns over the governance of technological systems often run up against narratives about the technical infeasibility of designing mechanisms for accountability. Whi…