296 citations · 575 across the 7 of their papers we have counts for
9 papers
How to post-train on a surrogate: Envelope sampling mitigates reward hacking
Sanjit Dandapanthula, Shuvom Sadhuka, Samir Khan +3
Large language models (LLMs) are commonly post-trained against LLM judges and other cheap surrogates because the true reward, such as human preference, is too expensive to query at…
Doubting AI Predictions: Influence-Driven Second Opinion Recommendation
Maria De-Arteaga, Alexandra Chouldechova, Artur Dubrawski
Effective human-AI collaboration requires a system design that provides humans with meaningful ways to make sense of and critically evaluate algorithmic recommendations. In this pa…
Human-Algorithm Collaboration: Achieving Complementarity and Avoiding Unfairness
Kate Donahue, Alexandra Chouldechova, Krishnaram Kenthapadi
Much of machine learning research focuses on predictive accuracy: given a task, create a machine learning model (or algorithm) that maximizes accuracy. In many settings, however, t…
The effect of differential victim crime reporting on predictive policing systems
Nil-Jana Akpinar, Maria De-Arteaga, Alexandra Chouldechova
Police departments around the world have been experimenting with forms of place-based data-driven proactive policing for over two decades. Modern incarnations of such systems are c…
Leveraging Expert Consistency to Improve Algorithmic Decision Support
Maria De-Arteaga, Vincent Jeanselme, Artur Dubrawski +1
Machine learning (ML) is increasingly being used to support high-stakes decisions. However, there is frequently a construct gap: a gap between the construct of interest to the deci…
A Case for Humans-in-the-Loop: Decisions in the Presence of Erroneous Algorithmic Scores
Maria De-Arteaga, Riccardo Fogliato, Alexandra Chouldechova
The increased use of algorithmic predictions in sensitive domains has been accompanied by both enthusiasm and concern. To understand the opportunities and risks of these technologi…
What's in a Name? Reducing Bias in Bios without Access to Protected Attributes
Alexey Romanov, Maria De-Arteaga, Hanna Wallach +7
There is a growing body of work that proposes methods for mitigating bias in machine learning systems. These methods typically rely on access to protected attributes such as race,…
Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting
Maria De-Arteaga, Alexey Romanov, Hanna Wallach +6
We present a large-scale study of gender bias in occupation classification, a task where the use of machine learning may lead to negative outcomes on peoples' lives. We analyze the…
Learning under selective labels in the presence of expert consistency
Maria De-Arteaga, Artur Dubrawski, Alexandra Chouldechova
We explore the problem of learning under selective labels in the context of algorithm-assisted decision making. Selective labels is a pervasive selection bias problem that arises w…