5 papers
Position: We Need Practical AI Alignment Methods to Mirror Human Reasoning
Vijay Keswani, Breanna K. Nguyen, Cyrus Cousins +3
AI systems are increasingly employed as decision aids, decision delegates, or autonomous decision-makers. This position paper argues that in many settings, particularly high-stakes…
Moral Change or Noise? On Problems of Aligning AI With Temporally Unstable Human Feedback
Vijay Keswani, Cyrus Cousins, Breanna Nguyen +4
Alignment methods in moral domains seek to elicit moral preferences of human stakeholders and incorporate them into AI. This presupposes moral preferences as static targets, but su…
Towards Cognitively-Faithful Decision-Making Models to Improve AI Alignment
Cyrus Cousins, Vijay Keswani, Vincent Conitzer +3
Recent AI trends seek to align AI models to learned human-centric objectives, such as personal preferences, utility, or societal values. Using standard preference elicitation metho…
Algorithms and Analysis for Optimizing Robust Objectives in Fair Machine Learning
Cyrus Cousins
The original position or veil of ignorance argument of John Rawls, perhaps the most famous argument for egalitarianism, states that our concept of fairness, justice, or welfare sho…
To Pool or Not To Pool: Analyzing the Regularizing Effects of Group-Fair Training on Shared Models
Cyrus Cousins, I. Elizabeth Kumar, Suresh Venkatasubramanian
In fair machine learning, one source of performance disparities between groups is over-fitting to groups with relatively few training samples. We derive group-specific bounds on th…