4 citations · 4 across the 7 of their papers we have counts for
7 papers
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…
Why Do Decision Makers (Not) Use AI? A Cross-Domain Analysis of Factors Impacting AI Adoption
Rebecca Yu, Valerie Chen, Ameet Talwalkar +1
Growing excitement around deploying AI across various domains calls for a careful assessment of how human decision-makers interact with AI-powered systems. In particular, it is ess…
Can AI Model the Complexities of Human Moral Decision-Making? A Qualitative Study of Kidney Allocation Decisions
Vijay Keswani, Vincent Conitzer, Walter Sinnott-Armstrong +3
A growing body of work in Ethical AI attempts to capture human moral judgments through simple computational models. The key question we address in this work is whether such simple…
Rethinking Distance Metrics for Counterfactual Explainability
Joshua Nathaniel Williams, Anurag Katakkar, Hoda Heidari +1
Counterfactual explanations have been a popular method of post-hoc explainability for a variety of settings in Machine Learning. Such methods focus on explaining classifiers by gen…
On The Stability of Moral Preferences: A Problem with Computational Elicitation Methods
Kyle Boerstler, Vijay Keswani, Lok Chan +4
Preference elicitation frameworks feature heavily in the research on participatory ethical AI tools and provide a viable mechanism to enquire and incorporate the moral values of va…
On the Pros and Cons of Active Learning for Moral Preference Elicitation
Vijay Keswani, Vincent Conitzer, Hoda Heidari +2
Computational preference elicitation methods are tools used to learn people's preferences quantitatively in a given context. Recent works on preference elicitation advocate for act…