257 citations · 258 across the 6 of their papers we have counts for
6 papers
Preference Conditioned Multi-Objective Reinforcement Learning: Decomposed, Diversity-Driven Policy Optimization
Tanmay Ambadkar, Sourav Panda, Shreyash Kale +2
Multi-objective reinforcement learning (MORL) seeks to train agents capable of balancing conflicting objectives. While single preference-conditioned policies offer a highly scalabl…
Fake News Detection After LLM Laundering: Measurement and Explanation
Rupak Kumar Das, Jonathan Dodge
With their advanced capabilities, Large Language Models (LLMs) can generate highly convincing and contextually relevant fake news, which can contribute to disseminating misinformat…
How to Measure Human-AI Prediction Accuracy in Explainable AI Systems
Sujay Koujalgi, Andrew Anderson, Iyadunni Adenuga +8
Assessing an AI system's behavior-particularly in Explainable AI Systems-is sometimes done empirically, by measuring people's abilities to predict the agent's next move-but how to…
Inclusive Design of AI's Explanations: Just for Those Previously Left Out, or for Everyone?
Md Montaser Hamid, Fatima Moussaoui, Jimena Noa Guevara +4
Motivations: Explainable Artificial Intelligence (XAI) systems aim to improve users' understanding of AI, but XAI research shows many cases of different explanations serving some u…
Conceptualizing the Relationship between AI Explanations and User Agency
Iyadunni Adenuga, Jonathan Dodge
We grapple with the question: How, for whom and why should explainable artificial intelligence (XAI) aim to support the user goal of agency? In particular, we analyze the relations…
Explaining Models: An Empirical Study of How Explanations Impact Fairness Judgment
Jonathan Dodge, Q. Vera Liao, Yunfeng Zhang +2
Ensuring fairness of machine learning systems is a human-in-the-loop process. It relies on developers, users, and the general public to identify fairness problems and make improvem…