153 citations · 303 across the 15 of their papers we have counts for
4 papers · 1 filter
TED: Teaching AI to Explain its Decisions
Michael Hind, Dennis Wei, Murray Campbell +5
Artificial intelligence systems are being increasingly deployed due to their potential to increase the efficiency, scale, consistency, fairness, and accuracy of decisions. However,…
Interpretable Multi-Objective Reinforcement Learning through Policy Orchestration
Ritesh Noothigattu, Djallel Bouneffouf, Nicholas Mattei +6
Autonomous cyber-physical agents and systems play an increasingly large role in our lives. To ensure that agents behave in ways aligned with the values of the societies in which th…
Teaching Meaningful Explanations
Noel C. F. Codella, Michael Hind, Karthikeyan Natesan Ramamurthy +5
The adoption of machine learning in high-stakes applications such as healthcare and law has lagged in part because predictions are not accompanied by explanations comprehensible to…
Learning to Teach in Cooperative Multiagent Reinforcement Learning
Shayegan Omidshafiei, Dong-Ki Kim, Miao Liu +5
Collective human knowledge has clearly benefited from the fact that innovations by individuals are taught to others through communication. Similar to human social groups, agents in…