21 citations · 53 across the 16 of their papers we have counts for
4 papers · 1 filter
The Differential Effects of AI Abstention on Advice Acceptance
Nicholas Wolczynski, Yotam Liel, Maytal Saar-Tsechansky +1
AI advising systems are typically designed to always provide a response. Recent work has focused on developing systems that withhold advice in some instances, such as when advice q…
The Value of AI Advice: Personalized and Value-Maximizing AI Advisors Are Necessary to Reliably Benefit Experts and Organizations
Nicholas Wolczynski, Maytal Saar-Tsechansky, Tong Wang
Despite advances in AI's performance and interpretability, AI advisors can undermine experts' decisions and increase the time and effort experts must invest to make decisions. Cons…
Learning to Advise Humans in High-Stakes Settings
Nicholas Wolczynski, Maytal Saar-Tsechansky, Tong Wang
Expert decision-makers (DMs) in high-stakes AI-assisted decision-making (AIaDM) settings receive and reconcile recommendations from AI systems before making their final decisions.…
Human-AI Collaboration with Bandit Feedback
Ruijiang Gao, Maytal Saar-Tsechansky, Maria De-Arteaga +3
Human-machine complementarity is important when neither the algorithm nor the human yield dominant performance across all instances in a given domain. Most research on algorithmic…