6 citations · 10 across the 5 of their papers we have counts for
5 papers
Same Words, Different Judgments: How Preferences Vary Across Modalities
Aaron Broukhim, Nadir Weibel, Eshin Jolly
Preference-based reinforcement learning (PbRL) is the dominant framework for aligning AI systems to human preferences. However, evaluation protocols for such data were designed for…
Preference-Based Learning in Audio Applications: A Systematic Analysis
Aaron Broukhim, Yiran Shen, Prithviraj Ammanabrolu +1
Despite the parallel challenges that audio and text domains face in evaluating generative model outputs, preference learning remains remarkably underexplored in audio applications.…
Predicting Trust In Autonomous Vehicles: Modeling Young Adult Psychosocial Traits, Risk-Benefit Attitudes, And Driving Factors With Machine Learning
Robert Kaufman, Emi Lee, Manas Satish Bedmutha +2
Low trust remains a significant barrier to Autonomous Vehicle (AV) adoption. To design trustworthy AVs, we need to better understand the individual traits, attitudes, and experienc…
What Did My Car Say? Impact of Autonomous Vehicle Explanation Errors and Driving Context On Comfort, Reliance, Satisfaction, and Driving Confidence
Robert Kaufman, Aaron Broukhim, David Kirsh +1
Explanations for autonomous vehicle (AV) decisions may build trust, however, explanations can contain errors. In a simulated driving study (n = 232), we tested how AV explanation e…
Developing Situational Awareness for Joint Action with Autonomous Vehicles
Robert Kaufman, David Kirsh, Nadir Weibel
Unanswered questions about how human-AV interaction designers can support rider's informational needs hinders Autonomous Vehicles (AV) adoption. To achieve joint human-AV action go…