My Actions Speak Louder Than Your Words: When User Behavior Predicts Their Beliefs about Agents' Attributes
An implicit expectation of asking users to rate agents, such as an AI decision-aid, is that they will use only relevant information -- ask them about an agent's benevolence, and they should consider whether or not it was kind. Behavioral science, however, suggests that people sometimes use irrelevant information. We identify an instance of this phenomenon, where users who experience better outcomes in a human-agent interaction systematically rated the agent as having better abilities, being more benevolent, and exhibiting greater integrity in a post hoc assessment than users who experienced worse outcome -- which were the result of their own behavior -- with the same agent. Our analyses suggest the need for augmentation of models so that they account for such biased perceptions as well as mechanisms so that agents can detect and even actively work to correct this and similar biases of users.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
The Effect of eHealth Training on Dysarthric Speech
In the current study on dysarthric speech, we investigate the effect of web-based treatment, and whether there is a difference between content and function words. Since the goal of the treatment is to speak louder, witho…
Riemannian Geometry Speaks Louder Than Words: From Graph Foundation Model to Next-Generation Graph Intelligence
Graphs provide a natural description of the complex relationships among objects, and play a pivotal role in communications, transportation, social computing, the life sciences, etc. Currently, there is strong agreement t…
Graph LearningActions Speak Louder Than (Pass)words: Passive Authentication of Smartphone Users via Deep Temporal Features
Prevailing user authentication schemes on smartphones rely on explicit user interaction, where a user types in a passcode or presents a biometric cue such as face, fingerprint, or iris. In addition to being cumbersome an…
Actions Speak Louder than Words: Agent Decisions Reveal Implicit Biases in Language Models
While advances in fairness and alignment have helped mitigate overt biases exhibited by large language models (LLMs) when explicitly prompted, we hypothesize that these models may still exhibit implicit biases when simul…
Decision MakingFairnessActions Speak Louder than Goals:Valuing Player Actions in Soccer
Assessing the impact of the individual actions performed by soccerplayers during games is a crucial aspect of the player recruitmentprocess. Unfortunately, most traditional metrics fall short in ad-dressing this task as …
Football Action Valuation