Do Humans Trust Advice More if it Comes from AI? An Analysis of Human-AI Interactions

• URL: http://arxiv.org/abs/2107.07015v1
• Date: Wed, 14 Jul 2021 21:33:14 GMT
• Title: Do Humans Trust Advice More if it Comes from AI? An Analysis of Human-AI Interactions
• Authors: Kailas Vodrahalli, Tobias Gerstenberg, James Zou
• Abstract summary: We characterize how humans use AI suggestions relative to equivalent suggestions from a group of peer humans. We find that participants' beliefs about the human versus AI performance on a given task affects whether or not they heed the advice.
• Score: 8.785345834486057
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In many applications of AI, the algorithm's output is framed as a suggestion to a human user. The user may ignore the advice or take it into consideration to modify his/her decisions. With the increasing prevalence of such human-AI interactions, it is important to understand how users act (or do not act) upon AI advice, and how users regard advice differently if they believe the advice come from an "AI" versus another human. In this paper, we characterize how humans use AI suggestions relative to equivalent suggestions from a group of peer humans across several experimental settings. We find that participants' beliefs about the human versus AI performance on a given task affects whether or not they heed the advice. When participants decide to use the advice, they do so similarly for human and AI suggestions. These results provide insights into factors that affect human-AI interactions.

Related papers

• The Ethics of Advanced AI Assistants [53.89899371095332]
  This paper focuses on the opportunities and the ethical and societal risks posed by advanced AI assistants. We define advanced AI assistants as artificial agents with natural language interfaces, whose function is to plan and execute sequences of actions on behalf of a user. We consider the deployment of advanced assistants at a societal scale, focusing on cooperation, equity and access, misinformation, economic impact, the environment and how best to evaluate advanced AI assistants.
  arXiv  Detail & Related papers  (2024-04-24T23:18:46Z)
• Towards Human-AI Deliberation: Design and Evaluation of LLM-Empowered Deliberative AI for AI-Assisted Decision-Making [47.33241893184721]
  In AI-assisted decision-making, humans often passively review AI's suggestion and decide whether to accept or reject it as a whole. We propose Human-AI Deliberation, a novel framework to promote human reflection and discussion on conflicting human-AI opinions in decision-making. Based on theories in human deliberation, this framework engages humans and AI in dimension-level opinion elicitation, deliberative discussion, and decision updates.
  arXiv  Detail & Related papers  (2024-03-25T14:34:06Z)
• Beyond Recommender: An Exploratory Study of the Effects of Different AI Roles in AI-Assisted Decision Making [48.179458030691286]
  We examine three AI roles: Recommender, Analyzer, and Devil's Advocate. Our results show each role's distinct strengths and limitations in task performance, reliance appropriateness, and user experience. These insights offer valuable implications for designing AI assistants with adaptive functional roles according to different situations.
  arXiv  Detail & Related papers  (2024-03-04T07:32:28Z)
• Improving Human-AI Collaboration With Descriptions of AI Behavior [14.904401331154062]
  People work with AI systems to improve their decision making, but often under- or over-rely on AI predictions and perform worse than they would have unassisted. To help people appropriately rely on AI aids, we propose showing them behavior descriptions.
  arXiv  Detail & Related papers  (2023-01-06T00:33:08Z)
• Should I Follow AI-based Advice? Measuring Appropriate Reliance in Human-AI Decision-Making [0.0]
  We aim to enable humans not to rely on AI advice blindly but rather to distinguish its quality and act upon it to make better decisions. Current research lacks a metric for appropriate reliance (AR) on AI advice on a case-by-case basis. We propose to view AR as a two-dimensional construct that measures the ability to discriminate advice quality and behave accordingly.
  arXiv  Detail & Related papers  (2022-04-14T12:18:51Z)
• The Response Shift Paradigm to Quantify Human Trust in AI Recommendations [6.652641137999891]
  Explainability, interpretability and how much they affect human trust in AI systems are ultimately problems of human cognition as much as machine learning. We developed and validated a general purpose Human-AI interaction paradigm which quantifies the impact of AI recommendations on human decisions. Our proof-of-principle paradigm allows one to quantitatively compare the rapidly growing set of XAI/IAI approaches in terms of their effect on the end-user.
  arXiv  Detail & Related papers  (2022-02-16T22:02:09Z)
• Uncalibrated Models Can Improve Human-AI Collaboration [10.106324182884068]
  We show that presenting AI models as more confident than they actually are can improve human-AI performance. We first learn a model for how humans incorporate AI advice using data from thousands of human interactions.
  arXiv  Detail & Related papers  (2022-02-12T04:51:00Z)
• Cybertrust: From Explainable to Actionable and Interpretable AI (AI2) [58.981120701284816]
  Actionable and Interpretable AI (AI2) will incorporate explicit quantifications and visualizations of user confidence in AI recommendations. It will allow examining and testing of AI system predictions to establish a basis for trust in the systems' decision making.
  arXiv  Detail & Related papers  (2022-01-26T18:53:09Z)
• The Who in XAI: How AI Background Shapes Perceptions of AI Explanations [61.49776160925216]
  We conduct a mixed-methods study of how two different groups--people with and without AI background--perceive different types of AI explanations. We find that (1) both groups showed unwarranted faith in numbers for different reasons and (2) each group found value in different explanations beyond their intended design.
  arXiv  Detail & Related papers  (2021-07-28T17:32:04Z)
• Trustworthy AI: A Computational Perspective [54.80482955088197]
  We focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Non-discrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability & Auditability, and (vi) Environmental Well-Being. For each dimension, we review the recent related technologies according to a taxonomy and summarize their applications in real-world systems.
  arXiv  Detail & Related papers  (2021-07-12T14:21:46Z)
• The corruptive force of AI-generated advice [0.0]
  We test whether AI-generated advice can corrupt people. We also test whether transparency about AI presence mitigates potential harm. Results reveal that AI's corrupting force is as strong as humans'
  arXiv  Detail & Related papers  (2021-02-15T13:15:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.