Does More Advice Help? The Effects of Second Opinions in AI-Assisted
Decision Making
- URL: http://arxiv.org/abs/2401.07058v1
- Date: Sat, 13 Jan 2024 12:19:01 GMT
- Title: Does More Advice Help? The Effects of Second Opinions in AI-Assisted
Decision Making
- Authors: Zhuoran Lu, Dakuo Wang, Ming Yin
- Abstract summary: We explore whether and how the provision of second opinions may affect decision-makers' behavior and performance in AI-assisted decision-making.
We find that if both the AI model's decision recommendation and a second opinion are always presented together, decision-makers reduce their over-reliance on AI.
If decision-makers have the control to decide when to solicit a peer's second opinion, we find that their active solicitations of second opinions have the potential to mitigate over-reliance on AI.
- Score: 45.20615051119694
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: AI assistance in decision-making has become popular, yet people's
inappropriate reliance on AI often leads to unsatisfactory human-AI
collaboration performance. In this paper, through three pre-registered,
randomized human subject experiments, we explore whether and how the provision
of {second opinions} may affect decision-makers' behavior and performance in
AI-assisted decision-making. We find that if both the AI model's decision
recommendation and a second opinion are always presented together,
decision-makers reduce their over-reliance on AI while increase their
under-reliance on AI, regardless whether the second opinion is generated by a
peer or another AI model. However, if decision-makers have the control to
decide when to solicit a peer's second opinion, we find that their active
solicitations of second opinions have the potential to mitigate over-reliance
on AI without inducing increased under-reliance in some cases. We conclude by
discussing the implications of our findings for promoting effective human-AI
collaborations in decision-making.
Related papers
- Raising the Stakes: Performance Pressure Improves AI-Assisted Decision Making [57.53469908423318]
We show the effects of performance pressure on AI advice reliance when laypeople complete a common AI-assisted task.
We find that when the stakes are high, people use AI advice more appropriately than when stakes are lower, regardless of the presence of an AI explanation.
arXiv Detail & Related papers (2024-10-21T22:39:52Z) - Combining AI Control Systems and Human Decision Support via Robustness and Criticality [53.10194953873209]
We extend a methodology for adversarial explanations (AE) to state-of-the-art reinforcement learning frameworks.
We show that the learned AI control system demonstrates robustness against adversarial tampering.
In a training / learning framework, this technology can improve both the AI's decisions and explanations through human interaction.
arXiv Detail & Related papers (2024-07-03T15:38:57Z) - 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) - AI Reliance and Decision Quality: Fundamentals, Interdependence, and the Effects of Interventions [6.356355538824237]
We argue that reliance and decision quality are often inappropriately conflated in the current literature on AI-assisted decision-making.
Our research highlights the importance of distinguishing between reliance behavior and decision quality in AI-assisted decision-making.
arXiv Detail & Related papers (2023-04-18T08:08:05Z) - Competent but Rigid: Identifying the Gap in Empowering AI to Participate
Equally in Group Decision-Making [25.913473823070863]
Existing research on human-AI collaborative decision-making focuses mainly on the interaction between AI and individual decision-makers.
This paper presents a wizard-of-oz study in which two participants and an AI form a committee to rank three English essays.
arXiv Detail & Related papers (2023-02-17T11:07:17Z) - Understanding the Role of Human Intuition on Reliance in Human-AI
Decision-Making with Explanations [44.01143305912054]
We study how decision-makers' intuition affects their use of AI predictions and explanations.
Our results identify three types of intuition involved in reasoning about AI predictions and explanations.
We use these pathways to explain why feature-based explanations did not improve participants' decision outcomes and increased their overreliance on AI.
arXiv Detail & Related papers (2023-01-18T01:33:50Z) - To Trust or to Think: Cognitive Forcing Functions Can Reduce
Overreliance on AI in AI-assisted Decision-making [4.877174544937129]
People supported by AI-powered decision support tools frequently overrely on the AI.
Adding explanations to the AI decisions does not appear to reduce the overreliance.
Our research suggests that human cognitive motivation moderates the effectiveness of explainable AI solutions.
arXiv Detail & Related papers (2021-02-19T00:38:53Z) - Artificial Artificial Intelligence: Measuring Influence of AI
'Assessments' on Moral Decision-Making [48.66982301902923]
We examined the effect of feedback from false AI on moral decision-making about donor kidney allocation.
We found some evidence that judgments about whether a patient should receive a kidney can be influenced by feedback about participants' own decision-making perceived to be given by AI.
arXiv Detail & Related papers (2020-01-13T14:15:18Z) - Effect of Confidence and Explanation on Accuracy and Trust Calibration
in AI-Assisted Decision Making [53.62514158534574]
We study whether features that reveal case-specific model information can calibrate trust and improve the joint performance of the human and AI.
We show that confidence score can help calibrate people's trust in an AI model, but trust calibration alone is not sufficient to improve AI-assisted decision making.
arXiv Detail & Related papers (2020-01-07T15:33:48Z)
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.