skip to main content

Explore Research Products in the NSF-PAR

Title: Three Challenges for AI-Assisted Decision-Making
Artificial intelligence (AI) has the potential to improve human decision-making by providing decision recommendations and problem-relevant information to assist human decision-makers. However, the full realization of the potential of human–AI collaboration continues to face several challenges. First, the conditions that support complementarity (i.e., situations in which the performance of a human with AI assistance exceeds the performance of an unassisted human or the AI in isolation) must be understood. This task requires humans to be able to recognize situations in which the AI should be leveraged and to develop new AI systems that can learn to complement the human decision-maker. Second, human mental models of the AI, which contain both expectations of the AI and reliance strategies, must be accurately assessed. Third, the effects of different design choices for human-AI interaction must be understood, including both the timing of AI assistance and the amount of model information that should be presented to the human decision-maker to avoid cognitive overload and ineffective reliance strategies. In response to each of these three challenges, we present an interdisciplinary perspective based on recent empirical and theoretical findings and discuss new research directions.  more » « less
Award ID(s):
1900644
NSF-PAR ID:
10461936
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Perspectives on Psychological Science
ISSN:
1745-6916
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ( , Proceedings of the ACM on Human-Computer Interaction)

    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.

     
    more » « less
  2. ; ; ; ( , Computational Brain & Behavior)
    Abstract

    AI assistance is readily available to humans in a variety of decision-making applications. In order to fully understand the efficacy of such joint decision-making, it is important to first understand the human’s reliance on AI. However, there is a disconnect between how joint decision-making is studied and how it is practiced in the real world. More often than not, researchers ask humans to provide independent decisions before they are shown AI assistance. This is done to make explicit the influence of AI assistance on the human’s decision. We develop a cognitive model that allows us to infer thelatentreliance strategy of humans on AI assistance without asking the human to make an independent decision. We validate the model’s predictions through two behavioral experiments. The first experiment follows aconcurrentparadigm where humans are shown AI assistance alongside the decision problem. The second experiment follows asequentialparadigm where humans provide an independent judgment on a decision problem before AI assistance is made available. The model’s predicted reliance strategies closely track the strategies employed by humans in the two experimental paradigms. Our model provides a principled way to infer reliance on AI-assistance and may be used to expand the scope of investigation on human-AI collaboration.

     
    more » « less
  3. ; ( , Proceedings of the ACM on Human-Computer Interaction)
    Proper calibration of human reliance on AI is fundamental to achieving complementary performance in AI-assisted human decision-making. Most previous works focused on assessing user reliance, and more broadly trust, retrospectively, through user perceptions and task-based measures. In this work, we explore the relationship between eye gaze and reliance under varying task difficulties and AI performance levels in a spatial reasoning task. Our results show a strong positive correlation between percent gaze duration on the AI suggestion and user AI task agreement, as well as user perceived reliance. Moreover, user agency is preserved particularly when the task is easy and when AI performance is low or inconsistent. Our results also reveal nuanced differences between reliance and trust. We discuss the potential of using eye gaze to gauge human reliance on AI in real-time, enabling adaptive AI assistance for optimal human-AI team performance. 
    more » « less
  4. ; ; ( , Proceedings of the AAAI Conference on Artificial Intelligence)
    The increased integration of artificial intelligence (AI) technologies in human workflows has resulted in a new paradigm of AI-assisted decision making,in which an AI model provides decision recommendations while humans make the final decisions. To best support humans in decision making, it is critical to obtain a quantitative understanding of how humans interact with and rely on AI. Previous studies often model humans' reliance on AI as an analytical process, i.e., reliance decisions are made based on cost-benefit analysis. However, theoretical models in psychology suggest that the reliance decisions can often be driven by emotions like humans' trust in AI models. In this paper, we propose a hidden Markov model to capture the affective process underlying the human-AI interaction in AI-assisted decision making, by characterizing how decision makers adjust their trust in AI over time and make reliance decisions based on their trust. Evaluations on real human behavior data collected from human-subject experiments show that the proposed model outperforms various baselines in accurately predicting humans' reliance behavior in AI-assisted decision making. Based on the proposed model, we further provide insights into how humans' trust and reliance dynamics in AI-assisted decision making is influenced by contextual factors like decision stakes and their interaction experiences. 
    more » « less
  5. ; ( , Proceedings of the Hawaii International Conference on System Sciences)
    Whereas artificial intelligence (AI) is increasingly used to facilitate team decision-making, little is known about how the timing of AI assistance may impact team performance. The study investigates this question with an online experiment in which teams completed a new product development task with assistance from a chatbot. Information needed for making the decision was distributed among the team members. The chatbot shared information critical to the decision in either the first half or second half of team interaction. The results suggest that teams assisted by the chatbot in the first half of the decision-making task made better decisions than those assisted by the chatbot in the second half. Analysis of team member perceptions and interaction processes suggests that having a chatbot at the beginning of team interaction may have generated a ripple effect in the team that promoted information sharing among team members. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email