ArticlePerspectives on psychological science : a journal of the Association for Psychological Science2024
Three Challenges for AI-Assisted Decision-Making.
Article in Perspectives on psychological science : a journal of the Association for Psychological Science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Human learning is an understudied but promising lever for boosting human-AI synergy.Proceedings of the National Academy of Sciences of the United States of America · 2026Pooled it
- Effects of AI Assistance Timing on Pharmacists' Trust in Automated Pill Recognition Technology: Within-Participants Experimental Study.JMIR human factors · 2026Article
- Therapist Skepticism and Adaptation to Teletherapy Under Enforced Uptake, With Theoretical Considerations for AI-Mediated Care: Qualitative Study.JMIR formative research · 2026Article
- Mapping Human-AI Teaming in Risk Analysis: Role Evolution, Thematic Landscape, and Governance Mechanisms From News Media.Risk analysis : an official publication of the Society for Risk Analysis · 2026Article
- AI Chatbot Anthropomorphism and Consumer Decision-Making: A Dual-Pathway Calibration Model.Behavioral sciences (Basel, Switzerland) · 2026Article
- Benchmarking Large Language Model Rationality Using Measurement Axioms.Research square · 2026Article
- Artificial intelligence and medical futility.BMC medical informatics and decision making · 2026Review
- Adaptive yet suboptimal integration of advice in decision-making.Communications psychology · 2026Article
- Driving sustainable innovation outcomes through employee AI collaboration with the mediating role of sustainable career capacities.Scientific reports · 2026Article
- Human-AI collaboration: trade-offs between performance and preferences.Cognitive research: principles and implications · 2026Article
- AI-augmented decision-making in face matching: comparing concurrent and non-concurrent advice presentation.Cognitive research: principles and implications · 2026Article
- Cognitive models facilitate real-time inference of latent motives.Scientific reports · 2026Article
- Application of the artificial intelligence-assisted World Café teaching model in clinical pharmacology graduate course: a pilot study.Frontiers in public health · 2026Article
- Too much, too little, or just right? The inverted-U relationship between human-AI collaboration modes and eudaimonic well-being.Frontiers in psychology · 2026Article
- Caring over computing: an ethical and sociotechnical perspective on digital technologies for social connectedness in dementia care.Frontiers in digital health · 2026Article
- Human analysts or artificial intelligence? Source attribution in athlete performance evaluation.Frontiers in psychology · 2026Article
- Asymmetric uncertainty regulation in human-artificial intelligence interaction.Frontiers in artificial intelligence · 2026Article
- Measuring human metacognition in GPT-assisted cognition: development and psychometric validation of a novel scale.Frontiers in psychology · 2026Article
- Addressing Autonomy Risks in Generative Chatbots with the Socratic Method.Science and engineering ethics · 2025Article
- Human-AI collectives most accurately diagnose clinical vignettes.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.