Towards a Science of Human-AI Decision Making: An Overview of Design Space in Empirical Human-Subject Studies

Lai, Vivian; Chen, Chacha; Smith-Renner, Alison; Liao, Q. Vera; Tan, Chenhao

doi:10.1145/3593013.3594087

Cited by 205Human-Automation Interaction and SafetyExplainable Artificial Intelligence (XAI)Usability and User Interface Design

Towards a Science of Human-AI Decision Making: An Overview of Design Space in Empirical Human-Subject Studies

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Lai, Vivian, Chen, Chacha, Smith-Renner, Alison, Liao, Q. Vera, Tan, Chenhao·2023·2023 ACM Conference on Fairness Accountability and Transparency

DOI 10.1145/3593013.3594087

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Abstract

AI systems are adopted in numerous domains due to their increasingly strong predictive performance. However, in high-stakes domains such as criminal justice and healthcare, full automation is often not desirable due to safety, ethical, and legal concerns, yet fully manual approaches can be inaccurate and time-consuming. As a result, there is growing interest in the research community to augment human decision making with AI assistance. Besides developing AI technologies for this purpose, the emerging field of human-AI decision making must embrace empirical approaches to form a foundational understanding of how humans interact and work with AI to make decisions. To invite and help structure research efforts towards a science of understanding and improving human-AI decision making, we survey recent literature of empirical human-subject studies on this topic. We summarize the study design choices made in over 100 papers in three important aspects: (1) decision tasks, (2) AI assistance elements, and (3) evaluation metrics. For each aspect, we summarize current trends, discuss gaps in current practices of the field, and make a list of recommendations for future research. Our work highlights the need to develop common frameworks to account for the design and research spaces of human-AI decision making, so that researchers can make rigorous choices in study design, and the research community can build on each other’s work and produce generalizable scientific knowledge. We also hope this work will serve as a bridge for HCI and AI communities to work together to mutually shape the empirical science and computational technologies for human-AI decision making.

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There is limited understanding around how task characteristics of complexity and uncertainty shape human decision-making and human-AI team performance.
“there is a limited understanding around how the important task characteristics of complexity and uncertainty shape human decision-making and human-AI team performance.”
From Dealing with Uncertainty: Understanding the Impact of Prognostic Versus Diagnostic Tasks on Trust and Reliance in Human-AI Decision Making (2024) · cites this paper as [68]
The Task characteristics section says task properties may affect reliance on AI and that studies often fail to justify task choices or establish how findings generalize. But it does not identify task complexity and task uncertainty as the two characteristics whose effects on human decision-making and human-AI team performance are poorly understood. Its discussion of uncertainty in Model predictions concerns AI prediction estimates, not task uncertainty. (LLM, conf=0.84)
Experimental — accuracy not yet measured
Task performance, measured as correctness of participants' answers and time on task, has been studied in prior work on AI-assisted decision making and specifically for studying the effect of uncertainty expression.
“Both of these have been studied in prior work on AI-assisted decision making [54], as well as specifically for studying the effect of uncertainty expression [102].”
From "I'm Not Sure, But...": Examining the Impact of Large Language Models' Uncertainty Expression on User Reliance and Trust (2024) · cites this paper as [54]

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Authors: Lai, V., Chen, C., Smith-Renner, A., Liao, Q. V., & Tan, C.
Affiliations: University of Colorado Boulder (US)
University of Chicago (US)
Dataminr Inc., USA
Microsoft (Canada) (CA)
ORCID: Lai, Vivian
Chen, Chacha
Smith-Renner, Alison
Liao, Q. Vera
Tan, Chenhao
Venue: 2023 ACM Conference on Fairness Accountability and Transparency
Publisher: ACM
Published: 2023-06-12
Pages: 1369-1385
DOI: 10.1145/3593013.3594087
OpenAlex: W4380365974
Cited by: 204
References: 77
Citations by year: 2026: 83 · 2025: 71 · 2024: 45 · 2023: 4 · 2022: 1
FWCI: 101.59
Type: conference paper
Language: en
Open access: bronze
doi.org
Licence: www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations: ACM Conference on Fairness, Accountability, and Transparency (FAccT) (publishedVersion)
Funders: National Science Foundation (2126602, 2040989)
Topics: Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
Keywords: human-AI decision-making, AI assistance, research design, decision tasks, evaluation metrics, human-AI interaction, human-in-the-loop

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Towards a Science of Human-AI Decision Making: An Overview of Design Space in Empirical Human-Subject Studies

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