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Cited by 206Human-Automation Interaction and SafetyExplainable Artificial Intelligence (XAI)Usability and User Interface Design

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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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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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  1. Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner. 2016. Machine bias. ProPublica. See https://www. propublica. org/article/machine-bias-risk-assessments-in-criminal-sentencing (2016).
  2. Suresh Kumar Annappindi. 2014. System and method for predicting consumer credit risk using income risk based credit score. US Patent 8,799,150.
  3. Lee J Cronbach and Paul E Meehl. 1955. Construct validity in psychological tests.Psychological bulletin 52, 4 (1955), 281.
  4. Berkeley J Dietvorst, Joseph P Simmons, and Cade Massey. 2015. Algorithm aversion: People erroneously avoid algorithms after seeing them err.Journal of Experimental Psychology: General 144, 1 (2015), 114.
  5. Steven E Dilsizian and Eliot L Siegel. 2014. Artificial intelligence in medicine and cardiac imaging: harnessing big data and advanced computing to provide personalized medical diagnosis and treatment. Current cardiology reports 16, 1 (2014), 441.
  6. Julia Dressel and Hany Farid. 2018. The accuracy, fairness, and limits of predicting recidivism. Science advances 4, 1 (2018), eaao5580.
  7. Ben Green. 2021. The Flaws of Policies Requiring Human Oversight of Government Algorithms. Available at SSRN (2021).
  8. 10.2139/ssrn.3465622 · DOI · Paper page
  9. Charles H Lawshe. 1975. A quantitative approach to content validity. Personnel psychology 28, 4 (1975), 563–575.
  10. John D Lee and Katrina A See. 2004. Trust in automation: Designing for appropriate reliance. Human factors 46, 1 (2004), 50–80.
  11. Piyawat Lertvittayakumjorn and Francesca Toni. 2021. Explanation-Based Human Debugging of NLP Models: A Survey. arXiv preprint arXiv:2104.15135 (2021). · arXiv
  12. Han Liu, Vivian Lai, and Chenhao Tan. 2021. Understanding the Effect of Out-of-distribution Examples and Interactive Explanations on Human-AI Decision Making. arXiv preprint arXiv:2101.05303 (2021). · arXiv
  13. Mahsan Nourani, Chiradeep Roy, Jeremy E Block, Donald R Honeycutt, Tahrima Rahman, Eric Ragan, and Vibhav Gogate. 2021. Anchoring Bias Affects Mental Model Formation and User Reliance in Explainable AI Systems. In 26th International Conference on Intelligent User Interfaces. 340–350.
  14. Forough Poursabzi-Sangdeh, Daniel G Goldstein, Jake M Hofman, Jennifer Wortman Vaughan, and Hanna Wallach. 2018. Manipulating and measuring model interpretability. arXiv preprint arXiv:1802.07810 (2018). · arXiv
  15. Peter Schmidt and Ann D Witte. 1988. Predicting recidivism in north carolina, 1978 and 1980. Inter-university Consortium for Political and Social Research.
  16. Yunfeng Zhang, Q Vera Liao, and Rachel KE Bellamy. 2020. Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making. arXiv preprint arXiv:2001.02114 (2020). · arXiv

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Metadata

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
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
FWCI
101.59
Type
conference paper
Language
en
Open access
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 › Human Factors and Ergonomics › 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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