arXiv 2 May 2024 · cs.HC · 4 citations (OpenAlex)
arXiv:2405.01484 · PDF · DOI · OpenAlex · Extracted main text
Algorithms frequently assist, rather than replace, human decision-makers. However, the design and analysis of algorithms often focus on predicting outcomes and do not explicitly model their effect on human decisions. This discrepancy between the design and role of algorithmic assistants becomes particularly concerning in light of empirical evidence that suggests that algorithmic assistants again and again fail to improve human decisions. In this article, we formalize the design of recommendation algorithms that assist human decision-makers without making restrictive ex-ante assumptions about how recommendations affect decisions. We formulate an algorithmic-design problem that leverages the potential-outcomes framework from causal inference to model the effect of recommendations on a human decision-maker's binary treatment choice. Within this model, we introduce a monotonicity assumption that leads to an intuitive classification of human responses to the algorithm. Under this assumption, we can express the human's response to algorithmic recommendations in terms of their compliance with the algorithm and the active decision they would take if the algorithm sends no recommendation. We showcase the utility of our framework using an online experiment that simulates a hiring task. We argue that our approach can make sense of the relative performance of different recommendation algorithms in the experiment and can help design solutions that realize human-AI complementarity. Finally, we leverage our approach to derive minimax optimal recommendation algorithms that can be implemented with machine learning using limited training data.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Angrist, Joshua D., Guido W. Imbens, and Donald B. Rubin (1996) Identification of Causal Effects Using Instrumental Variables | 0.811 | 4 | 2 | 100% |
| 2 | McLaughlin, Bryce and Jann Spiess (2022) Algorithmic Assistance with Recommendation-Dependent Preferences self | 0.737 | 3 | 2 | 100% |
| 3 | Athey, Susan C., Kevin A. Bryan, and Joshua S. Gans (2020) The Allocation of Decision Authority to Human and Artificial Intelligence | 0.644 | 2 | 2 | 100% |
| 4 | Bansal, Gagan, Besmira Nushi, Ece Kamar, Walter S. Lasecki, Daniel S… (2019) Beyond Accuracy: The Role of Mental Models in Human-AI Team Performance | 0.644 | 2 | 2 | 100% |
| 5 | Imbens, Guido W. and Joshua D. Angrist (1994) Identification and Estimation of Local Average Treatment Effects | 0.644 | 2 | 2 | 100% |
| 6 | Kamenica, Emir and Matthew Gentzkow (2011) Bayesian Persuasion | 0.644 | 2 | 2 | 100% |
| 7 | Li, Lan, Tina Lassiter, Joohee Oh, and Min Kyung Lee (2021) Algorithmic Hiring in Practice: Recruiter and HR Professional's Perspectives on AI Use in Hiring | 0.644 | 2 | 2 | 100% |
| 8 | Noti, Gali and Yiling Chen (2023) Learning When to Advise Human Decision Makers | 0.644 | 2 | 2 | 100% |
| 9 | Raghavan, Manish, Solon Barocas, Jon Kleinberg, and Karen Levy (2020) Mitigating bias in algorithmic hiring: evaluating claims and practices | 0.644 | 2 | 2 | 100% |
| 10 | Raghu, Maithra, Katy Blumer, Greg Corrado, Jon Kleinberg, Ziad Oberm… (2019) The Algorithmic Automation Problem: Prediction, Triage, and Human Effort | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 44 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Algorithmic Feature Highlighting for Human–AI Decision-Making | 0.405 | 1 | 1 |