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A growing literature on human-AI decision-making investigates strategies for combining human judgment with statistical models to improve decision-making.
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Predict responsibly: improving fairness and accuracy by learning to defer
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Investigating human+ machine complementarity for recidivism predictions
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Fairness and Machine Learning: Limitations and Opportunities
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Emily Denton, Mark Díaz, Ian Kivlichan, Vinodkumar Prabhakaran, and Rachel Rosen. 2021 · 2021
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The impact of algorithmic risk assessments on human predictions and its analysis via crowdsourcing studies
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Human-AI collaboration with bandit feedback
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The principles and limits of algorithm-in-the-loop decision making
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Allocating interventions based on predicted outcomes: A case study on homelessness services. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 33. 622–629
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A machine learning approach to low-value health care: wasted tests, missed heart attacks and mis-predictions
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Dissecting racial bias in an algorithm used to manage the health of populations
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A slow algorithm improves users’ assessments of the algorithm’s accuracy
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Algorithmic risk assessments can alter human decision-making processes in high-stakes government contexts
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Learning representations by humans, for humans. In International Conference on Machine Learning . PMLR, 4227–4238
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Measurement and fairness. In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency . 375–385
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Towards a Science of Human-AI Decision Making: A Survey of Empirical Studies
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Understanding the effect of out-of-distribution examples and interactive explanations on human-ai decision making
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From optimizing engagement to measuring value. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency . 714–722
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Confident learning: Estimating uncertainty in dataset labels
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Resources and benchmark corpora for hate speech detection: a systematic review
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Manipulating and measuring model interpretability. In Proceedings of the 2021 CHI conference on human factors in computing systems . 1–52
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Toward Improving Student Model Estimates through Assistance Scores in Principle and in Practice
Napol Rachatasumrit and Kenneth R Koedinger. 2021 · 2021
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Fairness violations and mitigation under covariate shift. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency . 3–13
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Are explanations helpful? a comparative study of the effects of explanations in ai-assisted decision-making. In 26th International Conference on Intelligent User Interfaces . 318–328
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Racial Disparities in the Enforcement of Marijuana Violations in the US. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society . 130–143
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How Child Welfare Workers Reduce Racial Disparities in Algorithmic Decisions. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (forthcoming)
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Human-Algorithm Collaboration: Achieving Complementarity and Avoiding Unfairness
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Backward baselines: Is your model predicting the past?
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On the Effect of Information Asymmetry in Human-AI Teams
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Megan T Stevenson and Jennifer L Doleac. 2022 · 2022
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Building Human Values into Recommender Systems: An Interdisciplinary Synthesis
Jonathan Stray, Alon Halevy, Parisa Assar, Dylan Hadfield-Menell, Craig Boutilier, Amar Ashar, Lex Beattie, Michael Ekstrand, Claire Leibowicz, Connie Moon Sehat, et al · 2022
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Against Predictive Optimization: On the Legitimacy of Decision-Making Algorithms that Optimize Predictive Accuracy
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SoK: A Validity Perspective on Evaluating the Justified Use of Data-driven Decision-making Algorithms
Amanda Lee Coston, Anna Kawakami, Haiyi Zhu, Ken Holstein, and Hoda Heidari. 2023 · 2023
Closest in time.
Toward supporting perceptual complementarity in human-AI collaboration via reflection on unobservables
Kenneth Holstein, Maria De-Arteaga, Lakshmi Tumati, and Yanghuidi Cheng. 2023 · 2023
Closest in time.