ArticleThe quarterly journal of economics2018
HUMAN DECISIONS AND MACHINE PREDICTIONS.
Article in The quarterly journal of economics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers, 2 of them syntheses that pooled it.
What it found
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Who cites it
72 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Language-based game theory in the age of artificial intelligence.Journal of the Royal Society, Interface · 2024Pooled it
- A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning.Sensors (Basel, Switzerland) · 2021Pooled it
- Measuring disparate impact in human and machine decisions.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Measuring complex constructs in large-scale text with computational social mixed methods.Behavior research methods · 2026Article
- An Assessment of Racial Disparities in Pretrial Decision-Making Using Misclassification Models.Journal of empirical legal studies · 2026Article
- Toward a science of human-AI teaming for decision making: A complementarity framework.PNAS nexus · 2026Article
- Article
- How public involvement can improve the science of AI.Proceedings of the National Academy of Sciences of the United States of America · 2025Review
- Algorithmic decision-making in health care: Evidence from post-acute care in Medicare Advantage.Journal of health economics · 2025Article
- Does AI help humans make better decisions? A statistical evaluation framework for experimental and observational studies.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Research on sentiment index and real estate demand forecasting based on BERT-BiLSTM and ADL-MIDAS models.Scientific reports · 2025Article
- Lay beliefs about AI assessment of interpersonal skills in personnel selection.Scientific reports · 2025Article
- A publicly available benchmark for assessing large language models' ability to predict how humans balance self-interest and the interest of others.Scientific reports · 2025Article
- Building Nondiscriminatory Algorithms in Selected Data.American economic review. Insights · 2025Article
- Me vs. the machine? Subjective evaluations of human- and AI-generated advice.Scientific reports · 2025Article
- On prediction-modelers and decision-makers: why fairness requires more than a fair prediction model.AI & society · 2025Article
- Breaking the gatekeepers: how AI will revolutionize scientific funding.Frontiers in artificial intelligence · 2025Article
- AI and digital justice in EU labor law. A comparative study on predictive tools and judicial transformation.Frontiers in artificial intelligence · 2025Article
- Social Preferences Toward Humans and Machines: A Systematic Experiment on the Role of Machine Payoffs.Perspectives on psychological science : a journal of the Association for Psychological Science · 2025Article
- Survival After Radical Cystectomy for Bladder Cancer: Development of a Fair Machine Learning Model.JMIR medical informatics · 2024Article
12 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Can machine learning improve human decision making? Bail decisions provide a good test case. Millions of times each year, judges make jail-or-release decisions that hinge on a prediction of what a defendant would do if released. The concreteness of the prediction task combined with the volume of data available makes this a promising machine-learning application. Yet comparing the algorithm to judges proves complicated. First, the available data are generated by prior judge decisions. We only observe crime outcomes for released defendants, not for those judges detained. This makes it hard to evaluate counterfactual decision rules based on algorithmic predictions. Second, judges may have a broader set of preferences than the variable the algorithm predicts; for instance, judges may care specifically about violent crimes or about racial inequities. We deal with these problems using different econometric strategies, such as quasi-random assignment of cases to judges. Even accounting for these concerns, our results suggest potentially large welfare gains: one policy simulation shows crime reductions up to 24.7% with no change in jailing rates, or jailing rate reductions up to 41.9% with no increase in crime rates. Moreover, all categories of crime, including violent crimes, show reductions; and these gains can be achieved while simultaneously reducing racial disparities. These results suggest that while machine learning can be valuable, realizing this value requires integrating these tools into an economic framework: being clear about the link between predictions and decisions; specifying the scope of payoff functions; and constructing unbiased decision counterfactuals.
Identifiers
What OpenQuestion holds
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.