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To Predict and Serve?
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Predictive policing systems are used increasingly by law enforcement to try to prevent crime before it occurs. But what happens when these systems are trained using biased data? Kristian Lum and William Isaac consider the evidence – and the social consequences
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“by using arrests as a proxy for crimes committed, COMPAS can only exacerbate racist policing practices, rather than transcending them [7,13,23,37,39].”
From Measurement and Fairness (2021) · cites this paper as [37]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“If an AI system is trained on such a biased sample, it will learn that people with an immigrant background are more likely to commit crime.”
From Discrimination, artificial intelligence, and algorithmic decision-making (2025) · cites this paper as Lum and Isaac 2016
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Lum and Isaac (2016) showed that PredPol, a popular predictive policing software, reinforced existing biases in the dataset and targeted Black people at disproportionately high rates (twice as high as white people, even though drug use was roughly equal between the two groups)”
From Equity and Artificial Intelligence in Education: Will "AIEd" Amplify or Alleviate Inequities in Education? (2021) · cites this paper as Lum and Isaac (2016)
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Such interactions between human and machine biases may have compounding effects - for better or worse - in the presence of human-AI feedback loops in learning or decision-making”
From Equity and Artificial Intelligence in Education: Will "AIEd" Amplify or Alleviate Inequities in Education? (2021) · cites this paper as Lum and Isaac, 2016
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“The effects of such a biased sample could even be amplified by AI predictions.”
From Discrimination, artificial intelligence, and algorithmic decision-making (2025) · cites this paper as Lum and Isaac 2016
The cited source says what the claim attributes to it.
In the citing paper
“if biased data is used to train these predictive models, the models will reproduce (…) those same biases.”
From Discrimination, artificial intelligence, and algorithmic decision-making (2025) · cites this paper as Lum and Isaac 2016
The cited source says what the claim attributes to it.
In the citing paper
“If police focus attention on certain ethnic groups and certain neighbourhoods, it is likely that police records will systematically over-represent those groups and neighbourhoods.”
From Discrimination, artificial intelligence, and algorithmic decision-making (2025) · cites this paper as Lum and Isaac 2016
The cited source says what the claim attributes to it.
In the citing paper
“The sampling procedure can also be biased.”
From Discrimination, artificial intelligence, and algorithmic decision-making (2025) · cites this paper as Lum and Isaac 2016
The cited source says what the claim attributes to it.
In the citing paper
“A predictive-policing application learns from past data that arrests have occurred frequently in certain areas, and it proposes that police patrol these areas preferentially. This leads to more arrests in these areas, which in turn feeds the learning to propose to patrol them, etc.”
From AI for the Common Good?! Pitfalls, challenges, and ethics pen-testing (2019) · cites this paper as [39]
The cited source says what the claim attributes to it.
In the citing paper
“they further showed via simulation that if police are more likely to find crime in areas where they are actively policing (as a result of PredPol's predictions), which in turn affects the crime rates in the data used by the algorithm, then the algorithm will become further biased in a”
From Equity and Artificial Intelligence in Education: Will "AIEd" Amplify or Alleviate Inequities in Education? (2021) · cites this paper as Lum and Isaac (2016)
The cited source says what the claim attributes to it.
In the citing paper
“Similarly, historical over-policing of some communities has unavoidable impacts on the sampling of the data used to train models for predictive policing [32,79].”
From Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty (2021) · cites this paper as [79]
In the citing paper
“When discrimination affects arrest rates, it becomes embedded in prosecution data (Lum and Isaac 2016).”
From How to Design AI for Social Good: Seven Essential Factors (2020) · cites this paper as Lum and Isaac 2016
In the citing paper
“Similar audits have turned up issues of bias reinforcement in systems that guide police to portions of a city to patrol [36], and in e-commerce platforms [35].”
From The fallacy of inscrutability (2018) · cites this paper as [36]
In the citing paper
“For example, arrests are often used as a proxy for crime in applications of machine learning to policing and criminal justice, even though arrests are a racially biased representation of the true incidence of crime [28].”
From Problem Formulation and Fairness (2019) · cites this paper as [28]
In the citing paper
“While the concern for justice has a long history in the West (e.g., Aristotle, Rawls) and the East (e.g., Confucius, Chanakya), they show that the majority of empirical work on social justice has been situated in the US and Western Europe, grounding the understanding of justice in the Western cultural context.”
From Re-imagining Algorithmic Fairness in India and Beyond (2021) · cites this paper as [120]
In the citing paper
“Summarising decades worth of research, [120] says that the more collectivist and hierarchical societies in the East differs from the more individualistic and egalitarian cultures of the West in how different forms of justicedistributive, procedural, and retributive-are conceptualised and achieved.”
From Re-imagining Algorithmic Fairness in India and Beyond (2021) · cites this paper as [120]
In the citing paper
“Algorithms that aim to facilitate hiring, lending, healthcare, policing, and criminal justice may provide an illusion of objectivity while reinforcing human biases and creating feedback loops that further exacerbate injustice and inequality”
From Stewardship of global collective behavior (2021) · cites this paper as [124]
In the citing paper
“Despite the opacity, research has revealed how algorithms lead users to radical or age-inappropriate content (128, 164), exacerbate disparities in health (123), and increase bias in policing (124).”
From Stewardship of global collective behavior (2021) · cites this paper as [124]
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Metadata
- Authors
- Lum, K., & Isaac, W.
- Affiliations
- Kristian Lum, PhD is Lead statistician at the Human Rights Data Analysis Group
- Michigan State University (US)
- Venue
- Significance
- Publisher
- Oxford University Press (OUP)
- Published
- 2016-10-01
- Volume
- 13
- Issue
- 5
- Pages
- 14-19
- ISSN
- 1740-9705 (ISSN-L)
- 1740-9713
- OpenAlex
- W2529251251
- Cited by
- 611
- References
- 10
- Citations by year
- 2026: 131 · 2025: 92 · 2024: 40 · 2023: 40 · 2022: 45 · 2021: 87 · 2020: 63 · 2019: 68 · 2018: 31 · 2017: 14
- FWCI
- 92.81
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Significance (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Law › Artificial Intelligence in Law
- Social Sciences › Political Science and International Relations › Policing Practices and Perceptions
- Keywords
- predictive policing, algorithmic bias, biased training data, crime prediction, law enforcement, algorithmic fairness
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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