To Predict and Serve?
Open accessAbstract
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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If an AI system is trained on a biased sample, it will learn that people with an immigrant background are more likely to commit crime.
“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
The “Bias in police-recorded data” section says algorithms reproduce biases in their training data and that police records may over-represent certain ethnic groups. It does not identify people with an immigrant background or say the system would learn that they are more likely to commit crime; the paper distinguishes predictions based on police records from predictions of crime itself. (LLM, conf=0.87)
Experimental — accuracy not yet measuredLum and Isaac showed that PredPol 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.
“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)
In “How do we estimate the number of drug users?”, the authors report that their application of the PredPol algorithm to Oakland police records reinforced existing biases and would target Black people at roughly twice the rate of white people, despite roughly equivalent estimated drug use. The claim omits that this was a modeled outcome in Oakland, not evidence that PredPol actually directed policing at those rates. (LLM, conf=0.86)
Experimental — accuracy not yet measuredInteractions 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.
“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
In “Bias in police-recorded data,” the paper describes a feedback loop in which police act on biased predictions, generate new records through targeted patrols, and feed those records back into the model, compounding the bias. This supports the claim about harmful compounding in a human–machine decision loop, but the paper does not support the “for better” possibility or establish the claim across learning and decision-making generally. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe effects of a biased sample could be amplified by AI predictions, causing a feedback loop in policing based on crime statistics.
“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
If biased data is used to train predictive models, the models will reproduce those same biases.
“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
If police focus attention on certain ethnic groups and certain neighbourhoods, police records will systematically over-represent those groups and neighbourhoods.
“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 sampling procedure can also be biased.
“The sampling procedure can also be biased.”
From Discrimination, artificial intelligence, and algorithmic decision-making (2025) · cites this paper as Lum and Isaac 2016
A predictive-policing application learns from past data that arrests have occurred frequently in certain areas, proposes patrols there, leading to more arrests, which feeds back into learning.
“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]
Lum and Isaac further showed via simulation that if police are more likely to find crime in areas where they are actively policing, which affects crime rates in the data used by the algorithm, then the algorithm will become further biased in a feedback loop that amplifies existing biases.
“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)
Historical over-policing of some communities has unavoidable impacts on the sampling of the data used to train models for predictive policing.
“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]
When discrimination affects arrest rates, it becomes embedded in prosecution data.
“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
Similar audits have turned up issues of bias reinforcement in systems that guide police to portions of a city to patrol, and in e-commerce platforms.
“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]
Arrests are often used as a proxy for crime in machine learning applications to policing and criminal justice, even though arrests are a racially biased representation of the true incidence of crime.
“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]
By using arrests as a proxy for crimes committed, COMPAS can only exacerbate racist policing practices, rather than transcending them.
“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]
Most empirical work on social justice has been conducted in the US and Western Europe, grounding understandings of justice in the Western cultural context.
“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]
More collectivist and hierarchical Eastern societies differ from more individualistic and egalitarian Western cultures in how distributive, procedural, and retributive justice are conceptualised and achieved.
“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]
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.
“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]
Research has revealed that algorithms increase bias in policing.
“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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- 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
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Political Science and International Relations › 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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