Academic Papers
Background reading. It's very much not exhaustive, so send us your favourites and we'll add them...
Ethics of Algorithmic Policing
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Asaro, P. M. (2019). AI Ethics in Predictive Policing: From Models of Threat to an Ethics of Care
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Babuta, A., Oswald,.M and Rinik, C. (2018) Machine Learning Algorithms and Police Decision-Making Legal, Ethical and Regulatory Challenges
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Babuta, A., & Oswald, M. (2019). Data analytics and algorithmic bias in policing
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Babuta, A., & Oswald, M. (2020). Data analytics and algorithms in policing in England and Wales: Towards a new policy framework
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Bland, M. (2020). Algorithms Can Predict Domestic Abuse, But Should We Let Them?
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Busuioc, M. (2021). Accountable artificial intelligence: Holding algorithms to account
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EUCPN – European Crime Prevention Network. (2022). Artificial intelligence and predictive policing: risks and challenges. Brussels: EUCPN
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Fantin, S., Emanuilov, I., Vogiatzoglou, P., & Marquenie, T. (2020). Purpose Limitation By Design As A Counter To Function Creep And System Insecurity In Police Artificial Intelligence (UNICRI Special Collection on AI in Criminal Justice)
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Janjeva, A., et al. (2023). The Rapid Rise of Generative AI: Assessing risks to safety and security
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Liberty (2019). Policing by Machine
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Oswald, M., Grace, J., Urwin, S., & Barnes, G. C. (2018). Algorithmic risk assessment policing models: lessons from the Durham HART model and ‘Experimental’ proportionality
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Powell, R., Oswald, M. (2024). Assurance of Third-Party AI Systems for UK National Security
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Palmiotto, F. (2021). The black box on trial: The impact of algorithmic opacity on fair trial rights in criminal proceedings.
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Vestby, A., Vestby, J. (2021). Machine Learning and the Police: Asking the Right Questions
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Zilka, M., et. al. (2023). Exploring Police Perspectives on Algorithmic Transparency: A Qualitative Analysis of Police Interviews in the
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More publications from related authors: Alexander Babuta, Marion Oswald
Machine Learning for Algorithmic Policing
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Babuta, A. (2017). Big Data and Policing: An Assessment of Law Enforcement Requirements, Expectations and Priorities
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Barros, T.S., Pires, C.E.S., Nascimento, D.C. (2023). Leveraging BERT for extractive text summarization on federal police documents
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Bland, M., Barak. A. (2020). Targeting Domestic Abuse with Police Data
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Choudelchova, A. (2017). Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
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Chouldechova, A., et al. (2018). A Case Study of Algorithm-Assisted Decision Making in Child Maltreatment Hotline Screening Decisions
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Cook, D., et al. (2023). Protecting Children from Online Exploitation: Can a trained model detect harmful communication strategies?
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Hiltz, N., Bland, M., Barnes, G.C. (2020). Victim-Offender Overlap in Violent Crime: Targeting Crime Harm in a Canadian Suburb
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Hutchinson, B., et al. (2022). Evaluation Gaps in Machine Learning Practice
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Isafiade, O., Ndingindwayo, B., Bagula, A. (2021). Predictive Policing Using Deep Learning: A Community Policing Practical Case Study
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Larson, J., Mattu, S., Kirchner, L., Angwin, J. (2016). How We Analyzed the COMPAS Recidivism Algorithm
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Liggins, A., Ratcliffe, J.H., Bland, M. (2019). Targeting the Most Harmful Offenders for an English Police Agency: Continuity and Change of Membership in the “Felonious Few”
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Somalwar, A., et al. (2021). AI For Bias Detection: Investigating the Existence of Racial Bias in Police Killings
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More publications from related authors: Matthew Bland, Alexandra Chouldechova, Miri Zilka
Topics in Policing
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Dominguez, M., Montolio, D. (2022). Social prevention of crime: alternatives to policing measures in an urban context
Elgar Modern Guides - Graham, M., Dittus, M. (2022). Geographies of Digital Exclusion: Data and Inequality
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Myhill, A. (2018). The police response to domestic violence: Risk, discretion, and the context of coercive control
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More publications from related authors: Miranda Horvath, Tom Kirchmaier
Ethical ML/AI and Useful General ML
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Aka, O., et al. (2021). Measuring Model Biases in the Absence of Ground Truth
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Brundage, M., et al. (2020). Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims
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d'Alessandro, B., O'Neil, C., LaGatta, T. (2019). Conscientious Classification: A Data Scientist's Guide to Discrimination-Aware Classification
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Donahue, K., Chouldechova, A., Kenthapadi, K., (2022). Human-Algorithm Collaboration: Achieving Complementarity and Avoiding Unfairness
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Gaci, Y. (2022). Debiasing Pretrained Text Encoders by Paying Attention to Paying Attention
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Heaton, H., Wu Fung, S. (2023). Explainable AI via Learning to Optimize
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Hutchinson, B., et al. (2022). Evaluation Gaps in Machine Learning Practice
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Madras, D., et al. (2019). Fairness through Causal Awareness: Learning Causal Latent-Variable Models for Biased Data
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Navigli, R., Conia, S., Ross, B. (2023). Biases in Large Language Models: Origins, Inventory, and Discussion
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Parrish A., et al. (2022). BBQ: A Hand-Built Bias Benchmark for Question Answering
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Tan, C., et al. (2021). Learning from Noisy Labels with Self-Supervision