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    Chapter 6 Learning to Discriminate

    The Perfect Proxy Problem in Artificially Intelligent Criminal Sentencing

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    Author(s)
    Davies, Benjamin
    Douglas, Thomas
    Collection
    European Research Council (ERC)
    Language
    English
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    Abstract
    It is often thought that traditional recidivism prediction tools used in criminal sentencing, though biased in many ways, can straightforwardly avoid one particularly pernicious type of bias: direct racial discrimination. They can avoid this by excluding race from the list of variables employed to predict recidivism. A similar approach could be taken to the design of newer, machine learning-based (ML) tools for predicting recidivism: information about race could be withheld from the ML tool during its training phase, ensuring that the resulting predictive model does not use race as an explicit predictor. However, if race is correlated with measured recidivism in the training data, the ML tool may ‘learn’ a perfect proxy for race. If such a proxy is found, the exclusion of race would do nothing to weaken the correlation between risk (mis)classifications and race. Is this a problem? We argue that, on some explanations of the wrongness of discrimination, it is. On these explanations, the use of an ML tool that perfectly proxies race would (likely) be more wrong than the use of a traditional tool that imperfectly proxies race. Indeed, on some views, use of a perfect proxy for race is plausibly as wrong as explicit racial profiling. We end by drawing out four implications of our arguments.
    Book
    Sentencing and Artificial Intelligence
    URI
    https://library.oapen.org/handle/20.500.12657/90555
    Keywords
    Discrimination; Profiling; Machine Learning; Algorithmic Fairness; Racial Bias; Redundant Encoding; Criminal Recidivism; Crime Prediction; Artificial Intelligence; AI
    ISBN
    9780197539538
    Publisher
    Oxford University Press
    Publisher website
    https://global.oup.com/
    Publication date and place
    2022
    Grantor
    • H2020 European Research Council
    Classification
    Criminal investigation and detection
    Crime and criminology
    Pages
    26
    Rights
    https://creativecommons.org/licenses/by/4.0/
    • Imported or submitted locally

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    License

    • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

    Credits

    • logo EU
    • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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