Chapter 6 Learning to Discriminate
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 t...
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| Formato: | Online |
| Idioma: | inglés |
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Oxford University Press
2024
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| Acceso en liña: | https://library.oapen.org/handle/20.500.12657/90555 |
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| _version_ | 1869527618914615296 |
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| author | Davies, Benjamin Douglas, Thomas |
| author_browse | Davies, Benjamin Douglas, Thomas |
| author_facet | Davies, Benjamin Douglas, Thomas |
| author_sort | Davies, Benjamin |
| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-138244 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Oxford University Press |
| publisherStr | Oxford University Press |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1382442024-07-11T04:11:24Z Chapter 6 Learning to Discriminate Davies, Benjamin Douglas, Thomas Discrimination; Profiling; Machine Learning; Algorithmic Fairness; Racial Bias; Redundant Encoding; Criminal Recidivism; Crime Prediction; Artificial Intelligence; AI thema EDItEUR::J Society and Social Sciences::JK Social services and welfare, criminology::JKV Crime and criminology::JKVF Criminal investigation and detection thema EDItEUR::J Society and Social Sciences::JK Social services and welfare, criminology::JKV Crime and criminology 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. 2024-05-24T04:06:51Z 2024-05-24T04:06:51Z 2024-05-23T12:05:33Z 2022 chapter https://library.oapen.org/handle/20.500.12657/90555 https://directory.doabooks.org/handle/20.500.12854/138244 eng open access image/jpeg image/jpeg Attribution 4.0 International Attribution 4.0 International https://library.oapen.org/bitstream/20.500.12657/90555/1/DAVIES%20AND%20DOUGLAS%20Learning%20to%20Discriminate%20OUP%20preproof.pdf https://library.oapen.org/bitstream/20.500.12657/90555/1/DAVIES%20AND%20DOUGLAS%20Learning%20to%20Discriminate%20OUP%20preproof.pdf Oxford University Press db4e319f-ca9f-449a-bcf2-37d7c6f885b1 Sentencing and Artificial Intelligence H2020 European Research Council European Research Council (ERC) 26 open access |
| spellingShingle | Discrimination; Profiling; Machine Learning; Algorithmic Fairness; Racial Bias; Redundant Encoding; Criminal Recidivism; Crime Prediction; Artificial Intelligence; AI thema EDItEUR::J Society and Social Sciences::JK Social services and welfare, criminology::JKV Crime and criminology::JKVF Criminal investigation and detection thema EDItEUR::J Society and Social Sciences::JK Social services and welfare, criminology::JKV Crime and criminology Davies, Benjamin Douglas, Thomas Chapter 6 Learning to Discriminate |
| title | Chapter 6 Learning to Discriminate |
| title_full | Chapter 6 Learning to Discriminate |
| title_fullStr | Chapter 6 Learning to Discriminate |
| title_full_unstemmed | Chapter 6 Learning to Discriminate |
| title_short | Chapter 6 Learning to Discriminate |
| title_sort | chapter 6 learning to discriminate |
| topic | Discrimination; Profiling; Machine Learning; Algorithmic Fairness; Racial Bias; Redundant Encoding; Criminal Recidivism; Crime Prediction; Artificial Intelligence; AI thema EDItEUR::J Society and Social Sciences::JK Social services and welfare, criminology::JKV Crime and criminology::JKVF Criminal investigation and detection thema EDItEUR::J Society and Social Sciences::JK Social services and welfare, criminology::JKV Crime and criminology |
| topic_facet | Discrimination; Profiling; Machine Learning; Algorithmic Fairness; Racial Bias; Redundant Encoding; Criminal Recidivism; Crime Prediction; Artificial Intelligence; AI thema EDItEUR::J Society and Social Sciences::JK Social services and welfare, criminology::JKV Crime and criminology::JKVF Criminal investigation and detection thema EDItEUR::J Society and Social Sciences::JK Social services and welfare, criminology::JKV Crime and criminology |
| url | https://library.oapen.org/handle/20.500.12657/90555 |
| work_keys_str_mv | AT daviesbenjamin chapter6learningtodiscriminate AT douglasthomas chapter6learningtodiscriminate |