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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2207.00108 |
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| _version_ | 1866909821694902272 |
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| author | Pappadà, Roberta Pauli, Francesco |
| author_facet | Pappadà, Roberta Pauli, Francesco |
| contents | Machine learning algorithms are routinely used for business decisions that may directly affect individuals, for example, because a credit scoring algorithm refuses them a loan. It is then relevant from an ethical (and legal) point of view to ensure that these algorithms do not discriminate based on sensitive attributes (like sex or race), which may occur unwittingly and unknowingly by the operator and the management. Statistical tools and methods are then required to detect and eliminate such potential biases. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2207_00108 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Discrimination in machine learning algorithms Pappadà, Roberta Pauli, Francesco Machine Learning Computers and Society Machine learning algorithms are routinely used for business decisions that may directly affect individuals, for example, because a credit scoring algorithm refuses them a loan. It is then relevant from an ethical (and legal) point of view to ensure that these algorithms do not discriminate based on sensitive attributes (like sex or race), which may occur unwittingly and unknowingly by the operator and the management. Statistical tools and methods are then required to detect and eliminate such potential biases. |
| title | Discrimination in machine learning algorithms |
| topic | Machine Learning Computers and Society |
| url | https://arxiv.org/abs/2207.00108 |