Exploiting Decision Trees to Detect Indirect Discrimination
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2022
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| author | Gouveia, Filipe Lynce, Inês |
| author_facet | Gouveia, Filipe Lynce, Inês |
| contents | <p>Machine learning systems are increasingly present in our everyday lives, making decisions in multiple scenarios such as credit loans or advertisement displays. <br>For this reason, there is a crescent concern regarding fairness in applying machine learning systems. <br>Fairness is particularly relevant when such systems can affect a person's life.</p> <p>When training classification models, several approaches consider that some attributes are protected to avoid discriminatory behavior. Hence, a model should not use these attributes to make a decision.<br>These attributes are typically sensitive features for which it is desired not to have discriminatory behavior or bias (e.g., race or gender).<br>However, it may be possible to infer information of a protected attribute based on other non-protected attributes' data, leading to (indirect) discriminatory behavior even without using the protected attributes.</p> <p>In this work, we introduce an approach to assess whether a dataset used to train a machine learning model contains potential sources of indirect discrimination. This approach exploits the use of decision trees to identify such potential sources of discrimination.<br>We can identify from which attributes it is possible to infer information regarding a protected attribute. These attributes are called proxy attributes.<br>Moreover, we consider sets of proxy attributes from which it is possible to infer sensitive information when combined. If these attributes were deemed separately, no information could be inferred.</p> <p>The proposed approach is applied to several datasets used in fairness-awareness studies. The experimental evaluation identifies possible reasons for indirect discriminatory behavior in the datasets.<br>Moreover, the evaluation also considers thresholds that allow the identification of proxy attributes with a certain degree of certainty. Therefore, we can identify proxy attributes even when the dataset contains noisy data.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_14810692 |
| institution | Zenodo |
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| publishDate | 2022 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Exploiting Decision Trees to Detect Indirect Discrimination Gouveia, Filipe Lynce, Inês <p>Machine learning systems are increasingly present in our everyday lives, making decisions in multiple scenarios such as credit loans or advertisement displays. <br>For this reason, there is a crescent concern regarding fairness in applying machine learning systems. <br>Fairness is particularly relevant when such systems can affect a person's life.</p> <p>When training classification models, several approaches consider that some attributes are protected to avoid discriminatory behavior. Hence, a model should not use these attributes to make a decision.<br>These attributes are typically sensitive features for which it is desired not to have discriminatory behavior or bias (e.g., race or gender).<br>However, it may be possible to infer information of a protected attribute based on other non-protected attributes' data, leading to (indirect) discriminatory behavior even without using the protected attributes.</p> <p>In this work, we introduce an approach to assess whether a dataset used to train a machine learning model contains potential sources of indirect discrimination. This approach exploits the use of decision trees to identify such potential sources of discrimination.<br>We can identify from which attributes it is possible to infer information regarding a protected attribute. These attributes are called proxy attributes.<br>Moreover, we consider sets of proxy attributes from which it is possible to infer sensitive information when combined. If these attributes were deemed separately, no information could be inferred.</p> <p>The proposed approach is applied to several datasets used in fairness-awareness studies. The experimental evaluation identifies possible reasons for indirect discriminatory behavior in the datasets.<br>Moreover, the evaluation also considers thresholds that allow the identification of proxy attributes with a certain degree of certainty. Therefore, we can identify proxy attributes even when the dataset contains noisy data.</p> |
| title | Exploiting Decision Trees to Detect Indirect Discrimination |
| url | https://doi.org/10.5281/zenodo.14810692 |