%0 Conference Paper %1 Roy2023 %A Roy, Arjun %A Horstmann, Jan %A Ntoutsi, Eirini %B Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency %C New York, NY, USA %D 2023 %I ACM %K fairness myown from:entoutsi aiml_group l3s responsibleML %P 89–100 %R 10.1145/3593013.3593979 %T Multi-dimensional Discrimination in Law and Machine Learning - A Comparative Overview %U https://doi.org/10.1145/3593013.3593979 %V Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency %X AI-driven decision-making can lead to discrimination against certain individuals or social groups based on protected characteristics/attributes such as race, gender, or age. The domain of fairness-aware machine learning focuses on methods and algorithms for understanding, mitigating, and accounting for bias in AI/ML models. Still, thus far, the vast majority of the proposed methods assess fairness based on a single protected attribute, e.g. only gender or race. In reality, though, human identities are multi-dimensional, and discrimination can occur based on more than one protected characteristic, leading to the so-called “multi-dimensional discrimination” or “multi-dimensional fairness” problem. While well-elaborated in legal literature, the multi-dimensionality of discrimination is less explored in the machine learning community. Recent approaches in this direction mainly follow the so-called intersectional fairness definition from the legal domain, whereas other notions like additive and sequential discrimination are less studied or not considered thus far. In this work, we overview the different definitions of multi-dimensional discrimination/fairness in the legal domain as well as how they have been transferred/ operationalized (if) in the fairness-aware machine learning domain. By juxtaposing these two domains, we draw the connections, identify the limitations, and point out open research directions. %@ 9798400701924

@inproceedings{Roy2023, abstract = {AI-driven decision-making can lead to discrimination against certain individuals or social groups based on protected characteristics/attributes such as race, gender, or age. The domain of fairness-aware machine learning focuses on methods and algorithms for understanding, mitigating, and accounting for bias in AI/ML models. Still, thus far, the vast majority of the proposed methods assess fairness based on a single protected attribute, e.g. only gender or race. In reality, though, human identities are multi-dimensional, and discrimination can occur based on more than one protected characteristic, leading to the so-called “multi-dimensional discrimination” or “multi-dimensional fairness” problem. While well-elaborated in legal literature, the multi-dimensionality of discrimination is less explored in the machine learning community. Recent approaches in this direction mainly follow the so-called intersectional fairness definition from the legal domain, whereas other notions like additive and sequential discrimination are less studied or not considered thus far. In this work, we overview the different definitions of multi-dimensional discrimination/fairness in the legal domain as well as how they have been transferred/ operationalized (if) in the fairness-aware machine learning domain. By juxtaposing these two domains, we draw the connections, identify the limitations, and point out open research directions.}, added-at = {2024-02-28T20:40:21.000+0100}, address = {New York, NY, USA}, author = {Roy, Arjun and Horstmann, Jan and Ntoutsi, Eirini}, biburl = {https://www.bibsonomy.org/bibtex/227bbb5d4e943ea9cbf4e21f4fb0a8fde/l3s}, booktitle = {Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency}, day = 12, description = {Multi-dimensional Discrimination in Law and Machine Learning - A Comparative Overview | Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency}, doi = {10.1145/3593013.3593979}, interhash = {596029e5110511d139fab5b43e482023}, intrahash = {27bbb5d4e943ea9cbf4e21f4fb0a8fde}, isbn = {9798400701924}, keywords = {fairness myown from:entoutsi aiml_group l3s responsibleML}, location = {<conf-loc>, <city>Chicago</city>, <state>IL</state>, <country>USA</country>, </conf-loc>}, month = {6}, pages = {89–100}, publisher = {ACM}, series = {FAccT '23}, timestamp = {2024-02-28T20:40:21.000+0100}, title = {Multi-dimensional Discrimination in Law and Machine Learning - A Comparative Overview}, url = {https://doi.org/10.1145/3593013.3593979}, volume = {Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency}, year = 2023 }