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Counterfactual fairness: The case study of a food delivery platform’s reputational-ranking algorithm
Data-driven algorithms are currently deployed in several fields, leading to a rapid increase in the importance algorithms have in decision-making processes. Over the last years, several instances of discrimination by algorithms were observed. A new branch of research emerged to examine the concept o...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9643653/ https://www.ncbi.nlm.nih.gov/pubmed/36389466 http://dx.doi.org/10.3389/fpsyg.2022.1015100 |
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author | Piccininni, Marco |
author_facet | Piccininni, Marco |
author_sort | Piccininni, Marco |
collection | PubMed |
description | Data-driven algorithms are currently deployed in several fields, leading to a rapid increase in the importance algorithms have in decision-making processes. Over the last years, several instances of discrimination by algorithms were observed. A new branch of research emerged to examine the concept of “algorithmic fairness.” No consensus currently exists on a single operationalization of fairness, although causal-based definitions are arguably more aligned with the human conception of fairness. The aim of this article is to investigate the degree of this alignment in a case study inspired by a recent ruling of an Italian court on the reputational-ranking algorithm used by a food delivery platform. I relied on the documentation of the legal dispute to discuss the applicability, intuitiveness and appropriateness of causal models in evaluating fairness, with a specific focus on a causal-based fairness definition called “counterfactual fairness.” I first describe the details of the dispute and the arguments presented to the court, as well as the court’s final decision, to establish the context of the case study. Then, I translate the dispute into a formal simplified problem using a causal diagram, which represents the main aspects of the data generation process in the case study. I identify the criteria used by the court in ruling that the algorithm was unfair and compare them with the counterfactual fairness definition. The definition of counterfactual fairness was found to be well aligned with the human conception of fairness in this case study, using the court order rationale as a gold standard. |
format | Online Article Text |
id | pubmed-9643653 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-96436532022-11-15 Counterfactual fairness: The case study of a food delivery platform’s reputational-ranking algorithm Piccininni, Marco Front Psychol Psychology Data-driven algorithms are currently deployed in several fields, leading to a rapid increase in the importance algorithms have in decision-making processes. Over the last years, several instances of discrimination by algorithms were observed. A new branch of research emerged to examine the concept of “algorithmic fairness.” No consensus currently exists on a single operationalization of fairness, although causal-based definitions are arguably more aligned with the human conception of fairness. The aim of this article is to investigate the degree of this alignment in a case study inspired by a recent ruling of an Italian court on the reputational-ranking algorithm used by a food delivery platform. I relied on the documentation of the legal dispute to discuss the applicability, intuitiveness and appropriateness of causal models in evaluating fairness, with a specific focus on a causal-based fairness definition called “counterfactual fairness.” I first describe the details of the dispute and the arguments presented to the court, as well as the court’s final decision, to establish the context of the case study. Then, I translate the dispute into a formal simplified problem using a causal diagram, which represents the main aspects of the data generation process in the case study. I identify the criteria used by the court in ruling that the algorithm was unfair and compare them with the counterfactual fairness definition. The definition of counterfactual fairness was found to be well aligned with the human conception of fairness in this case study, using the court order rationale as a gold standard. Frontiers Media S.A. 2022-10-26 /pmc/articles/PMC9643653/ /pubmed/36389466 http://dx.doi.org/10.3389/fpsyg.2022.1015100 Text en Copyright © 2022 Piccininni. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Psychology Piccininni, Marco Counterfactual fairness: The case study of a food delivery platform’s reputational-ranking algorithm |
title | Counterfactual fairness: The case study of a food delivery platform’s reputational-ranking algorithm |
title_full | Counterfactual fairness: The case study of a food delivery platform’s reputational-ranking algorithm |
title_fullStr | Counterfactual fairness: The case study of a food delivery platform’s reputational-ranking algorithm |
title_full_unstemmed | Counterfactual fairness: The case study of a food delivery platform’s reputational-ranking algorithm |
title_short | Counterfactual fairness: The case study of a food delivery platform’s reputational-ranking algorithm |
title_sort | counterfactual fairness: the case study of a food delivery platform’s reputational-ranking algorithm |
topic | Psychology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9643653/ https://www.ncbi.nlm.nih.gov/pubmed/36389466 http://dx.doi.org/10.3389/fpsyg.2022.1015100 |
work_keys_str_mv | AT piccininnimarco counterfactualfairnessthecasestudyofafooddeliveryplatformsreputationalrankingalgorithm |