Digital discrimination detection in ridesharing services
Abstract
The introduction of technology has significantly transformed the urban transport industry, revealing social issues such as bias-driven trip cancellations. After a bibliographical review on how the topic is treated, we created an ontology and established the objective of analyzing digital discrimination, approaching it through the analysis of collective data, which can direct mechanisms to discourage discrimination in digital services. This study seeks to answer: RQ1: Is there evidence of digital discrimination in the shared transport service in the city of Rio de Janeiro? RQ2: Is it possible to identify the factors that lead to discrimination? RQ3: What are the key concepts related to detecting Digital Discrimination in a shared transport service?
Keywords:
Ontology, ridesharing service, crowd data, digital discrimination
References
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Alex Rosenblat, Karen E.C. Levy, S. B. and Hwang, T. (2017). Discriminating tastes: Uber’s customer ratings as vehicles for workplace discrimination.
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Brown, A. E. (2019). Prevalence and mechanisms of discrimination: Evidence from the ride-hail and taxi industries.
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Ge, Y. e. a. (2018). Racial discrimination in transportation network companies.
Jorge Mejia, C. P. (2020). When transparency fails: Bias and financial incentives in ridesharing platforms.
Miroslav Tushev, F. E. and Mahmoud, A. (2020). Digital discrimination in sharing economy.
Miroslav Tushev, F. E. and Mahmoud, A. (2021). A systematic literature review of anti-discrimination design strategies in the digital sharing economy.
Monachou, F. G. and Ashlagi, I. (2019). Discrimination in online markets: Effects of social bias on learning from reviews and policy design.
Murphy, S. A. (2002). Appendix b: Audit studies and the assessment of discrimination. Pandey, A. and Caliskan, A. (2021). Disparate impact of artificial intelligence bias in ridehailing.
Alex Rosenblat, Karen E.C. Levy, S. B. and Hwang, T. (2017). Discriminating tastes: Uber’s customer ratings as vehicles for workplace discrimination.
Batty, M. e. a. (2012). Smart cities of the future.
Brown, A. E. (2019). Prevalence and mechanisms of discrimination: Evidence from the ride-hail and taxi industries.
Dovidio, J. F. e. a. (2000). Reducing contemporary prejudice: Combating explicit and implicit bias at the individual and intergroup level.
Ge, Y. e. a. (2018). Racial discrimination in transportation network companies.
Jorge Mejia, C. P. (2020). When transparency fails: Bias and financial incentives in ridesharing platforms.
Miroslav Tushev, F. E. and Mahmoud, A. (2020). Digital discrimination in sharing economy.
Miroslav Tushev, F. E. and Mahmoud, A. (2021). A systematic literature review of anti-discrimination design strategies in the digital sharing economy.
Monachou, F. G. and Ashlagi, I. (2019). Discrimination in online markets: Effects of social bias on learning from reviews and policy design.
Murphy, S. A. (2002). Appendix b: Audit studies and the assessment of discrimination. Pandey, A. and Caliskan, A. (2021). Disparate impact of artificial intelligence bias in ridehailing.
Published
2024-04-29
How to Cite
PAIVA, Raquel T. de; CATALDO, Wendy S.; GARCIA, Ana Cristina B.; DE MELLO, Carlos E..
Digital discrimination detection in ridesharing services. In: BRAZILIAN SYMPOSIUM ON COLLABORATIVE SYSTEMS (SBSC), 19. , 2024, Salvador/BA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 183-191.
ISSN 2326-2842.
DOI: https://doi.org/10.5753/sbsc.2024.238064.
