Abstract
Reported evidence of biased matchmaking calls into question the ethicality of recommendations generated by a machine learning algorithm. In the context of dating services, the failure of an automated matchmaker to respect the user’s expressed sensitive preferences (racial, religious, etc.) may lead to biased decisions perceived by users as unfair. To address the issue, we introduce the notion of preferential fairness, and propose two algorithmic approaches for re-ranking the recommendations under preferential fairness constraints. Our experimental results demonstrate that the state of fairness can be reached with minimal accuracy compromises for both binary and non-binary attributes.
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Notes
- 1.
- 2.
The assumption of proportionality may not hold for all individuals. More user studies are needed to better understand what users actually mean by ‘no racial preference’.
- 3.
Due to the termination criterion, the precise running time of Tabu search is unknown.
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Source code: https://git.io/preferential_fairness.
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A limitation of this approach is that it cannot properly match participants having biracial or multiracial identity.
- 6.
Although the speed dating study also took place in the U.S., we allow some margin of error due to state-to-state variability of racial/religious composition.
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Paraschakis, D., Nilsson, B.J. (2020). Matchmaking Under Fairness Constraints: A Speed Dating Case Study. In: Boratto, L., Faralli, S., Marras, M., Stilo, G. (eds) Bias and Social Aspects in Search and Recommendation. BIAS 2020. Communications in Computer and Information Science, vol 1245. Springer, Cham. https://doi.org/10.1007/978-3-030-52485-2_5
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