Skip to main content
SpringerLink
Log in

Matchmaking Under Fairness Constraints: A Speed Dating Case Study

  • Conference paper
  • First Online:
Book cover Bias and Social Aspects in Search and Recommendation (BIAS 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1245))

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://coffeemeetsbagel.com/.

  2. 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. 3.

    Due to the termination criterion, the precise running time of Tabu search is unknown.

  4. 4.

    Source code: https://git.io/preferential_fairness.

  5. 5.

    A limitation of this approach is that it cannot properly match participants having biracial or multiracial identity.

  6. 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.

References

  1. Alfonsín, R.J.: On variations of the subset sum problem. Discrete Appl. Math. 81(1–3), 1–7 (1998)

    Article  MathSciNet  Google Scholar 

  2. Biega, A.J., Gummadi, K.P., Weikum, G.: Equity of attention: amortizing individual fairness in rankings. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, SIGIR 2018, pp. 405–414. ACM (2018)

    Google Scholar 

  3. Burke, R.: Multisided fairness for recommendation. CoRR abs/1707.00093 (2017). http://arxiv.org/abs/1707.00093

  4. Cappanera, P., Trubian, M.: A local-search-based heuristic for the demand-constrained multidimensional knapsack problem. INFORMS J. Comput. 17(1), 82–98 (2005)

    Article  MathSciNet  Google Scholar 

  5. Caprara, A., Kellerer, H., Pferschy, U., Pisinger, D.: Approximation algorithms for knapsack problems with cardinality constraints. Eur. J. Oper. Res. 123(2), 333–345 (2000)

    Article  MathSciNet  Google Scholar 

  6. Dwork, C., Hardt, M., Pitassi, T., Reingold, O., Zemel, R.: Fairness through awareness. In: Proceedings of the 3rd Innovations in Theoretical Computer Science Conference, pp. 214–226. ACM (2012)

    Google Scholar 

  7. Fisman, R., Iyengar, S.S., Kamenica, E., Simonson, I.: Gender differences in mate selection: evidence from a speed dating experiment. Q. J. Econ. 121(2), 673–697 (2006)

    Article  Google Scholar 

  8. Fisman, R., Iyengar, S.S., Kamenica, E., Simonson, I.: Racial preferences in dating. Rev. Econ. Stud. 75(1), 117–132 (2008)

    Article  Google Scholar 

  9. Gajane, P., Pechenizkiy, M.: On formalizing fairness in prediction with machine learning. CoRR abs/1710.03184 (2017). http://arxiv.org/abs/1710.03184

  10. Gosepath, S.: Equality. In: Zalta, E.N. (ed.) The Stanford Encyclopedia of Philosophy, 2011 edn. Metaphysics Research Lab, Stanford University, Spring (2011)

    Google Scholar 

  11. Hutson, J.A., Taft, J.G., Barocas, S., Levy, K.: Debiasing desire: addressing bias & discrimination on intimate platforms. Proc. ACM Hum.-Comput. Interact. 2(CSCW), 73:1–73:18 (2018)

    Article  Google Scholar 

  12. Joel, S., Eastwick, P.W., Finkel, E.J.: Is romantic desire predictable? Machine learning applied to initial romantic attraction. Psychol. Sci. 28(10), 1478–1489 (2017)

    Article  Google Scholar 

  13. Joseph, M., Kearns, M., Morgenstern, J., Neel, S., Roth, A.: Meritocratic fairness for infinite and contextual bandits. In: Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, AIES 2018, pp. 158–163. ACM (2018)

    Google Scholar 

  14. Kamishima, T., Akaho, S., Asoh, H.: Enhancement of the neutrality in recommendation. In: Proceedings of the 2nd Workshop on Human Decision Making in Recommender Systems, pp. 8–14 (2012)

    Google Scholar 

  15. Karako, C., Manggala, P.: Using image fairness representations in diversity-based re-ranking for recommendations. In: Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization, UMAP 2018, pp. 23–28. ACM (2018)

    Google Scholar 

  16. Kyriakidi, M., Stefanidis, K., Ioannidis, Y.: On achieving diversity in recommender systems. In: Proceedings of the ExploreDB 2017, pp. 4:1–4:6. ACM (2017)

    Google Scholar 

  17. Liu, W., Burke, R.: Personalizing fairness-aware re-ranking. CoRR abs/1809.02921 (2018). http://arxiv.org/abs/1809.02921

  18. Liu, Y., Radanovic, G., Dimitrakakis, C., Mandal, D., Parkes, D.C.: Calibrated fairness in bandits. In: Proceedings of the 4th Workshop on Fairness, Accountability, and Transparency in Machine Learning (Fat/ML 2017) (2017). https://arxiv.org/abs/1707.01875

  19. Martello, S., Toth, P.: Algorithms for knapsack problems. North-Holland Math. Stud. 132, 213–257 (1987)

    Article  MathSciNet  Google Scholar 

  20. Notopoulos, K.: The dating app that knows you secretly aren’t into guys from other races, January 2016. https://www.buzzfeednews.com/article/katienotopoulos/coffee-meets-bagel-racial-preferences. Accessed 26 June 2019

  21. Pazzani, M.J.: A framework for collaborative, content-based and demographic filtering. Artif. Intell. Rev. 13(5–6), 393–408 (1999)

    Article  Google Scholar 

  22. Pew Research Center: Racial and ethnic composition by religious group (2014). https://www.pewforum.org/religious-landscape-study/racial-and-ethnic-composition/. Accessed 09 July 2019

  23. Sapiezynski, P., Zeng, W., Robertson, R.E., Mislove, A., Wilson, C.: Quantifying the impact of user attention on fair group representation in ranked lists. In: Companion Proceedings of the 2019 World Wide Web Conference, WWW 2019, pp. 553–562. ACM (2019)

    Google Scholar 

  24. Saxena, N.A., Huang, K., DeFilippis, E., Radanovic, G., Parkes, D.C., Liu, Y.: How do fairness definitions fare?: examining public attitudes towards algorithmic definitions of fairness. In: Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, AIES 2019, pp. 99–106. ACM (2019)

    Google Scholar 

  25. Steck, H.: Calibrated recommendations. In: Proceedings of the 12th ACM Conference on Recommender Systems, pp. 154–162. ACM (2018)

    Google Scholar 

  26. Van Der Zon, S.B.: Predictive performance and discrimination in unbalanced classification. Master’s thesis, TU Eindhoven (2016)

    Google Scholar 

  27. Verma, S., Rubin, J.: Fairness definitions explained. In: Proceedings of the International Workshop on Software Fairness, FairWare 2018, pp. 1–7. ACM (2018)

    Google Scholar 

  28. Xiao, L., Min, Z., Yongfeng, Z., Zhaoquan, G., Yiqun, L., Shaoping, M.: Fairness-aware group recommendation with pareto-efficiency. In: Proceedings of the Eleventh ACM Conference on Recommender Systems, RecSys 2017, pp. 107–115. ACM (2017)

    Google Scholar 

  29. Xu, Z.: The knapsack problem with a minimum filling constraint. Naval Res. Logist. 60(1), 56–63 (2013)

    Article  MathSciNet  Google Scholar 

  30. Yang, K., Stoyanovich, J.: Measuring fairness in ranked outputs. In: Proceedings of the 29th International Conference on Scientific and Statistical Database Management, pp. 22:1–22:6. ACM (2017)

    Google Scholar 

  31. Yao, S., Huang, B.: New fairness metrics for recommendation that embrace differences. CoRR abs/1706.09838 (2017). http://arxiv.org/abs/1706.09838

  32. Zehlike, M., Bonchi, F., Castillo, C., Hajian, S., Megahed, M., Baeza-Yates, R.: FA*IR: a fair top-k ranking algorithm. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, CIKM 2017, pp. 1569–1578. ACM (2017)

    Google Scholar 

  33. Zheng, Y., Dave, T., Mishra, N., Kumar, H.: Fairness in reciprocal recommendations: a speed-dating study. In: Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization, UMAP 2018, pp. 29–34. ACM (2018)

    Google Scholar 

  34. Zhu, Z., Hu, X., Caverlee, J.: Fairness-aware tensor-based recommendation. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, pp. 1153–1162. ACM (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Malmö University, Nordenskiöldsgatan 1, 211 19, Malmö, Sweden

    Dimitris Paraschakis & Bengt J. Nilsson

Authors
  1. Dimitris Paraschakis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bengt J. Nilsson
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dimitris Paraschakis .

Editor information

Editors and Affiliations

  1. Eurecat - Centre Tecnològic de Catalunya, Barcelona, Spain

    Ludovico Boratto

  2. Unitelma Sapienza University of Rome, Rome, Italy

    Stefano Faralli

  3. University of Cagliari, Cagliari, Italy

    Mirko Marras

  4. University of L’Aquila, L’Aquila, Italy

    Giovanni Stilo

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

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

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-52485-2_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-52484-5

  • Online ISBN: 978-3-030-52485-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation