Fairness in rankings and recommendations: an overview

Angwin, J., et al.: Machine bias. ProPublica (2016). https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing Albarghouthi, A., D’Antoni, L., Drews, S., Nori, A.V.: Fairness as a program property. CoRR abs/1610.06067 (2016). http://arxiv.org/abs/1610.06067 Albarghouthi, A., Vinitsky, S.: Fairness-aware programming. In: Proceedings of the Conference on Fairness, Accountability, and Transparency, FAT* 2019, pp. 211–219. ACM (2019) Amer-Yahia, S., Roy, S.B., Chawla, A., Das, G., Yu, C.: Group recommendation: semantics and efficiency. Proc. VLDB Endow. 2(1), 754–765 (2009) Asudeh, A., Jagadish, H.V.: Fairly evaluating and scoring items in a data set. Proc. VLDB Endow. 13(12), 3445–3448 (2020) Asudeh, A., Jagadish, H.V., Stoyanovich, J., Das, G.: Designing fair ranking schemes. In: Proceedings of the 2019 International Conference on Management of Data, SIGMOD Conference 2019, pp. 1259–1276. ACM (2019) Asudeh, A., Jin, Z., Jagadish, H.V.: Assessing and remedying coverage for a given dataset. In: 35th IEEE International Conference on Data Engineering, ICDE 2019, pp. 554–565. IEEE (2019) Baeza-Yates, R.: Bias on the web. Commun. ACM 61(6), 54–61 (2018) Baruah, S.K., Cohen, N.K., Plaxton, C.G., Varvel, D.A.: Proportionate progress: a notion of fairness in resource allocation. Algorithmica 15(6), 600–625 (1996) Beutel, A., Chen, J., Doshi, T., Qian, H., Wei, L., Wu, Y., Heldt, L., Zhao, Z., Hong, L., Chi, E.H., Goodrow, C.: Fairness in recommendation ranking through pairwise comparisons. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, pp. 2212–2220. ACM (2019) 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) Bolukbasi, T., Chang, K., Zou, J.Y., Saligrama, V., Kalai, A.T.: Man is to computer programmer as woman is to homemaker? Debiasing word embeddings. In: Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, pp. 4349–4357 (2016) Borges, R., Stefanidis, K.: Enhancing long term fairness in recommendations with variational autoencoders. In: 11th International Conference on Management of Digital EcoSystems, MEDES 2019, pp. 95–102. ACM (2019) Burges, C.J.C., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N., Hullender, G.N.: Learning to rank using gradient descent. In: Machine Learning, Proceedings of the Twenty-Second International Conference (ICML 2005), ACM International Conference Proceeding Series, vol. 119, pp. 89–96. ACM (2005) Calders, T., Verwer, S.: Three Naive bayes approaches for discrimination-free classification. Data Min. Knowl. Discov. 21(2), 277–292 (2010) Cao, Z., Qin, T., Liu, T., Tsai, M., Li, H.: Learning to rank: from pairwise approach to listwise approach. In: Machine Learning, Proceedings of the Twenty-Fourth International Conference (ICML 2007), ACM International Conference Proceeding Series, vol. 227, pp. 129–136. ACM (2007) Carbinell, J., Goldstein, J.: The use of MMR, diversity-based reranking for reordering documents and producing summaries. SIGIR Forum 51(2), 209–210 (2017) Celis, L.E., Straszak, D., Vishnoi, N.K.: Ranking with fairness constraints. In: 45th International Colloquium on Automata, Languages, and Programming, ICALP 2018, LIPIcs, vol. 107, pp. 28:1–28:15. Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2018) Chierichetti, F., Kumar, R., Lattanzi, S., Vassilvitskii, S.: Fair clustering through fairlets. In: Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4–9, 2017, Long Beach, CA, USA, pp. 5029–5037 (2017) Chouldechova, A., Roth, A.: A snapshot of the frontiers of fairness in machine learning. Commun. ACM 63(5), 82–89 (2020). https://doi.org/10.1145/3376898 Crammer, K., Singer, Y.: Pranking with ranking. In: Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, NIPS 2001], pp. 641–647. MIT Press (2001) Chouldechova, A.: Fair prediction with disparate impact: a study of bias in recidivism prediction instruments. Big Data 5(2), 153–163 (2017) Dastin, J.: RPT-insight-amazon scraps secret AI recruiting tool that showed bias against women. In: Reuters (2018) Dwork, C., Hardt, M., Pitassi, T., Reingold, O., Zemel, R.S.: Fairness through awareness. In: Innovations in Theoretical Computer Science 2012, pp. 214–226. ACM (2012) Ekstrand, M.D., Burke, R., Diaz, F.: Fairness and discrimination in retrieval and recommendation. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019, pp. 1403–1404. ACM (2019) Elbassuoni, S., Amer-Yahia, S., Ghizzawi, A.: Fairness of scoring in online job marketplaces. Trans. Data Sci. 1(4), 29:1-29:30 (2020) Fehr, E., Goette, L., Zehnder, C.: A behavioral account of the labor market: the role of fairness concerns. Ann. Rev. Econ. 1, 355–384 (2009) Feldman, M., Friedler, S.A., Moeller, J., Scheidegger, C., Venkatasubramanian, S.: Certifying and removing disparate impact. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 259–268. ACM (2015) Ferraro, A., Serra, X., Bauer, C.: Break the loop: gender imbalance in music recommenders. In: Scholer, F., Thomas, P., Elsweiler, D., Joho, H., Kando, N., Smith, C. (eds.) CHIIR ’21: ACM SIGIR Conference on Human Information Interaction and Retrieval, Canberra, ACT, Australia, March 14–19, 2021, pp. 249–254. ACM (2021) Freund, Y., Iyer, R.D., Schapire, R.E., Singer, Y.: An efficient boosting algorithm for combining preferences. In: Proceedings of the Fifteenth International Conference on Machine Learning (ICML 1998), pp. 170–178. Morgan Kaufmann (1998) Friedler, S.A., Scheidegger, C., Venkatasubramanian, S.: On the (im)possibility of fairness. CoRR abs/1609.07236 (2016). http://arxiv.org/abs/1609.07236 Friedler, S.A., Scheidegger, C., Venkatasubramanian, S.: The (im)possibility of fairness: different value systems require different mechanisms for fair decision making. Commun. ACM 64(4), 136–143 (2021) Friedler, S.A., Scheidegger, C., Venkatasubramanian, S., Choudhary, S., Hamilton, E.P., Roth, D.: A comparative study of fairness-enhancing interventions in machine learning. In: Proceedings of the Conference on Fairness, Accountability, and Transparency, FAT* 2019, pp. 329–338. ACM (2019) Galhotra, S., Brun, Y., Meliou, A.: Fairness testing: testing software for discrimination. In: Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering, ESEC/FSE 2017, pp. 498–510. ACM (2017) Geyik, S.C., Ambler, S., Kenthapadi, K.: Fairness-aware ranking in search & recommendation systems with application to linkedin talent search. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, pp. 2221–2231. ACM (2019) Ghodsi, A., Zaharia, M., Hindman, B., Konwinski, A., Shenker, S., Stoica, I.: Dominant resource fairness: fair allocation of multiple resource types. In: Proceedings of the 8th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2011. USENIX Association (2011) Grgic-Hlaca, N., Redmiles, E.M., Gummadi, K.P., Weller, A.: Human perceptions of fairness in algorithmic decision making: a case study of criminal risk prediction. In: Proceedings of the 2018 World Wide Web Conference on World Wide Web, WWW 2018, pp. 903–912. ACM (2018) Gupta, C., Mehta, A., Wang, S., Dayal, U.: Fair, effective, efficient and differentiated scheduling in an enterprise data warehouse. In: EDBT 2009, 12th International Conference on Extending Database Technology, ACM International Conference Proceeding Series, vol. 360, pp. 696–707. ACM (2009) Hainmueller, J.: Entropy balancing for causal effects: a multivariate reweighting method to produce balanced samples in observational studies. Polit. Anal. 20(1), 25–46 (2012) Hardt, M., Price, E., Srebro, N.: Equality of opportunity in supervised learning. In: NIPS, pp. 3315–3323 (2016) Ho, D., Imai, K., King, G., Stuart, E.: Matchit: nonparametric preprocessing for parametric causal inference. J. Stat. Softw. 42(8), 1–28 (2011) Hu, L., Chen, Y.: A short-term intervention for long-term fairness in the labor market. In: Proceedings of the 2018 World Wide Web Conference on World Wide Web, WWW 2018, Lyon, France, April 23–27, 2018, pp. 1389–1398. ACM (2018) Imana, B., Korolova, A., Heidemann, J.: Auditing for discrimination in algorithms delivering job ads. In: Proceedings of the Web Conference 2021 (WWW ’21) (2021) Ingold, D., Soper, S.: Amazon doesn’t consider the race of its customers. Should it? In: Bloomberg (2016) Ingold, D., Soper, S.: Amazon doesn’t consider the race of its customers. Should it? (2016) Jameson, A., Smyth, B.: Recommendation to groups. In: The Adaptive Web, Methods and Strategies of Web Personalization, Lecture Notes in Computer Science, vol. 4321, pp. 596–627. Springer (2007) Kamiran, F., Calders, T.: Classifying without discriminating. In: 2009 2nd International Conference on Computer, Control and Communication, pp. 1–6 (2009) Kamiran, F., Calders, T.: Data preprocessing techniques for classification without discrimination. Knowl. Inf. Syst. 33(1), 1–33 (2011) Kamishima, T., Akaho, S., Asoh, H., Sakuma, J.: Recommendation independence. In: Conference on Fairness, Accountability and Transparency, FAT 2018, Proceedings of Machine Learning Research, vol. 81, pp. 187–201. PMLR (2018) Kay, M., Matuszek, C., Munson, S.A.: Unequal representation and gender stereotypes in image search results for occupations. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, CHI 2015, pp. 3819–3828. ACM (2015) Kendall, M.G.: A new measure of rank correlation. Biometrika 30(1), 81–93 (1938) Kingma, D.P., Welling, M.: Auto-encoding variational bayes. In: 2nd International Conference on Learning Representations, ICLR 2014 (2014) Kleinberg, J.M., Mullainathan, S., Raghavan, M.: Inherent trade-offs in the fair determination of risk scores. In: 8th Innovations in Theoretical Computer Science Conference, ITCS 2017, LIPIcs, vol. 67, pp. 43:1–43:23. Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2017) Krasanakis, E., Spyromitros-Xioufis, E., Papadopoulos, S., Kompatsiaris, I.: Adaptive sensitive reweighting to mitigate bias in fairness-aware classification, pp. 853–862 (2018) Kuhlman, C., Rundensteiner, E.A.: Rank aggregation algorithms for fair consensus. Proc. VLDB Endow. 13(11), 2706–2719 (2020) Kunjir, M., Fain, B., Munagala, K., Babu, S.: ROBUS: fair cache allocation for data-parallel workloads. In: Proceedings of the 2017 ACM International Conference on Management of Data, SIGMOD Conference 2017, pp. 219–234. ACM (2017) Kusner, M.J., Loftus, J.R., Russell, C., Silva, R.: Counterfactual fairness. In: Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, pp. 4066–4076 (2017) Liang, D., Krishnan, R.G., Hoffman, M.D., Jebara, T.: Variational autoencoders for collaborative filtering. In: Proceedings of the 2018 World Wide Web Conference on World Wide Web, WWW 2018, pp. 689–698. ACM (2018) Lin, X., Zhang, M., Zhang, Y., Gu, Z., Liu, Y., Ma, S.: Fairness-aware group recommendation with pareto-efficiency. In: Proceedings of the Eleventh ACM Conference on Recommender Systems, RecSys 2017, pp. 107–115. ACM (2017) Liu, T.: Learning to Rank for Information Retrieval. Springer (2011). Lum, K., Johndrow, J.E.: A statistical framework for fair predictive algorithms. CoRR abs/1610.08077 (2016). http://arxiv.org/abs/1610.08077 Machado, L., Stefanidis, K.: Fair team recommendations for multidisciplinary projects. In: 2019 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2019, Thessaloniki, Greece, October 14–17, 2019, pp. 293–297. ACM (2019) Martin, N.: How social media has changed how we consume news. In: Forbes (2018). https://www.forbes.com/sites/nicolemartin1/2018/11/30/how-social-media-has-changed-how-we-consume-news/#18ae4c093c3c Ntoutsi, E., Stefanidis, K., Nørvåg, K., Kriegel, H.: Fast group recommendations by applying user clustering. In: Conceptual Modeling—31st International Conference ER 2012 Proceedings, Lecture Notes in Computer Science, vol. 7532, pp. 126–140. Springer (2012) Olsson, T., Huhtamäki, J., Kärkkäinen, H.: Directions for professional social matching systems. Commun. ACM 63(2), 60–69 (2020) Oosterhuis, H., Jagerman, R., de Rijke, M.: Unbiased learning to rank: counterfactual and online approaches. In: Seghrouchni, A.E.F., Sukthankar, G., Liu, T., van Steen, M. (eds.) Companion of The 2020 Web Conference 2020, pp. 299–300. ACM / IW3C2 (2020) du Pin Calmon, F., Wei, D., Vinzamuri, B., Ramamurthy, K.N., Varshney, K.R.: Optimized pre-processing for discrimination prevention. In: Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, pp. 3992–4001 (2017) Pitoura, E.: Social-minded measures of data quality: fairness, diversity, and lack of bias. ACM J. Data Inf. Qual. 12(3), 12:1-12:8 (2020) Plane, A.C., Redmiles, E.M., Mazurek, M.L., Tschantz, M.C.: Exploring user perceptions of discrimination in online targeted advertising. In: 26th USENIX Security Symposium, USENIX Security 2017, pp. 935–951. USENIX Association (2017) Ramakrishnan, R., Gehrke, J.: Database management systems, 3 edn. McGraw-Hill (2003) Rastegarpanah, B., Gummadi, K.P., Crovella, M.: Fighting fire with fire: using antidote data to improve polarization and fairness of recommender systems. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, WSDM 2019, pp. 231–239. ACM (2019) Roy, S.B., Amer-Yahia, S., Chawla, A., Das, G., Yu, C.: Space efficiency in group recommendation. VLDB J. 19(6), 877–900 (2010) Sacharidis, D.: Top-n group recommendations with fairness. In: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, SAC 2019, pp. 1663–1670. ACM (2019) Salimi, B., Rodriguez, L., Howe, B., Suciu, D.: Interventional fairness: causal database repair for algorithmic fairness. In: Proceedings of the 2019 International Conference on Management of Data, SIGMOD Conference 2019, pp. 793–810. ACM (2019) Saxena, N.A., Huang, K., De Filippis, 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) Serbos, D., Qi, S., Mamoulis, N., Pitoura, E., Tsaparas, P.: Fairness in package-to-group recommendations. In: Proceedings of the 26th International Conference on World Wide Web, WWW 2017, pp. 371–379. ACM (2017) Singh, A., Joachims, T.: Fairness of exposure in rankings. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2018, pp. 2219–2228. ACM (2018) Steck, H.: Calibrated recommendations. In: Proceedings of the 12th ACM Conference on Recommender Systems, RecSys 2018, pp. 154–162. ACM (2018) Stoyanovich, J., Howe, B., Abiteboul, S., Miklau, G., Sahuguet, A., Weikum, G.: Fides: towards a platform for responsible data science. In: Proceedings of the 29th International Conference on Scientific and Statistical Database Management, SSDBM 2017, pp. 26:1–26:6. ACM (2017) Stratigi, M., Nummenmaa, J., Pitoura, E., Stefanidis, K.: Fair sequential group recommendations. In: SAC ’20: The 35th ACM/SIGAPP Symposium on Applied Computing, pp. 1443–1452. ACM (2020) Sweeney, L.: Discrimination in online ad delivery. Commun. ACM 56(5), 44–54 (2013) Tijdeman, R.: The chairman assignment problem. Discret. Math. 32(3), 323–330 (1980) Tsioutsiouliklis, S., Pitoura, E., Tsaparas, P., Kleftakis, I., Mamoulis, N.: Fairness-aware pagerank. In: WWW ’21: The Web Conference 2021, Virtual Event/Ljubljana, Slovenia, April 19–23, 2021, pp. 3815–3826. ACM / IW3C2 (2021) Verma, S., Rubin, J.: Fairness definitions explained. In: Proceedings of the International Workshop on Software Fairness, FairWare@ICSE 2018, pp. 1–7. ACM (2018) Weber, L., Dwoskin, E.: Are workplace personality tests fair? Wall Street J. (2014) Xie, M., Lakshmanan, L.V.S., Wood, P.T.: Composite recommendations: from items to packages. Front. Comput. Sci. 6(3), 264–277 (2012) Yang, K., Stoyanovich, J.: Measuring fairness in ranked outputs. In: Proceedings of the 29th International Conference on Scientific and Statistical Database Management, SSDBM 2017, pp. 22:1–22:6. ACM (2017) Yao, S., Huang, B.: Beyond parity: fairness objectives for collaborative filtering. In: Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, pp. 2921–2930 (2017) 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) Zehlike, M., Diehn, G.T., Castillo, C.: Reducing disparate exposure in ranking: a learning to rank approach. In: The Web Conference (WWW). ACM (2020) Zehlike, M., Yang, K., Stoyanovich, J.: Fairness in ranking: a survey. CoRR abs/2103.14000 (2021) Zemel, R.S., Wu, Y., Swersky, K., Pitassi, T., Dwork, C.: Learning fair representations. In: Proceedings of the 30th International Conference on Machine Learning, ICML 2013, JMLR Workshop and Conference Proceedings, vol. 28, pp. 325–333. JMLR.org (2013)