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International Conference on Case-Based Reasoning

ICCBR 2022: Case-Based Reasoning Research and Development pp 48–62Cite as

Algorithmic Bias and Fairness in Case-Based Reasoning

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13405))

Abstract

Algorithmic bias due to underestimation refers to situations where an algorithm under-predicts desirable outcomes for a protected minority. In this paper we show how this can be addressed in a case-based reasoning (CBR) context by a metric learning strategy that explicitly considers bias/fairness. Since one of the advantages CBR has over alternative machine learning approaches is interpretability, it is interesting to see how much this metric learning distorts the case-retrieval process. We find that bias is addressed with a minimum impact on case-based predictions - little more than the predictions that need to be changed are changed. However, the effect on explanation is more significant as the case-retrieval order is impacted.

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Notes

  1. 1.

    https://github.com/williamblanzeisky/Algorithmic-Bias-and-Fairness-in-Case-Based-Reasoning.

  2. 2.

    https://scikit-learn.org/.

References

  1. Montalvo, I., Izquierdo, J., Pérez-García, R., Herrera, M.: Improved performance of PSO with self-adaptive parameters for computing the optimal design of water supply systems. Eng. Appl. Artif. Intell. 23(5), 727–735 (2010). Advances in metaheuristics for hard optimization: new trends and case studies

    Google Scholar 

  2. Žliobaitė, I.: Measuring discrimination in algorithmic decision making. Data Min. Knowl. Discov. 31(4), 1060–1089 (2017). https://doi.org/10.1007/s10618-017-0506-1

    Article  MathSciNet  Google Scholar 

  3. Bender, E.M., Gebru, T., McMillan-Major, A., Shmitchell, S.: On the dangers of stochastic parrots: can language models be too big? In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2021, pp. 610–623. Association for Computing Machinery, New York (2021)

    Google Scholar 

  4. Berk, R., Heidari, H., Jabbari, S., Kearns, M., Roth, A.: Fairness in criminal justice risk assessments: the state of the art. Sociol. Methods Res. 50(1), 3–44 (2021)

    Article  MathSciNet  Google Scholar 

  5. Blanzeisky, W., Cunningham, P.: Algorithmic factors influencing bias in machine learning. In: Kamp, M. (ed.) Machine Learning and Principles and Practice of Knowledge Discovery in Databases, pp. 559–574. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-93736-2_41

    Chapter  Google Scholar 

  6. Blanzeisky, W., Cunningham, P., Kennedy, K.: Introducing a family of synthetic datasets for research on bias in machine learning. CoRR abs/2107.08928 (2021)

    Google Scholar 

  7. Castelnovo, A., Crupi, R., Greco, G., Regoli, D.: The zoo of fairness metrics in machine learning (2021)

    Google Scholar 

  8. Caton, S., Haas, C.: Fairness in machine learning: a survey. arXiv preprint arXiv:2010.04053 (2020)

  9. Coello, C., Lechuga, M.: MOPSO: a proposal for multiple objective particle swarm optimization, vol. 2, pp. 1051–1056 (2002)

    Google Scholar 

  10. Corbett-Davies, S., Goel, S.: The measure and mismeasure of fairness: a critical review of fair machine learning (2018)

    Google Scholar 

  11. Dodge, J., Liao, Q.V., Zhang, Y., Bellamy, R.K., Dugan, C.: Explaining models: an empirical study of how explanations impact fairness judgment. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 275–285 (2019)

    Google Scholar 

  12. Dressel, J., Farid, H.: The accuracy, fairness, and limits of predicting recidivism. Sci. Adv. 4(1), eaao5580 (2018)

    Google Scholar 

  13. Emigh, M.S., Kriminger, E.G., Brockmeier, A.J., Príncipe, J.C., Pardalos, P.M.: Reinforcement learning in video games using nearest neighbor interpolation and metric learning. IEEE Trans. Comput. Intell. AI Games 8(1), 56–66 (2014)

    Article  Google Scholar 

  14. 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 (2015)

    Google Scholar 

  15. Holstein, K., Wortman Vaughan, J., Daumé III, H., Dudik, M., Wallach, H.: Improving fairness in machine learning systems: what do industry practitioners need? In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, pp. 1–16 (2019)

    Google Scholar 

  16. Jalali, V., Leake, D.: Harnessing hundreds of millions of cases: case-based prediction at industrial scale. In: Cox, M.T., Funk, P., Begum, S. (eds.) ICCBR 2018. LNCS (LNAI), vol. 11156, pp. 153–169. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01081-2_11

    Chapter  Google Scholar 

  17. Kamishima, T., Akaho, S., Asoh, H., Sakuma, J.: Fairness-aware classifier with prejudice remover regularizer. In: Flach, P.A., De Bie, T., Cristianini, N. (eds.) ECML PKDD 2012. LNCS (LNAI), vol. 7524, pp. 35–50. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33486-3_3

    Chapter  Google Scholar 

  18. Keane, M.T., Kenny, E.M.: How case-based reasoning explains neural networks: a theoretical analysis of XAI using Post-Hoc explanation-by-example from a survey of ANN-CBR twin-systems. In: Bach, K., Marling, C. (eds.) ICCBR 2019. LNCS (LNAI), vol. 11680, pp. 155–171. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29249-2_11

    Chapter  Google Scholar 

  19. Kennedy, J.: Particle Swarm Optimization. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of Machine Learning, pp. 760–766. Springer, Boston (2010). https://doi.org/10.1007/978-0-387-30164-8_630

    Chapter  Google Scholar 

  20. Kohavi, R.: Scaling up the accuracy of Naive-Bayes classifiers: a decision-tree hybrid. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, vol. 96, pp. 202–207 (1996)

    Google Scholar 

  21. Larsen, R., Jouffroy, J., Lassen, B.: On the premature convergence of particle swarm optimization (2016). https://doi.org/10.1109/ECC.2016.7810572

  22. Limlawan, V., Pongchairerks, P.: A PSO with ability to avoid being trapped in a local optimum a PSO with ability to avoid being trapped in a local optimum (2010)

    Google Scholar 

  23. Lin, Y.B., Ping, X.O., Ho, T.W., Lai, F.: Processing and analysis of imbalanced liver cancer patient data by case-based reasoning. In: The 7th 2014 Biomedical Engineering International Conference, pp. 1–5. IEEE (2014)

    Google Scholar 

  24. Ma, R.J., Yu, N.Y., Hu, J.Y.: Application of particle swarm optimization algorithm in the heating system planning problem. Sci. World J. 2013, 718345 (2013)

    Google Scholar 

  25. Malof, J.M., Mazurowski, M.A., Tourassi, G.D.: The effect of class imbalance on case selection for case-based classifiers: an empirical study in the context of medical decision support. Neural Netw. 25, 141–145 (2012)

    Article  Google Scholar 

  26. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., Galstyan, A.: A survey on bias and fairness in machine learning. ACM Comput. Surv. 54(6), 1–35 (2021). https://doi.org/10.1145/3457607

    Article  Google Scholar 

  27. Moslehi, F., Haeri, A., Martínez-Álvarez, F.: A novel hybrid GA-PSO framework for mining quantitative association rules. Soft Comput. 24, 4645–4666 (2020)

    Article  Google Scholar 

  28. Nicolàs Sans, R., Vernet Bellet, D., Golobardes, E., Fornells Herrera, A., de la Torre Frade, F., Puig, S.: Applying distance metric learning in a collaborative melanoma diagnosis system with case-based reasoning. In: Proceedings of the 14th Workshop on Case-based reasoning at the 29th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence (2011)

    Google Scholar 

  29. Ontañón, S., Plaza, E.: Cooperative case bartering for case-based reasoning agents. In: Escrig, M.T., Toledo, F., Golobardes, E. (eds.) CCIA 2002. LNCS (LNAI), vol. 2504, pp. 294–308. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-36079-4_26

    Chapter  Google Scholar 

  30. Richter, M.M., Michael, M.: Knowledge containers. In: Readings in Case-Based Reasoning (2003)

    Google Scholar 

  31. Roth-Berghofer, T.R.: Explanations and case-based reasoning: foundational issues. In: Funk, P., González Calero, P.A. (eds.) ECCBR 2004. LNCS (LNAI), vol. 3155, pp. 389–403. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-28631-8_29

    Chapter  Google Scholar 

  32. Sørmo, F., Cassens, J., Aamodt, A.: Explanation in case-based reasoning-perspectives and goals. Artif. Intell. Rev. 24(2), 109–143 (2005)

    Article  Google Scholar 

  33. Wang, F., Sun, J.: Survey on distance metric learning and dimensionality reduction in data mining. Data Min. Knowl. Disc. 29(2), 534–564 (2014). https://doi.org/10.1007/s10618-014-0356-z

    Article  MathSciNet  MATH  Google Scholar 

  34. Weinberger, K.Q., Saul, L.K.: Distance metric learning for large margin nearest neighbor classification. J. Mach. Learn. Res. 10(2) (2009)

    Google Scholar 

  35. Woodworth, B., Gunasekar, S., Ohannessian, M.I., Srebro, N.: Learning non-discriminatory predictors. In: Conference on Learning Theory, pp. 1920–1953. PMLR (2017)

    Google Scholar 

  36. Xing, E., Jordan, M., Russell, S.J., Ng, A.: Distance metric learning with application to clustering with side-information. In: Advances in Neural Information Processing Systems 15 (2002)

    Google Scholar 

  37. Zliobaite, I.: Fairness-aware machine learning: a perspective. arXiv preprint arXiv:1708.00754 (2017)

  38. Ünal, A., Kayakutlu, G.: Multi-objective particle swarm optimization with random immigrants. Complex Intell. Syst. 6(3), 635–650 (2020). https://doi.org/10.1007/s40747-020-00159-y

    Article  Google Scholar 

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Acknowledgements

This work was funded by Science Foundation Ireland through the SFI Centre for Research Training in Machine Learning (Grant No. 18/CRT/6183) with support from Microsoft Ireland.

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Authors and Affiliations

  1. School of Computer Science, University College Dublin, Dublin 4, Ireland

    William Blanzeisky, Barry Smyth & Pádraig Cunningham

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  1. William Blanzeisky
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  2. Barry Smyth
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  3. Pádraig Cunningham
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Correspondence to William Blanzeisky .

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Editors and Affiliations

  1. University College Dublin, Dublin, Ireland

    Mark T. Keane

  2. The Robert Gordon University, Aberdeen, UK

    Nirmalie Wiratunga

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Blanzeisky, W., Smyth, B., Cunningham, P. (2022). Algorithmic Bias and Fairness in Case-Based Reasoning. In: Keane, M.T., Wiratunga, N. (eds) Case-Based Reasoning Research and Development. ICCBR 2022. Lecture Notes in Computer Science(), vol 13405. Springer, Cham. https://doi.org/10.1007/978-3-031-14923-8_4

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  • DOI: https://doi.org/10.1007/978-3-031-14923-8_4

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