Advertisement

Counterfactually Fair Prediction Using Multiple Causal Models

  • Fabio Massimo ZennaroEmail author
  • Magdalena Ivanovska
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11450)

Abstract

In this paper we study the problem of making predictions using multiple structural causal models defined by different agents, under the constraint that the prediction satisfies the criterion of counterfactual fairness. Relying on the frameworks of causality, fairness and opinion pooling, we build upon and extend previous work focusing on the qualitative aggregation of causal Bayesian networks and causal models. In order to complement previous qualitative results, we devise a method based on Monte Carlo simulations. This method enables a decision-maker to aggregate the outputs of the causal models provided by different agents while guaranteeing the counterfactual fairness of the result. We demonstrate our approach on a simple, yet illustrative, toy case study.

Keywords

Causality Structural causal networks Fairness Counterfactual fairness Opinion pooling Judgement aggregation Monte Carlo sampling 

References

  1. 1.
    Alrajeh, D., Chockler, H., Halpern, J.Y.: Combining experts’ causal judgments (2018)Google Scholar
  2. 2.
    Barabas, C., Dinakar, K., Virza, J.I., Zittrain, J., et al.: Interventions over predictions: reframing the ethical debate for actuarial risk assessment. arXiv preprint arXiv:1712.08238 (2017)
  3. 3.
    Bradley, R., Dietrich, F., List, C.: Aggregating causal judgments. Philos. Sci. 81(4), 491–515 (2014)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Dietrich, F., List, C., Hájek, A., Hitchcock, C.: Probabilistic Opinion Pooling: Oxford Handbook of Probability and Philosophy. Oxford University Press, Oxford (2016)Google Scholar
  5. 5.
    Gajane, P.: On formalizing fairness in prediction with machine learning. arXiv preprint arXiv:1710.03184 (2017)
  6. 6.
    Grossi, D., Pigozzi, G.: Judgment aggregation: a primer. Synth. Lect. Artif. Intell. Mach. Learn. 8(2), 1–151 (2014)CrossRefGoogle Scholar
  7. 7.
    Kusner, M.J., Loftus, J., Russell, C., Silva, R.: Counterfactual fairness. In: Advances in Neural Information Processing Systems, pp. 4069–4079 (2017)Google Scholar
  8. 8.
    MacKay, D.J.: Information Theory, Inference and Learning Algorithms. Cambridge University Press, Cambridge (2003)zbMATHGoogle Scholar
  9. 9.
    Pearl, J.: Causality. Cambridge University Press, Cambridge (2009)CrossRefGoogle Scholar
  10. 10.
    Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Elsevier, Amsterdam (2014)zbMATHGoogle Scholar
  11. 11.
    Russell, C., Kusner, M.J., Loftus, J., Silva, R.: When worlds collide: integrating different counterfactual assumptions in fairness. In: Advances in Neural Information Processing Systems, pp. 6417–6426 (2017)Google Scholar
  12. 12.
    Tran, D., Kucukelbir, A., Dieng, A.B., Rudolph, M., Liang, D., Blei, D.M.: Edward: a library for probabilistic modeling, inference, and criticism. arXiv preprint arXiv:1610.09787 (2016)
  13. 13.
    Zennaro, F.M., Ivanovska, M.: Pooling of causal models under counterfactual fairness via causal judgement aggregation. ICML/IJCAI/AAMAS Workshop on Machine Learning for Causal Inference, Counterfactual Prediction, and Autonomous Action. arXiv preprint arXiv:1805.09866 (2018)
  14. 14.
    Zliobaite, I.: A survey on measuring indirect discrimination in machine learning. arXiv preprint arXiv:1511.00148 (2015)

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of InformaticsUniversity of OsloOsloNorway

Personalised recommendations