Abstract
Algorithmic fairness is an important aspect when data is used for predictive purposes. This paper analyses the sources of discrimination, and proposes sufficient conditions for building non-discriminatory/fair predictive models in the presence of context attributes. The paper then uses real world datasets to demonstrate the existence and the extent of discrimination in applications and the independence between the discrimination of datasets and the discrimination of classification models.
Granted by: 61472166 of National Natural Science Foundation China; ARC DP200101210 Fairness Aware Data Mining For Discrimination Free Decisions, Australia.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The author wishes to acknowledge the statistical office that provided the underlying data making this research possible: Statistics Canada.
References
Calders, T., Verwer, S.: Three Naive Bayes approaches for discrimination-free classification. Data Min. Knowl. Discov. 21(2), 277–292 (2010)
Friedler, S.A., Scheidegger, C., Venkatasubramanian, S., Choudhary, S., Hamilton, E.P., Roth, D.: A comparative study of fairness-enhancing interventions in machine learning. arxiv:1802.04422 (2018)
Hardt, M., Price, E., Srebro, N.: Equality of opportunity in supervised learning. In: Advances in Neural Information Processing Systems (2016)
Larson, J., Mattu, S., Kirchner, L., Angwin, J.: How we analyzed the COMPAS recidivism algorithm. https://www.propublica.org/ (2016)
Madras, D., Creager, E., Pitassi, T., Zemel, R.: Fairness through causal awareness: learning causal latent-variable models for biased data. In: FAT* (2019)
Minnesota, Population, and Center. Integrated public use microdata series, international: Version 7.0 [dataset]. minneapolis, mn: Ipums, 2018. IPUMS (2018). https://doi.org/10.18128/D020.V7.0
Pleiss, G., Raghavan, M., Wu, F., Kleinberg, J., Weinberger, K.Q.: On fairness and calibration. In: NIPS (2017)
Speicher, T., et al.: A unified approach to quantifying algorithmic unfairness: Measuring individual & group unfairness via inequality indices. In: KDD (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Liu, J. et al. (2020). Building Fair Predictive Models. In: Gallagher, M., Moustafa, N., Lakshika, E. (eds) AI 2020: Advances in Artificial Intelligence. AI 2020. Lecture Notes in Computer Science(), vol 12576. Springer, Cham. https://doi.org/10.1007/978-3-030-64984-5_17
Download citation
DOI: https://doi.org/10.1007/978-3-030-64984-5_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-64983-8
Online ISBN: 978-3-030-64984-5
eBook Packages: Computer ScienceComputer Science (R0)