Exploring Causal Models for Algorithmic Fairness

Project Description

In Simpson's Paradox, conflicting trends from aggregate data can be (mis-)used to advance different (i.e., opposing) arguments. Similarly, gerrymandering employs voter-aggregation techniques to establish an (unfair) advantage of one party over another. More generally, algorithmic bias describes systematic errors in computer-based systems to create unfair outcomes. The goal of this project is to explore the impact of causal models when trying to explain or minimize algorithmic bias. For example, in their 2019 SIGMOD best paper “Interventional fairness: Causal database repair for algorithmic fairness”, Salimi et al.[^SRHS19] considered a causal approach for fair machine learning, reducing it to a database repair problem. In this internship project you will explore different explanatory approaches, including causal models, and develop Jupyter notebooks (in Python) that reveal the different ways data can be used (or misused) to make an argument. To analyze alternative scenarios, you will employ the Possible Worlds Explorer which combines Python data analysis features with a logic programming approach. The resulting notebooks will be shared via the Whole Tale platform.

Necessary Prerequisites

Desirable Skills / Qualifications

Expected Outcomes

Primary Mentors: Bertram Ludäscher


[^SRHS19]: Salimi, B., Rodriguez, L., Howe, B. and Suciu, D., 2019. Interventional fairness: Causal database repair for algorithmic fairness. In Proceedings of the 2019 International Conference on Management of Data (SIGMOD). (download)

[^GCL19]: Gupta, S., Cheng, Y.Y. and Ludäscher, B., 2019. Possible Worlds Explorer: Datalog and Answer Set Programming for the Rest of Us. In Datalog 2.0, 3rd Intl. Workshop on the Resurgence of Datalog in Academia and Industry, Philadelphia Logic Week. CEUR Workshop Proceedings (Vol. 2368, pp. 44-55). CEUR-WS. (download)

[^HEJ14]: Hyttinen, A., Eberhardt, F. and Järvisalo, M., 2014. Constraint-based Causal Discovery: Conflict Resolution with Answer Set Programming. In Conference on Uncertainty in Artificial Intelligence. Quebec, Canada. (download)

[^WT19]: Brinckman, A., Chard, K., Gaffney, N., Hategan, M., Jones, M.B., Kowalik, K., Kulasekaran, S., Ludäscher, B., Mecum, B.D., Nabrzyski, J. and Stodden, V., 2019. Computing environments for reproducibility: Capturing the "Whole Tale". In Future Generation Computer Systems, 94, pp.854-867. (download)