BIAS: Bias and Discrimination in Big Data and Algorithmic Processing. Philosophical Assessments, Legal Dimensions, and Technical Solutions

1. Project details

• Project acronym: BIAS
• Project full name: Bias and Discrimination in Big Data and Algorithmic Processing. Philosophical Assessments, Legal Dimensions, and Technical Solutions
• Funding period: 19.12.2018 — 18.12.2022
• Funding body: Volkswagen foundation
• Homepage: BIAS

2. Involved partners

• Institute of Philosophy, LUH Hannover.
• Institute for Legal Informatics (IRI), LUH Hannover.
• L3S Research Center, Hannover.
• Institute for Information Processing (TNT), LUH Hannover.

3. Team

• Prof. Dr. Eirini Ntoutsi (PI)
• MSc. Arjun Roy (Ph.D. student)

4. Project overview

BIAS is an interfaculty research initiative composed of experts from philosophy, law, and computer science, bringing together epistemological, ethical, legal and technical perspectives. Our shared research question is: How can we ensure that big data analysis and algorithm-based decision-making are unbiased and nondiscriminatory? To this end, we provide philosophical analyses of relevant concepts and principles, we investigate their utilisation in pertinent legal frameworks, and we develop technical solutions such as debiasing strategies and discrimination detection procedures.

5. Overview of our contributions

Within BIAS, our group focuses on the following topics:

• Computer bias, esp. caused by imbalanced data or rare classes.
• Statistical fairness, esp. for non-stationary data.
• Debiasing strategies, esp. focusing on explainability.

6. Publications

• Roy, A., & Ntoutsi, E. (2022). Learning to Teach Fairness-aware Deep Multi-task Learning . Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD).
• Roy, A., Iosifidis, V., & Ntoutsi, E. (2022). Multi-fairness under class-imbalance . Proceedings of the 25th International Conference on Discovery Science (DS 2022).
• Le Quy, T., Roy, A., Iosifidis, V., Zhang, W., & Ntoutsi, E. (2022). A survey on datasetsfor fairness-aware machine learning.Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery,e1452.https://doi.org/10.1002/widm.1452
• Iosifidis, V., Roy, A. & Ntoutsi, E. (2022). Parity-based cumulative fairness-aware boosting. Knowledge and Information Systems 64, 2737–2770. https://doi.org/10.1007/s10115-022-01723-3.
• Le Quy, T., Roy, A., Friege, G., & Ntoutsi, E. (2021). Fair-capacitated clustering. In Proceedings of The 14th International Conference on Educational Data Mining (EDM21) (pp. 407-414).
• Cai, Y., Zimek, A., & Ntoutsi, E. (2021, October). XPROAX-Local explanations for text classification with progressive neighborhood approximation. In 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA) (pp. 1-10). IEEE.
• Naumann, P., & Ntoutsi, E. (2021, September). Consequence-aware Sequential Counterfactual Generation. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 682-698). Springer, Cham.
• Hu, T., Iosifidis, V., Liao, W., Zhang, H., Yang, M. Y., Ntoutsi, E., & Rosenhahn, B. (2020, October). FairNN - Conjoint learning of fair representations for fair decisions. In International Conference on Discovery Science (pp. 581-595). Springer, Cham.
• Ntoutsi, E., Fafalios, P., Gadiraju, U., Iosifidis, V., Nejdl, W., Vidal, M. E., … & Staab, S. (2020). Bias in data‐driven artificial intelligence systems—An introductory survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(3), e1356.
• Zhang, W., & Ntoutsi, E. (2019, August). FAHT: an adaptive fairness-aware decision tree classifier . In Proceedings of the 28th International Joint Conference on Artificial Intelligence (pp. 1480-1486).
• Iosifidis, V., & Ntoutsi, E. (2019, November). AdaFair: Cumulative fairness adaptive boosting. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (pp. 781-790).
• Iosifidis, V., Fetahu, B., & Ntoutsi, E. (2019, December). FAE: A fairness-aware ensemble framework. In 2019 IEEE International Conference on Big Data (Big Data) (pp. 1375-1380). IEEE.
• Iosifidis, V., Tran, T. N. H., & Ntoutsi, E. (2019, August). Fairness-enhancing interventions in stream classification. In International Conference on Database and Expert Systems Applications (pp. 261-276). Springer, Cham.
• Iosifidis, V., & Ntoutsi, E. (2018). Dealing with bias via data augmentation in supervised learning scenarios. BIAS workshop in conjunction with iConference.

7. Highlights

• Arjun Roy gave a talk on the topic “Multi-dimensional Discrimination: An essential but overlooked Legal and Algorithmic Challenge” at the Herrenhausen Conference - AI and the future of societies organised by Volkswagen foundation on 12th Oct, 2022 in Hannover, Germany (YouTube video to be uploaded soon).
• Arjun Roy presented our paper “Multi-fairness under class-imbalance” at the Discovery Science conference on 11th Oct, 2022 in Montpellier, France (YouTube Link).
• Arjun Roy presented our paper “Learning to Teach Fairness-Aware Deep Multi-Task Learning” at the ECMLPKDD 22 on 22nd Sept, 2022 in Grenoble, France (YouTube Link).