Enhancing Algorithmic Fairness: Integrative Approaches and Multi-Objective Optimization Application in Recidivism Models-Reference-Cited by-同舟云学术

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Enhancing Algorithmic Fairness: Integrative Approaches and Multi-Objective Optimization Application in Recidivism Models

Published:2024-07-30 Issue: Volume:80 Page:1-10
ISSN:
Container-title:Proceedings of the 19th International Conference on Availability, Reliability and Security
language:
Short-container-title:

Author:

Farayola Michael Mayowa¹^ORCID,Bendechache Malika²^ORCID,Saber Takfarinas³^ORCID,Connolly Regina⁴^ORCID,Tal Irina¹^ORCID

Affiliation:

1. Lero Research Centre, School of Computing, Dublin City University, Ireland

2. Lero & ADAPT Research Centres, School of Computer Science, University of Galway, Ireland

3. Lero Reseach Centre, School of Computer Science, University of Galway, Ireland

4. Lero Research Centre, School of Business, Dublin City University, Ireland

Funder

This work was supported in part by the Science Foundation Ireland grants 13/RC/2094\_P2 (Lero) and 13/RC/2106\_P2 (ADAPT) and is co-funded under the European Regional Development Fund (ERDF).

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3664476.3669978

Reference46 articles.

1. One-Network Adversarial Fairness

2. Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, and Hanna Wallach. 2018. A reductions approach to fair classification. In International conference on machine learning. PMLR, 60–69.

3. HLEG AI. 2019. High-level expert group on artificial intelligence. 6 pages.

4. Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner. 2022. Machine bias. In Ethics of data and analytics. Auerbach Publications, 254–264.

5. AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias

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