Pushing the Limits of Fairness in Algorithmic Decision-Making
Abstract
Designing provably fair decision-making algorithms is a task of growing interest and importance. In this article, I argue that preference-based notions of fairness proposed decades ago in the economics literature and subsequently explored in-depth within computer science (specifically, within the field of computational social choice) are aptly suited for a wide range of modern decision-making systems, from conference peer review to recommender systems to participatory budgeting.
Cite
Text
Shah. "Pushing the Limits of Fairness in Algorithmic Decision-Making." International Joint Conference on Artificial Intelligence, 2023. doi:10.24963/IJCAI.2023/806Markdown
[Shah. "Pushing the Limits of Fairness in Algorithmic Decision-Making." International Joint Conference on Artificial Intelligence, 2023.](https://mlanthology.org/ijcai/2023/shah2023ijcai-pushing/) doi:10.24963/IJCAI.2023/806BibTeX
@inproceedings{shah2023ijcai-pushing,
title = {{Pushing the Limits of Fairness in Algorithmic Decision-Making}},
author = {Shah, Nisarg},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2023},
pages = {7051-7056},
doi = {10.24963/IJCAI.2023/806},
url = {https://mlanthology.org/ijcai/2023/shah2023ijcai-pushing/}
}