@inbook{bdaddb05d923458c96921f008838c1c3,
title = "Assessing and Enforcing Fairness in the AI Lifecycle",
abstract = "A significant challenge in detecting and mitigating bias is creating a mindset amongst AI developers to address unfairness. The current literature on fairness is broad, and the learning curve to distinguish where to use existing metrics and techniques for bias detection or mitigation is difficult. This survey systematises the state-of-the-art about distinct notions of fairness and relative techniques for bias mitigation according to the AI lifecycle. Gaps and challenges identified during the development of this work are also discussed.",
author = "Roberta Calegari and Casta{\~n}{\'e}, \{Gabriel G.\} and Michela Milano and Barry O'Sullivan",
note = "Publisher Copyright: {\textcopyright} 2023 International Joint Conferences on Artificial Intelligence. All rights reserved.; 32nd International Joint Conference on Artificial Intelligence, IJCAI 2023 ; Conference date: 19-08-2023 Through 25-08-2023",
year = "2023",
doi = "10.24963/ijcai.2023/735",
language = "English",
series = "IJCAI International Joint Conference on Artificial Intelligence",
publisher = "International Joint Conferences on Artificial Intelligence",
pages = "6554--6562",
editor = "Edith Elkind",
booktitle = "Proceedings of the 32nd International Joint Conference on Artificial Intelligence, IJCAI 2023",
address = "United States",
}