Assessing and Enforcing Fairness in the AI Lifecycle

Roberta Calegari; Gabriel G. Castañé; Michela Milano; Barry O'Sullivan

IJCAI 2023poster7 citations

Assessing and Enforcing Fairness in the AI Lifecycle

Roberta Calegari, Gabriel G. Castañé, Michela Milano, Barry O'Sullivan

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.

Survey: AI Ethics, Trust, FairnessSurvey: Machine LearningSurvey: Humans and AI

BibTeX

@inproceedings{ijcai2023p735,
  title     = {Assessing and Enforcing Fairness in the AI Lifecycle},
  author    = {Calegari, Roberta and G. Castañé, Gabriel and Milano, Michela and O'Sullivan, Barry},
  booktitle = {Proceedings of the Thirty-Second International Joint Conference on
               Artificial Intelligence, {IJCAI-23}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Edith Elkind},
  pages     = {6554--6562},
  year      = {2023},
  month     = {8},
  note      = {Survey Track},
  doi       = {10.24963/ijcai.2023/735},
  url       = {https://doi.org/10.24963/ijcai.2023/735},
}