Year: 2022
Type: article
Abstract: Clear understanding of artificial intelligence (AI) usage risks and how they are being addressed is needed, which requires proper and adequate corporate disclosure. We advance a legal framework for AI Fairness Reporting to which companies can and should adhere on a comply-or-explain basis. We analyse the sources of unfairness arising from different aspects of AI models and the disparities in the performance of machine learning systems. We evaluate how the machine learning literature has sought to address the problem of unfairness through the use of different fairness metrics. We then put forward a nuanced and viable framework for AI Fairness Reporting comprising: (1) disclosure of all machine learning models usage; (2) disclosure of fairness metrics used and the ensuing trade-offs; (3) disclosure of de-biasing methods used; and (d) release of datasets for public inspection or for third-party audit. We then apply this reporting framework to two case studies.
Source: The Cambridge Law Journal
Authors: Jia Qing Yap, Ernest Lim
Institution: National University of Singapore
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $3,255
OpenAlex ID: W4296205861