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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.
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Stored only when both papers are in the corpus — 14 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.