 A model of fair and explainable artificial intelligenceAmy Wenxuan Ding ()
Post-Print from HAL Abstract: Artificial intelligence (AI) is making more and more algorithmic decisions for humans. However, the "intelligence" function in AI relies heavily on learning technologies, which suffer two major flaws leading to legal and technical challenges: potentially discriminative/biased decisions, and unable to explain why and how a machine makes such decisions. Therefore, building a fair and explainable AI model is important and urgent. This article presents a novel theory-based individual-level dynamic learning method that performs learning using data of an individual subject without employing others' information, and identifies causal mechanism from unobserved data generating process that each subject exhibits. Thus, data selection bias is avoided and a fair and interpretable decision is achieved. We empirically test our method using a real-world dataset on risk assessment for lending decisions. Our results show that the proposed method outperforms conventional learning methods in terms of fairness in treating data subjects, decision accuracy and interpretability. Keywords: discrimination; Explainability; Artificial intelligence; Responsible; fairness (search for similar items in EconPapers) Published in Artificial Intelligence for Sustainable Value Creation, Edward Elgar, pp.122 - 150, 2021, ⟨10.4337/9781839104398.00017⟩ There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05626249 DOI: 10.4337/9781839104398.00017 Access Statistics for this paper More papers in Post-Print from HAL |
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