April 30, 2024, 4:42 a.m. | Luca Deck, Astrid Schoem\"acker, Timo Speith, Jakob Sch\"offer, Lena K\"astner, Niklas K\"uhl

cs.LG updates on arXiv.org arxiv.org

arXiv:2404.18736v1 Announce Type: new
Abstract: The widespread use of artificial intelligence (AI) systems across various domains is increasingly highlighting issues related to algorithmic fairness, especially in high-stakes scenarios. Thus, critical considerations of how fairness in AI systems might be improved, and what measures are available to aid this process, are overdue. Many researchers and policymakers see explainable AI (XAI) as a promising way to increase fairness in AI systems. However, there is a wide variety of XAI methods and fairness …

abstract ai systems algorithmic fairness artificial artificial intelligence arxiv cs.ai cs.lg domains explainable artificial intelligence fairness fairness in ai highlighting intelligence lifecycle mapping systems type xai

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