Web: http://arxiv.org/abs/2007.08911

Jan. 21, 2022, 2:20 a.m. | Ehsan Toreini, Mhairi Aitken, Kovila P. L. Coopamootoo, Karen Elliott, Vladimiro Gonzalez Zelaya, Paolo Missier, Magdalene Ng, Aad van Moorsel

cs.CR updates on arXiv.org arxiv.org

Concerns about the societal impact of AI-based services and systems has
encouraged governments and other organisations around the world to propose AI
policy frameworks to address fairness, accountability, transparency and related
topics. To achieve the objectives of these frameworks, the data and software
engineers who build machine-learning systems require knowledge about a variety
of relevant supporting tools and techniques. In this paper we provide an
overview of technologies that support building trustworthy machine learning
systems, i.e., systems whose properties justify …

learning lg machine machine learning survey technical technologies

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