Web: http://arxiv.org/abs/2206.11834

June 24, 2022, 1:10 a.m. | A. Feder Cooper, Jonathan Frankle, Christopher De Sa

cs.LG updates on arXiv.org arxiv.org

Legal literature on machine learning (ML) tends to focus on harms, and as a
result tends to reason about individual model outcomes and summary error rates.
This focus on model-level outcomes and errors has masked important aspects of
ML that are rooted in its inherent non-determinism. We show that the effects of
non-determinism, and consequently its implications for the law, instead become
clearer from the perspective of reasoning about ML outputs as probability
distributions over possible outcomes. This distributional viewpoint …

arxiv code ml

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