Corpus ID: 59336269

A Framework for Understanding Unintended Consequences of Machine Learning

@article{Suresh2019AFF,
  title={A Framework for Understanding Unintended Consequences of Machine Learning},
  author={H. Suresh and J. Guttag},
  journal={ArXiv},
  year={2019},
  volume={abs/1901.10002}
}

H. Suresh, J. Guttag

Published 2019

Computer Science, Mathematics

ArXiv

As machine learning increasingly affects people and society, it is important that we strive for a comprehensive and unified understanding of potential sources of unwanted consequences. For instance, downstream harms to particular groups are often blamed on "biased data," but this concept encompass too many issues to be useful in developing solutions. In this paper, we provide a framework that partitions sources of downstream harm in machine learning into six distinct categories spanning the… CONTINUE READING

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