A Framework for Understanding Unintended Consequences of. . As machine learning increasingly affects people and society, it is important that we strive for a comprehensive and unified understanding of how and why unwanted consequences arise. 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.
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Abstract. As machine learning increasingly affects people and society, it is important that we strive for a comprehensive and unified understanding of how and why.
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For our February 2020 Meetup we had a series of talks on papers covered in local reading groups. We had four presenters sharing their synopsis and review on.
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A Framework for Understanding Unintended Consequences of Machine Learning, by Harini Suresh and John V. Guttag Original Abstract. As machine learning.
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A Framework for Understanding Unintended Consequences of Machine Learning Harini Suresh MIT hsuresh@mit.edu John V. Guttag MIT guttag@mit.edu Abstract As machine.
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A Framework For Understanding Sources of Unintended Consequences in Machine Learning. This paper provides six sources of harm spanning from data to model in machine.
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As machine learning increasingly affects people and society, it is important that we strive for a comprehensive and unified understanding of how and why unwanted consequences arise..
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As machine learning increasingly affects people and society, it is important that we strive for a comprehensive and unified understanding of how and why unwanted.
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Figure 1: (a) The data generation process begins with data collection from the world. This process involves both sampling from a population and identifying which features.
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These are heady times in machine learning and artificial intelligence; new algorithms, TensorFlow, and clusters of powerful GPU’s are combining to produce powerful.
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This paper provides a framework that partitions sources of downstream harm in machine learning into six distinct categories spanning the data generation and machine.
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MLGdańsk #932020.10.05Robert RóżańskiA Framework for Understanding Unintended Consequences of Machine Learningslajdy:https://github.com/mlgdansk/meetings/blo...
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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.
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Data Ethics Week 6 Reading Summaries framework for understanding unintended consequences of machine learning john guttag, harini suresh abstract it is important.
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As usual, we have our DALab meeting today. I presented Suresh’s paper [1] related to bias in the ML process. Firstly, we talked about the bias occurring in the ML, and how to.
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