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A Case Against Mission-Critical Applications of Machine Learning

Journal Article


Abstract


  • IN THEIR COLUMN “Learning

    Machine Learning” (Dec. 2018),

    Ted G. Lewis and Peter J. Denning

    raised a crucial question about

    machine learning systems: “These

    [neural] networks are now used for critical

    functions such as medical diagnosis …

    fire-control systems. How can we trust the

    networks?” They answered: “We know

    that a network is quite reliable when its

    inputs come from its training set. But

    these critical systems will have inputs

    corresponding to new, often unanticipated

    situations. There are numerous

    examples where a network gives poor

    responses for untrained inputs.”

Publication Date


  • 2019

Citation


  • Zhou, Z. Quan. & Sun, L. (2019). A Case Against Mission-Critical Applications of Machine Learning. Communications of the ACM, 62 (8), 9.

Start Page


  • 9

Volume


  • 62

Issue


  • 8

Place Of Publication


  • United States

Abstract


  • IN THEIR COLUMN “Learning

    Machine Learning” (Dec. 2018),

    Ted G. Lewis and Peter J. Denning

    raised a crucial question about

    machine learning systems: “These

    [neural] networks are now used for critical

    functions such as medical diagnosis …

    fire-control systems. How can we trust the

    networks?” They answered: “We know

    that a network is quite reliable when its

    inputs come from its training set. But

    these critical systems will have inputs

    corresponding to new, often unanticipated

    situations. There are numerous

    examples where a network gives poor

    responses for untrained inputs.”

Publication Date


  • 2019

Citation


  • Zhou, Z. Quan. & Sun, L. (2019). A Case Against Mission-Critical Applications of Machine Learning. Communications of the ACM, 62 (8), 9.

Start Page


  • 9

Volume


  • 62

Issue


  • 8

Place Of Publication


  • United States