Communications of the ACM

Letters to the editor

A Case Against Mission-Critical Applications of Machine Learning

By CACM Staff
Communications of the ACM, August 2019, Vol. 62 No. 8, Page 9
10.1145/3332409
Comments

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."

David Lorge Parnas followed up on this discussion in his Letter to the Editor (Feb. 2019), highlighting "the trained network may fail unexpectedly when it encounters data radically different from its training set."

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