Case Against Mission Critical Machine Learning

Good caution, like with any kind of automation.

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

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."

We wish to point out that machine learning-based systems, including commercial ones performing safety critical tasks, can fail not only under "unanticipated situations" (noted by Lewis and Denning) or "when it encounters data radically different from its training set" (noted by Parnas), but also under normal situations, even on data that is extremely similar to its training set.

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