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Fixes That Fail: Self-Defeating Improvements in Machine-Learning Systems
[article]
2021
arXiv
pre-print
Machine-learning systems such as self-driving cars or virtual assistants are composed of a large number of machine-learning models that recognize image content, transcribe speech, analyze natural language, infer preferences, rank options, etc. Models in these systems are often developed and trained independently, which raises an obvious concern: Can improving a machine-learning model make the overall system worse? We answer this question affirmatively by showing that improving a model can
arXiv:2103.11766v2
fatcat:3srqii5mnzb7fh4h2oyrgj47y4