Fixes That Fail: Self-Defeating Improvements in Machine-Learning Systems

• URL: http://arxiv.org/abs/2103.11766v1
• Date: Mon, 22 Mar 2021 12:29:10 GMT
• Title: Fixes That Fail: Self-Defeating Improvements in Machine-Learning Systems
• Authors: Ruihan Wu, Chuan Guo, Awni Hannun, Laurens van der Maaten
• Abstract summary: We show that improving a machine-learning model can deteriorate the performance of downstream models. We identify different types of entanglement and demonstrate via simple experiments how they can produce self-defeating improvements. We also show that self-defeating improvements emerge in a realistic stereo-based object detection system.
• Score: 31.702684333839585
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. These systems can be represented as directed acyclic graphs in which each vertex is a model, and models feed each other information over the edges. Oftentimes, the models are 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 deteriorate the performance of downstream models, even after those downstream models are retrained. Such self-defeating improvements are the result of entanglement between the models. We identify different types of entanglement and demonstrate via simple experiments how they can produce self-defeating improvements. We also show that self-defeating improvements emerge in a realistic stereo-based object detection system.

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