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

Ruihan Wu, Chuan Guo, Awni Hannun, Laurens van der Maaten

Keywords: System-level Evaluation, Model Compatibility, Error Decomposition

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. 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 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 in the system. We perform an error decomposition of systems with multiple machine-learning models, which sheds light on the types of errors that can lead to self-defeating improvements. We also present the results of experiments which show that self-defeating improvements emerge in a realistic stereo-based detection system for cars and pedestrians.

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TL;DR: We generally study machine learning models in isolation. But AI systems consist of many machine learning models. Can improving a model make the system worse? The answer is yes.

Code: https://github.com/facebookresearch/self_defeating_improvements