Debugging Machine Learning Tasks
This addresses the challenge of debugging data errors in machine learning for developers, offering a novel tool for root cause analysis in classification tasks, though it is incremental in applying existing causal theory to a specific domain.
The paper tackles the problem of debugging misclassifications in machine learning tasks caused by errors in training data, proposing an automated root cause analysis method based on Pearl's theory of causation and implementing it in a tool called Psi, which efficiently computes the Probability of Sufficiency to identify data errors in classification tasks.
Unlike traditional programs (such as operating systems or word processors) which have large amounts of code, machine learning tasks use programs with relatively small amounts of code (written in machine learning libraries), but voluminous amounts of data. Just like developers of traditional programs debug errors in their code, developers of machine learning tasks debug and fix errors in their data. However, algorithms and tools for debugging and fixing errors in data are less common, when compared to their counterparts for detecting and fixing errors in code. In this paper, we consider classification tasks where errors in training data lead to misclassifications in test points, and propose an automated method to find the root causes of such misclassifications. Our root cause analysis is based on Pearl's theory of causation, and uses Pearl's PS (Probability of Sufficiency) as a scoring metric. Our implementation, Psi, encodes the computation of PS as a probabilistic program, and uses recent work on probabilistic programs and transformations on probabilistic programs (along with gray-box models of machine learning algorithms) to efficiently compute PS. Psi is able to identify root causes of data errors in interesting data sets.