Domino: Discovering Systematic Errors with Cross-Modal EmbeddingsDownload PDF


Sep 29, 2021 (edited Nov 23, 2021)ICLR 2022 Conference Blind SubmissionReaders: Everyone
  • Keywords: robustness, subgroup analysis, error analysis, multimodal, slice discovery
  • Abstract: Machine learning models that achieve high overall accuracy often make systematic errors on important subgroups (or slices) of data. When working with high-dimensional inputs (e.g. images, audio) where important slices are often unlabeled, identifying underperforming slices is a challenging task. In order to address this issue, recent studies have proposed automated slice discovery methods (SDMs), which leverage learned model representations to mine input data for slices on which a model performs poorly. To be useful to a practitioner, these methods must identify slices that are both underperforming and coherent (i.e. united by a human-understandable concept). However, no quantitative evaluation framework currently exists for rigorously assessing SDMs with respect to these criteria. Additionally, prior qualitative evaluations have shown that SDMs often identify slices that are incoherent. In this work, we address these challenges by first designing a principled evaluation framework that enables a quantitative comparison of SDMs across 1,235 slice discovery settings in three input domains (natural images, medical images, and time-series data). Then, motivated by the recent development of powerful cross-modal representation learning approaches, we present Domino, an SDM that leverages cross-modal embeddings and a novel error-aware mixture model to discover and describe coherent slices. We find that Domino accurately identifies 36% of the 1,235 slices in our evaluation framework -- a 12 percentage-point improvement over prior methods. Further, Domino is the first SDM that can generate natural language descriptions of identified slices, correctly outputting the exact name of the slice in 35% of settings.
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