Error Discovery by Clustering Influence Embeddings

• URL: http://arxiv.org/abs/2312.04712v1
• Date: Thu, 7 Dec 2023 21:42:55 GMT
• Title: Error Discovery by Clustering Influence Embeddings
• Authors: Fulton Wang, Julius Adebayo, Sarah Tan, Diego Garcia-Olano, Narine Kokhlikyan
• Abstract summary: We present a method for identifying groups of test examples -- slices -- on which a model under-performs. We formalize coherence as a key property that any slice discovery method should satisfy. We derive a new slice discovery method, InfEmbed, which satisfies coherence by returning slices whose examples are influenced similarly by the training data.
• Score: 7.27282591214364
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a method for identifying groups of test examples -- slices -- on which a model under-performs, a task now known as slice discovery. We formalize coherence -- a requirement that erroneous predictions, within a slice, should be wrong for the same reason -- as a key property that any slice discovery method should satisfy. We then use influence functions to derive a new slice discovery method, InfEmbed, which satisfies coherence by returning slices whose examples are influenced similarly by the training data. InfEmbed is simple, and consists of applying K-Means clustering to a novel representation we deem influence embeddings. We show InfEmbed outperforms current state-of-the-art methods on 2 benchmarks, and is effective for model debugging across several case studies.

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