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What Is Wrong with My Model? Identifying Systematic Problems with Semantic Data Slicing

Year: 2024
Type: conference-paper
Abstract: Machine learning models make mistakes, yet sometimes it is difficult to identify the systematic problems behind the mistakes. Practitioners engage in various activities, including error analysis, testing, auditing, and red-teaming, to form hypotheses of what can go (or has gone) wrong with their models. To validate these hypotheses, practitioners employ data slicing to identify relevant examples. However, traditional data slicing is limited by available features and programmatic slicing functions. In this work, we propose SemSlicer, a framework that supports semantic data slicing, which identifies a semantically coherent slice, without the need for existing features. SemSlicer uses Large Language Models to annotate datasets and generate slices from any user-defined slicing criteria. We show that SemSlicer generates accurate slices with low cost, allows flexible trade-offs between different design dimensions, reliably identifies under-performing data slices, and helps practitioners identify useful data slices that reflect systematic problems.
DOI: 10.1145/3691620.3695033
Language: English
Open Access status: hybrid
OpenAlex ID: W4403536571
FWCI: 0.883
Cites: 33
Cited by: 3
Related to: 10