SparCAssist

A Model Risk Assessment Assistant Based on Sparse Generated Counterfactuals

Conference paper (2022)

Authors

Zijian Zhang L3S Research Center
Vinay Setty University of Stavanger, L3S Research Center
A. Anand Web Information Systems - EEMCS , L3S Research Center
Research Group
Web Information Systems (EEMCS) (TU Delft)
Copyright: © 2022 Zijian Zhang, Vinay Setty, A. Anand
DOI: https://doi.org/10.1145/3477495.3531677
Interpretable machine learning Counterfactual interpretation Data-annotation tools Human-in-the-loop machine learning
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Published Date

2022

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Software Technology

Research Group

Web Information Systems

Abstract

We introduce SparCAssist, a general-purpose risk assessment tool for the machine learning models trained for language tasks. It evaluates models' risk by inspecting their behavior on counterfactuals, namely out-of-distribution instances generated based on the given data instance. The counterfactuals are generated by replacing tokens in rational subsequences identified by ExPred, while the replacements are retrieved using HotFlip or the Masked-Language-Model-based algorithms. The main purpose of our system is to help the human annotators to assess the model's risk on deployment. The counterfactual instances generated during the assessment are the by-product and can be used to train more robust NLP models in the future.