SparCAssist
A Model Risk Assessment Assistant Based on Sparse Generated Counterfactuals
Zijian Zhang (L3S Research Center)
Vinay Setty (L3S Research Center, University of Stavanger)
Avishek Anand (TU Delft - Electrical Engineering, Mathematics and Computer Science, L3S Research Center)
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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.