A General Search-based Framework for Generating Textual Counterfactual Explanations
This work addresses the need for flexible and efficient counterfactual explanation methods in machine learning, though it appears incremental as it builds on existing search-based approaches.
The authors tackled the problem of generating textual counterfactual explanations for machine learning classifiers by proposing a search-based framework that avoids costly retraining associated with generative models, resulting in a model-agnostic, domain-agnostic, and anytime solution.
One of the prominent methods for explaining the decision of a machine-learning classifier is by a counterfactual example. Most current algorithms for generating such examples in the textual domain are based on generative language models. Generative models, however, are trained to minimize a specific loss function in order to fulfill certain requirements for the generated texts. Any change in the requirements may necessitate costly retraining, thus potentially limiting their applicability. In this paper, we present a general search-based framework for generating counterfactual explanations in the textual domain. Our framework is model-agnostic, domain-agnostic, anytime, and does not require retraining in order to adapt to changes in the user requirements. We model the task as a search problem in a space where the initial state is the classified text, and the goal state is a text in a given target class. Our framework includes domain-independent modification operators, but can also exploit domain-specific knowledge through specialized operators. The search algorithm attempts to find a text from the target class with minimal user-specified distance from the original classified object.