Generating Plausible Counterfactual Explanations for Deep Transformers in Financial Text Classification
This addresses the need for trustworthy AI in sensitive financial domains like M&A, where current XAI methods often yield implausible explanations, though it is incremental as it builds on existing counterfactual and adversarial training techniques.
The paper tackles the problem of generating plausible counterfactual explanations for deep transformers in financial text classification, specifically for corporate M&A, and shows that their method improves model accuracy compared to state-of-the-art and human performance while producing significantly more plausible explanations based on human trials.
Corporate mergers and acquisitions (M&A) account for billions of dollars of investment globally every year, and offer an interesting and challenging domain for artificial intelligence. However, in these highly sensitive domains, it is crucial to not only have a highly robust and accurate model, but be able to generate useful explanations to garner a user's trust in the automated system. Regrettably, the recent research regarding eXplainable AI (XAI) in financial text classification has received little to no attention, and many current methods for generating textual-based explanations result in highly implausible explanations, which damage a user's trust in the system. To address these issues, this paper proposes a novel methodology for producing plausible counterfactual explanations, whilst exploring the regularization benefits of adversarial training on language models in the domain of FinTech. Exhaustive quantitative experiments demonstrate that not only does this approach improve the model accuracy when compared to the current state-of-the-art and human performance, but it also generates counterfactual explanations which are significantly more plausible based on human trials.