Exploring Subjective Notions of Explainability through Counterfactual Visualization of Sentiment Analysis

The generation and presentation of counterfactual explanations (CFEs) are a commonly used, model-agnostic, approach to helping end-users reason about the validity of AI/ML model outputs. By demonstrating how sensitive the model's outputs are to minor variations, CFEs are thought to improve understanding of the model's behavior, identify potential biases, and increase the transparency of ‘black box models’. Here, we examine how CFEs support a diverse audience, both with and without technical expertise, to understand the results of an LLM-informed sentiment analysis. We conducted a preliminary pilot study with ten individuals with varied expertise from ranging NLP, ML, and ethics, to specific domains. All individuals were actively using or working with AI/ML technology as part of their daily jobs. Through semi-structured interviews grounded in a set of concrete examples, we examined how CFEs influence participants' perceptions of the model's correctness, fairness, and trust- worthiness, and how visualization of CFEs specifically influences those perceptions. We also surface how participants wrestle with their internal definitions of ‘explainability’, relative to what CFEs present, their cultures, and backgrounds, in addition to the, much more widely studied phenomena, of comparing their baseline expectations of the model's performance. Compared to prior research, our findings highlight the sociotechnical frictions that CFEs surface but do not necessarily remedy. We conclude with the design implications of developing transparent AI/ML visualization systems for more general tasks.