Explainability pitfalls: Beyond dark patterns in explainable AI

Abstract

To make explainable artificial intelligence (XAI) systems trustworthy, understanding harmful effects is important. In this paper, we address an important yet unarticulated type of negative effect in XAI. We introduce explainability pitfalls (EPs), unanticipated negative downstream effects from AI explanations manifesting even when there is no intention to manipulate users. EPs are different from dark patterns, which are intentionally deceptive practices. We articulate the concept of EPs by demarcating it from dark patterns and highlighting the challenges arising from uncertainties around pitfalls. We situate and operationalize the concept using a case study that showcases how, despite best intentions, unsuspecting negative effects, such as unwarranted trust in numerical explanations, can emerge. We propose proactive and preventative strategies to address EPs at three interconnected levels: research, design, and organizational. We discuss design and societal implications around reframing AI adoption, recalibrating stakeholder empowerment, and resisting the "move fast and break things" mindset.

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