English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

To make Explainable AI (XAI) systems trustworthy, understanding harmfuleffects is just as important as producing well-designed explanations. In thispaper, we address an important yet unarticulated type of negative effect inXAI. We introduce explainability pitfalls(EPs), unanticipated negativedownstream effects from AI explanations manifesting even when there is nointention to manipulate users. EPs are different from, yet related to, darkpatterns, which are intentionally deceptive practices. We articulate theconcept of EPs by demarcating it from dark patterns and highlighting thechallenges arising from uncertainties around pitfalls. We situate andoperationalize the concept using a case study that showcases how, despite bestintentions, unsuspecting negative effects such as unwarranted trust innumerical explanations can emerge. We propose proactive and preventativestrategies to address EPs at three interconnected levels: research, design, andorganizational.

Explainability Pitfalls: Beyond Dark Patterns in Explainable AI