Surfacing AI Explainability in Enterprise Product Visual Design to Address User Tech Proficiency Differences - Tandon, Sara; Wang, Jennifer (CHI 2023, 2023)
This case study presents an investigation on explainable artificial intelligence (AI) visualization in business applications. Design guidelines for human-AI interaction are broad and touch on a range of user experiences with AI. Oftentimes, guidelines are not specific to enterprise scenarios with late-stage end users with limited AI knowledge and experience. We present a three-phase study on a visual design of a machine learning (ML) algorithm output. We conducted a user study on an existing design with limited visual AI explanation cues, ran a redesign workshop with various design and data experts, and conducted a reassessment with systematically applied AI explanation guidelines in place. We surface how users with various tech proficiency and AI/ML backgrounds interact with designs and how visual explanation cues increase understanding and effective decision making of users with low AI/ML familiarity. This design process corroborated the application and impact of existing guidelines and surfaced specific design implications for AI explainability within enterprise design.