The AI-DEC: A Card-based Design Method for User-centered AI Explanations
arXiv:2405.16711 · doi:10.1145/3643834.3661576
Abstract
Increasing evidence suggests that many deployed AI systems do not sufficiently support end-user interaction and information needs. Engaging end-users in the design of these systems can reveal user needs and expectations, yet effective ways of engaging end-users in the AI explanation design remain under-explored. To address this gap, we developed a design method, called AI-DEC, that defines four dimensions of AI explanations that are critical for the integration of AI systems -- communication content, modality, frequency, and direction -- and offers design examples for end-users to design AI explanations that meet their needs. We evaluated this method through co-design sessions with workers in healthcare, finance, and management industries who regularly use AI systems in their daily work. Findings indicate that the AI-DEC effectively supported workers in designing explanations that accommodated diverse levels of performance and autonomy needs, which varied depending on the AI system's workplace role and worker values. We discuss the implications of using the AI-DEC for the user-centered design of AI explanations in real-world systems.
References in corpus (18)
- Training language models to follow instructions with human feedback
- Explaining Explanations in AI
- Questioning the AI: Informing Design Practices for Explainable AI User Experiences
- Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making
- Expanding Explainability: Towards Social Transparency in AI systems
- Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)
- Conceptual Metaphors Impact Perceptions of Human-AI Collaboration
- Understanding Large-Language Model (LLM)-powered Human-Robot Interaction
- Understanding Frontline Workers' and Unhoused Individuals' Perspectives on AI Used in Homeless Services
- Imagining new futures beyond predictive systems in child welfare: A qualitative study with impacted stakeholders
- Towards Relatable Explainable AI with the Perceptual Process
- How Case Based Reasoning Explained Neural Networks: An XAI Survey of Post-Hoc Explanation-by-Example in ANN-CBR Twins
- Lessons Learned from Designing an AI-Enabled Diagnosis Tool for Pathologists
- Telling Stories from Computational Notebooks: AI-Assisted Presentation Slides Creation for Presenting Data Science Work
- Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes
- "It's Not a Replacement:" Enabling Parent-Robot Collaboration to Support In-Home Learning Experiences of Young Children
- Explanation Strategies as an Empirical-Analytical Lens for Socio-Technical Contextualization of Machine Learning Interpretability
- Investigating the Day-to-Day Experiences of Users with Traumatic Brain Injury with Conversational Agents