Human-centered Explainable AI: Towards a Reflective Sociotechnical Approach
This work addresses the problem of improving AI transparency and usability for diverse stakeholders in sociotechnical systems, though it is incremental in building on existing HCI and XAI approaches.
The paper tackles the challenge of making AI systems more accessible and interpretable by introducing Human-centered Explainable AI (HCXAI), which focuses on integrating human values and social contexts into explanation design, as demonstrated through a case study for non-technical end-users.
Explanations--a form of post-hoc interpretability--play an instrumental role in making systems accessible as AI continues to proliferate complex and sensitive sociotechnical systems. In this paper, we introduce Human-centered Explainable AI (HCXAI) as an approach that puts the human at the center of technology design. It develops a holistic understanding of "who" the human is by considering the interplay of values, interpersonal dynamics, and the socially situated nature of AI systems. In particular, we advocate for a reflective sociotechnical approach. We illustrate HCXAI through a case study of an explanation system for non-technical end-users that shows how technical advancements and the understanding of human factors co-evolve. Building on the case study, we lay out open research questions pertaining to further refining our understanding of "who" the human is and extending beyond 1-to-1 human-computer interactions. Finally, we propose that a reflective HCXAI paradigm-mediated through the perspective of Critical Technical Practice and supplemented with strategies from HCI, such as value-sensitive design and participatory design--not only helps us understand our intellectual blind spots, but it can also open up new design and research spaces.