Human-centered Explainable AI: Towards a Reflective Sociotechnical Approach

By Upol Ehsan, Mark O. Riedl. Interacción, 2020

298 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Safety Research

View Paper: https://www.semanticscholar.org/paper/2c9d71966e1e8a527a392bbe28aa53f8e0918755

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Vision and Pattern Recognition

In Safety Research

Citations in the corpus

Expanding Explainability: Towards Social Transparency in AI systems (2021, background): “Recent work also emphasizes that explainability is an audience-dependant instead of a model-inherent property [7, 8, 26, 70, 73].”
Transitioning to Human Interaction with AI Systems: New Challenges and Opportunities for HCI Professionals to Enable Human-Centered AI (2021, background): “…have also promoted HCAI approach from a variety of perspectives, such as design process (Cerejo, 2021), human-centered explainable AI (Bond et al., 2019; Ehsan & Riedl, 2020), trustworthy AI (He et al., 2021), human-centered machine learning (Kaluarachchi et al., 2021; Sperrle et al., 2021).”
The Metacognitive Demands and Opportunities of Generative AI (2023, background): “Well-adjusted confidence in prompting ability Metacognitive flexibility to adapt prompting strategy”
The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice (2023, background): “Much of the effort branded as Explainable AI falls under this pursuit [e.g., 67, 111, 131 [128, 192, 211].”
Explanations Can Reduce Overreliance on AI Systems During Decision-Making (2022, background): “Ecological Validity: Selecting maze solving trade s off some ecological validity compared to other tasks, as few human-XAI tasks outside of the lab will include solving mazes, but a rich literature in cognitive psychology and cognitive science involves solving puzzles of this sort (e.g., [2, 60]).”
Human-Centered Explainable AI (XAI): From Algorithms to User Experiences (2021, background): “A research community of human-centered XAI [33, 35, 99] has emerged, which bring in cognitive, sociotechnical, design perspectives, and more.”
Six Human-Centered Artificial Intelligence Grand Challenges (2023, background): “For instance, a socio-technical approach in which the technical AI development and the understanding of subconscious and implicit human factors evolve together, has been proposed to incorporate the “ who ” in a systems ’ design (Ehsan & Riedl, 2020).”