Human-Centered Explainable AI: Towards a Reflective Sociotechnical Approach

Ehsan, Upol; Riedl, Mark O.

doi:10.1007/978-3-030-60117-1_33

Cited by 124 publications

(51 citation statements)

References 24 publications

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“…Hence there are rich opportunities for HCI researchers and design practitioners to contribute insights, solutions, and methods to make AI more explainable. A research community of human-centered XAI [33,35,99] has emerged, which bring in cognitive, sociotechnical, design perspectives, and more. We hope this chapter serves as a call to engagement in this inter-disciplinary endeavor by presenting a selected overview of recent AI and HCI work on the topic of XAI.…”

Section: Introductionmentioning

confidence: 99%

Human-Centered Explainable AI (XAI): From Algorithms to User Experiences

Liao¹,

Varshney²

2021

Preprint

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Book Chapter Draft 10/2021) As a technical sub-field of artificial intelligence (AI), explainable AI (XAI) has produced a vast collection of algorithms, providing a toolbox for researchers and practitioners to build XAI applications. With the rich application opportunities, explainability has moved beyond a demand by data scientists or researchers to comprehend the models they are developing, to become an essential requirement for people to trust and adopt AI deployed in numerous domains. However, explainability is an inherently human-centric property and the field is starting to embrace human-centered approaches. Human-computer interaction (HCI) research and user experience (UX) design in this area are becoming increasingly important. In this chapter, we begin with a high-level overview of the technical landscape of XAI algorithms, then selectively survey our own and other recent HCI works that take human-centered approaches to design, evaluate, provide conceptual and methodological tools for XAI. We ask the question "what are human-centered approaches doing for XAI " and highlight three roles that they play in shaping XAI technologies by helping navigate, assess and expand the XAI toolbox: to drive technical choices by users' explainability needs, to uncover pitfalls of existing XAI methods and inform new methods, and to provide conceptual frameworks for human-compatible XAI.

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Section: Introductionmentioning

confidence: 99%

Human-Centered Explainable AI (XAI): From Algorithms to User Experiences

Liao¹,

Varshney²

2021

Preprint

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“…XAI systems are required to fully understand the user, which means to adapt to the one that receives the explanation [21,336]. This is crucial to determine the explanation requirements for a given problem, and understand the 'why' behind user actions [337]. Furthermore, understanding is required to adapt to its socio-technical environment since the AI user will interact with other humans outside of the 1-1 human-computer interaction, and thus trust should be transitive to them [334].…”

Section: Xai In Atm Synthesismentioning

confidence: 99%

A Survey on Artificial Intelligence (AI) and eXplainable AI in Air Traffic Management: Current Trends and Development with Future Research Trajectory

et al. 2022

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Air Traffic Management (ATM) will be more complex in the coming decades due to the growth and increased complexity of aviation and has to be improved in order to maintain aviation safety. It is agreed that without significant improvement in this domain, the safety objectives defined by international organisations cannot be achieved and a risk of more incidents/accidents is envisaged. Nowadays, computer science plays a major role in data management and decisions made in ATM. Nonetheless, despite this, Artificial Intelligence (AI), which is one of the most researched topics in computer science, has not quite reached end users in ATM domain. In this paper, we analyse the state of the art with regards to usefulness of AI within aviation/ATM domain. It includes research work of the last decade of AI in ATM, the extraction of relevant trends and features, and the extraction of representative dimensions. We analysed how the general and ATM eXplainable Artificial Intelligence (XAI) works, analysing where and why XAI is needed, how it is currently provided, and the limitations, then synthesise the findings into a conceptual framework, named the DPP (Descriptive, Predictive, Prescriptive) model, and provide an example of its application in a scenario in 2030. It concludes that AI systems within ATM need further research for their acceptance by end-users. The development of appropriate XAI methods including the validation by appropriate authorities and end-users are key issues that needs to be addressed.

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“…In healthcare, the use of standardized clinical guidelines (e.g., [31,38] for MDD treatment selection) means that aspects of these mental models are established, encodable, and may therefore be used in both the development of the machine learning models and the design of DST interfaces. Ehsan and Riedl have also suggested that allowing users to voice skepticism in AI models may afford new interactions that encourage users to consider the limitations of the technology [16]. In the context of healthcare decisions, allowing clinicians and patients to voice skepticism and highlight surprising DST outputs may allow the underlying models to adapt as medical guidelines continue to evolve.…”

Section: Adapting Decision Support For Contrasting Informationmentioning

confidence: 99%

Designing AI for Trust and Collaboration in Time-Constrained Medical Decisions: A Sociotechnical Lens

Jacobs

Pradier

et al. 2021

Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

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Major depressive disorder is a debilitating disease affecting 264 million people worldwide. While many antidepressant medications are available, few clinical guidelines support choosing among them. Decision support tools (DSTs) embodying machine learning models may help improve the treatment selection process, but often fail in clinical practice due to poor system integration.We use an iterative, co-design process to investigate clinicians' perceptions of using DSTs in antidepressant treatment decisions. We identify ways in which DSTs need to engage with the healthcare sociotechnical system, including clinical processes, patient preferences, resource constraints, and domain knowledge. Our results suggest that clinical DSTs should be designed as multi-user systems that support patient-provider collaboration and offer on-demand explanations that address discrepancies between predictions and current standards of care. Through this work, we demonstrate how current trends in explainable AI may be inappropriate for clinical environments and consider paths towards designing these tools for real-world medical systems. CCS CONCEPTS• Human-centered computing → User centered design; • Applied computing → Health care information systems; • Information systems → Decision support systems.

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Human-Centered Explainable AI: Towards a Reflective Sociotechnical Approach

Cited by 124 publications

References 24 publications

Human-Centered Explainable AI (XAI): From Algorithms to User Experiences

Human-Centered Explainable AI (XAI): From Algorithms to User Experiences

A Survey on Artificial Intelligence (AI) and eXplainable AI in Air Traffic Management: Current Trends and Development with Future Research Trajectory

Designing AI for Trust and Collaboration in Time-Constrained Medical Decisions: A Sociotechnical Lens

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