Designing Theory-Driven User-Centric Explainable AI

Wang, Danding; Yang, Qian; Abdul, Ashraf; Lim, Brian Y.

doi:10.1145/3290605.3300831

Cited by 578 publications

(483 citation statements)

References 64 publications

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“…In light of this, a recent wave of HCI research has studied what end-users actually desire to understand about ML systems, and how that transparency affects user attitudes and outcomes. Domains of study include recommender systems [4], medicine [12,75], social media [22], creativity [14], and advertisements [23]. While the majority of this work has tended to focus on explaining the reasoning behind specific model decisions, our work instead examines the broader questions that a user may desire to ask of the system as a whole, including components of the ML pipeline that may occur even before a model is built (e.g., data collection, or selection of model design goals).…”

Section: Algorithmic Transparencymentioning

confidence: 99%

"Hello AI": Uncovering the Onboarding Needs of Medical Practitioners for Human-AI Collaborative Decision-Making

Cai

Winter

Steiner

et al. 2019

Proc. ACM Hum.-Comput. Interact.

335

188

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Although rapid advances in machine learning have made it increasingly applicable to expert decision-making, the delivery of accurate algorithmic predictions alone is insufficient for effective human-AI collaboration. In this work, we investigate the key types of information medical experts desire when they are first introduced to a diagnostic AI assistant. In a qualitative lab study, we interviewed 21 pathologists before, during, and after being presented deep neural network (DNN) predictions for prostate cancer diagnosis, to learn the types of information that they desired about the AI assistant. Our findings reveal that, far beyond understanding the local, case-specific reasoning behind any model decision, clinicians desired upfront information about basic, global properties of the model, such as its known strengths and limitations, its subjective point-of-view, and its overall design objective-what it's designed to be optimized for. Participants compared these information needs to the collaborative mental models they develop of their medical colleagues when seeking a second opinion: the medical perspectives and standards that those colleagues embody, and the compatibility of those perspectives with their own diagnostic patterns. These findings broaden and enrich discussions surrounding AI transparency for collaborative decision-making, providing a richer understanding of what experts find important in their introduction to AI assistants before integrating them into routine practice. CCS Concepts: • Human-centered computing → Human computer interaction (HCI).

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Section: Algorithmic Transparencymentioning

confidence: 99%

"Hello AI": Uncovering the Onboarding Needs of Medical Practitioners for Human-AI Collaborative Decision-Making

Cai

Winter

Steiner

et al. 2019

Proc. ACM Hum.-Comput. Interact.

335

188

View full text Add to dashboard Cite

show abstract

“…The concept of explaining has been studied for a long time by research disciplines other than information systems or computer science, such as social sciences [14,32]. Following those lenses, Miller [33] defines explanations in XAI as an answer to a question an explainee would have to an explainer, which can be a why-question such as "Why is that transaction marked as a fraud?".…”

Section: Related Workmentioning

confidence: 99%

“…In addition, while it is essential to develop those methods, researchers from the social perspective highlight XAI research tended to adopt particular notions of what is a good explanation, not considering, for instance, the usability and causability requirements of stakeholders for understanding explanations [14,15,16,17]. Such requirements are essential as they enable an understanding of the quality of explanations associated with a human agent's properties and his cognitive and intelligence capabilities for working with AI models [18].…”

Section: Introductionmentioning

confidence: 99%

Scenario-Based Requirements Elicitation for User-Centric Explainable AI

Cirqueira

Nedbal

Helfert

et al. 2020

Lecture Notes in Computer Science

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Explainable Artificial Intelligence (XAI) develops technical explanation methods and enable interpretability for human stakeholders on why Artificial Intelligence (AI) and machine learning (ML) models provide certain predictions. However, the trust of those stakeholders into AI models and explanations is still an issue, especially domain experts, who are knowledgeable about their domain but not AI inner workings. Social and user-centric XAI research states it is essential to understand the stakeholder's requirements to provide explanations tailored to their needs, and enhance their trust in working with AI models. Scenariobased design and requirements elicitation can help bridge the gap between social and operational aspects of a stakeholder early before the adoption of information systems and identify its real problem and practices generating user requirements. Nevertheless, it is still rarely explored the adoption of scenarios in XAI, especially in the domain of fraud detection to supporting experts who are about to work with AI models. We demonstrate the usage of scenario-based requirements elicitation for XAI in a fraud detection context, and develop scenarios derived with experts in banking fraud. We discuss how those scenarios can be adopted to identify user or expert requirements for appropriate explanations in his daily operations and to make decisions on reviewing fraudulent cases in banking. The generalizability of the scenarios for further adoption is validated through a systematic literature review in domains of XAI and visual analytics for fraud detection.

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“…Semantic understanding includes not only the user's speaking intention, but also the acquisition of content, so as to form a complete interactive link (Dove et al 2017). In addition, in the interactive design, some fragments of hesitating and waiting sub-linguistic phenomena as well as humorous sentence corpus, such as "hmm", "uh" and "tell jokes", are timely added through the patchwork synthesis system to make the dialogue more like a real person in a natural conversation (Wang et al 2019). In the field of design creativity, AI has also made many explorations, such as Ali Luban system, Looka, Wix and so on.…”

Section: Related Workmentioning

confidence: 99%

Research on human–AI co-creation based on reflective design practice

Zhou

2020

CCF Trans. Pervasive Comp. Interact.

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Artificial intelligence has appeared in every aspect of our life, whether it is a large number of data analysis and repetitive work, or the scanning recognition in life, AI has become an essential application technology. How humans and AI will coexist in the future is a thought-provoking question. The involvement of AI will lead to changes in design method and process, the designers and researchers need to conduct research on future designs from a new perspective. We selected the field of design practice and put forward the concept of human-AI co-creation (HACC), aiming to study how AI can better design and create with people. We use ethnographic design research methods to summarize how teachers guide students in design practice, and summarize the normative vocabulary that AI guides students to use in experiments. Finally, we summarize the basic elements of HACC, and then use the "Wizard of OZ" method to simulate HACC experiment, and further improve the HACC model through video observation and data analysis.

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Designing Theory-Driven User-Centric Explainable AI

Cited by 578 publications

References 64 publications

"Hello AI": Uncovering the Onboarding Needs of Medical Practitioners for Human-AI Collaborative Decision-Making

"Hello AI": Uncovering the Onboarding Needs of Medical Practitioners for Human-AI Collaborative Decision-Making

Scenario-Based Requirements Elicitation for User-Centric Explainable AI

Research on human–AI co-creation based on reflective design practice

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