DGRMiner: Anomaly Detection and Explanation in Dynamic Graphs

Vaculík, Karel; Popelínský, Lubomír

doi:10.1007/978-3-319-46349-0_27

Cited by 5 publications

(1 citation statement)

References 8 publications

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“…This is an open access post-print version; the final authenticated version is available online at https://link.springer.com/chapter/10.1007/978-3-030-57321-8_18 by © IFIP International Federation for Information Processing 2020. [22,36,43,44,55,68,71,72,80,88,89,92,93,95,96,97,98,101,102,103,104,105,106,107] 24 Yes…”

Section: Usage Of Scenarios For Requirements Elicitation For Explanatmentioning

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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