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Towards design principles for user-centric explainable AI in fraud detection

Cirqueira, Douglas orcid logoORCID: 0000-0002-1283-0453, Helfert, Markus orcid logoORCID: 0000-0001-6546-6408 and Bezbradica, Marija orcid logoORCID: 0000-0001-9366-5113 (2021) Towards design principles for user-centric explainable AI in fraud detection. In: International Conference on Human-Computer Interaction HCII 2021, 24-29 July 2021, Washington, DC, USA. ISBN 978-3-030-77772-2

Experts rely on fraud detection and decision support systems to analyze fraud cases, a growing problem in digital retailing and banking. With the advent of Artificial Intelligence (AI) for decision support, those experts face the black-box problem and lack trust in AI predictions for fraud. Such an issue has been tackled by employing Explainable AI (XAI) to provide experts with explained AI predictions through various explanation methods. However, fraud detection studies supported by XAI lack a user-centric perspective and discussion on how principles are deployed, both important requirements for experts to choose an appropriate explanation method. On the other hand, recent research in Information Systems (IS) and Human-Computer Interaction highlights the need for understanding user requirements to develop tailored design principles for decision support systems. In this research, we adopt a design science research methodology and IS theoretical lens to develop and evaluate design principles, which align fraud expert's tasks with explanation methods for Explainable AI decision support. We evaluate the utility of these principles using an information quality framework to interview experts in banking fraud, plus a simulation. The results show that the principles are an useful tool for designing decision support systems for fraud detection with embedded user-centric Explainable AI.
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Uncontrolled Keywords:Explainable AI; Fraud Detection; Decision Support Systems; Artificial Intelligence; Design Principles; HCI; Human-AI Interaction; Human-Centered AI
Subjects:Computer Science > Artificial intelligence
Computer Science > Machine learning
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Institutes and Centres > Lero: The Irish Software Engineering Research Centre
Published in: HCII 2021: Artificial Intelligence in HCI. . Springer. ISBN 978-3-030-77772-2
Official URL:https://dx.doi.org/10.1007/978-3-030-77772-2_2
Copyright Information:© Springer Nature Switzerland AG 2021
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:This research was supported by the European Union Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 765395, This research was supported, in part, by Science Foundation Ireland grant 13/RC/2094_P2
ID Code:26056
Deposited On:14 Jul 2021 18:01 by Douglas Da Rocha cirqueira . Last Modified 16 Nov 2023 12:43

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