Evaluating XAI: A comparison of rule-based and example-based explanations

Waa, Jasper van der; Nieuwburg, Elisabeth G.I.; Cremers, Anita; Neerincx, Mark A.

doi:10.1016/j.artint.2020.103404

Cited by 226 publications

(148 citation statements)

References 24 publications

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“…The research field of explainable AI (XAI) tackles the black box problem by introducing transparent models as well as techniques for generating different types of explanations for black box models (Adadi & Berrada, 2018;Arrieta et al, 2020;. Consequently, modern AIbased decision support systems (DSSs) can provide powerful decision support while also explaining the outcome via user interfaces (UIs) (Lamy et al, 2019;van der Waa et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, XAI can help monitor and ensure the fairness and transparency of AI-based systems, improve the management of such systems or support the maintenance of faulty systems (Kim et al, 2020;Tschandl et al, 2020). Despite active research in this context, there is a lack of user evaluation studies in the XAI field regarding the perception and effects of explanations on the targeted stakeholders (van der Waa et al, 2021). Moreover, different explanation goals and information needs, as well as varying backgrounds and/or expertise, can influence users' perceptions of XAI-based explanations, which further underlines the relevance of evaluations with targeted users (Barda et al, 2020;van der Waa et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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Design Principles for User Interfaces in AI-Based Decision Support Systems: The Case of Explainable Hate Speech Detection

Meske

Bunde

2022

Inf Syst Front

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Hate speech in social media is an increasing problem that can negatively affect individuals and society as a whole. Moderators on social media platforms need to be technologically supported to detect problematic content and react accordingly. In this article, we develop and discuss the design principles that are best suited for creating efficient user interfaces for decision support systems that use artificial intelligence (AI) to assist human moderators. We qualitatively and quantitatively evaluated various design options over three design cycles with a total of 641 participants. Besides measuring perceived ease of use, perceived usefulness, and intention to use, we also conducted an experiment to prove the significant influence of AI explainability on end users’ perceived cognitive efforts, perceived informativeness, mental model, and trustworthiness in AI. Finally, we tested the acquired design knowledge with software developers, who rated the reusability of the proposed design principles as high.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design Principles for User Interfaces in AI-Based Decision Support Systems: The Case of Explainable Hate Speech Detection

Meske

Bunde

2022

Inf Syst Front

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“…One of our research goals is to highlight the models' explainability in smart manufacturing processes, aligning XAI technologies with human interaction. We also aim to collect feedback on the quality of such explanations since there are few validated measurements for user evaluations on explanations' quality [64].…”

Section: Explainable Artificial Intelligencementioning

confidence: 99%

Help Me Learn! Architecture and Strategies to Combine Recommendations and Active Learning in Manufacturing

Zajec¹,

Rožanec²,

Trajkova³

et al. 2021

Preprint

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This research work describes an architecture for building a system that guide a user from a forecast generated by a machine learning model through a sequence of decision-making steps. The system is demonstrated in manufacturing demand forecasting use case and can be extended to other domains. In addition, the system provides means for knowledge acquisition by gathering data from users. Finally, it implements an active learning component and compares multiple strategies to recommend media news to the user. Such media news aims to provide additional context to demand forecasts and enhance judgment on decision-making.

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Section: Explainable Artificial Intelligencementioning

confidence: 99%

Help Me Learn! Architecture and Strategies to Combine Recommendations and Active Learning in Manufacturing

et al. 2021

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This research work describes an architecture for building a system that guides a user from a forecast generated by a machine learning model through a sequence of decision-making steps. The system is demonstrated in a manufacturing demand forecasting use case and can be extended to other domains. In addition, the system provides the means for knowledge acquisition by gathering data from users. Finally, it implements an active learning component and compares multiple strategies to recommend media news to the user. We compare such strategies through a set of experiments to understand how they balance learning and provide accurate media news recommendations to the user. The media news aims to provide additional context to demand forecasts and enhance judgment on decision-making.

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Evaluating XAI: A comparison of rule-based and example-based explanations

Cited by 226 publications

References 24 publications

Design Principles for User Interfaces in AI-Based Decision Support Systems: The Case of Explainable Hate Speech Detection

Design Principles for User Interfaces in AI-Based Decision Support Systems: The Case of Explainable Hate Speech Detection

Help Me Learn! Architecture and Strategies to Combine Recommendations and Active Learning in Manufacturing

Help Me Learn! Architecture and Strategies to Combine Recommendations and Active Learning in Manufacturing

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