SAsSy - Making Decisions Transparent with Argumentation and Natural Language Generation

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Demo: Making Plans Scrutable with Argumentation and Natural Language Generation.

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The Effectiveness of Personalized Movie Explanations : An Experiment Using Commercial Meta-data

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Adapting Recommendation Diversity to Openness to Experience : A Study of Human Behaviour

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A Co-design Study for Multi-Stakeholder Job Recommender System Explanations

Recent legislation proposals have significantly increased the demand for eXplainable Artificial Intelligence (XAI) in many businesses, especially in so-called `high-risk' domains, such as recruitment. Within recruitment, AI has become commonplace, mainly in the form of job recommender systems (JRSs), which try to match candidates to vacancies, and vice versa. However, common XAI techniques often fall short in this domain due to the different levels and types of expertise of the individuals involved, making explanations difficult to generalize. To determine the explanation preferences of the different stakeholder types - candidates, recruiters, and companies - we created and validated a semi-structured interview guide. Using grounded theory, we structurally analyzed the results of these interviews and found that different stakeholder types indeed have strongly differing explanation preferences. Candidates indicated a preference for brief, textual explanations that allow them to quickly judge potential matches. On the other hand, hiring managers preferred visual graph-based explanations that provide a more technical and comprehensive overview at a glance. Recruiters found more exhaustive textual explanations preferable, as those provided them with more talking points to convince both parties of the match. Based on these findings, we describe guidelines on how to design an explanation interface that fulfills the requirements of all three stakeholder types. Furthermore, we provide the validated interview guide, which can assist future research in determining the explanation preferences of different stakeholder types

How Can Skin Check Reminders be Personalised to Patient Conscientiousness?

This paper explores the potential of personalising health reminders to melanoma patients based on their personality (high vs low conscientiousness). We describe a study where we presented participants with a scenario with a fictional patient who has not performed a skin check for recurrent melanoma. The patient was described as either very conscientious, or very unconscientious. We asked participants to rate reminders inspired by Cialdini’s 6 principles of persuasion for their suitability for the patient. Participants then chose their favourite reminder and an alternative reminder to send if that one failed. We found that conscientiousness had an effect on both the ratings of reminder types and the most preferred reminders selected by participants

Evaluation of Human-Understandability of Global Model Explanations Using Decision Tree

Artificial Intelligence. ECAI 2023 International Workshops XAI^3, TACTIFUL, XI-ML, SEDAMI, RAAIT, AI4S, HYDRA, AI4AI, Kraków, Poland, September 30 – October 4, 2023, Proceedings, Part

Natural Language Generation and Fuzzy Sets : An Exploratory Study on Geographical Referring Expression Generation

This work was supported by the Spanish Ministry for Economy and Competitiveness (grant TIN2014-56633-C3-1-R) and by the European Regional Development Fund (ERDF/FEDER) and the Galician Ministry of Education (grants GRC2014/030 and CN2012/151). Alejandro Ramos-Soto is supported by the Spanish Ministry for Economy and Competitiveness (FPI Fellowship Program) under grant BES-2012-051878.Postprin