114 research outputs found
SAsSy - Making Decisions Transparent with Argumentation and Natural Language Generation
Peer reviewedPublisher PD
The Effectiveness of Personalized Movie Explanations : An Experiment Using Commercial Meta-data
Preprin
Demo: Making Plans Scrutable with Argumentation and Natural Language Generation.
Peer reviewedPublisher PD
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
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
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
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