SAsSy - Making Decisions Transparent with Argumentation and Natural Language Generation

Peer reviewedPublisher PD

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

Peer reviewedPublisher PD

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

Preprin

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

Postprin

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

Adapting Emotional Support to Personality for Carers Experiencing Stress.

Carers - people who provide regular support for a friend or relative who could not manage without them - frequently report high levels of stress. Good emotional support (e.g. provided by an Intelligent Virtual Agent) could help relieve this stress. This study investigates whether adaptation to personality affects the amount and type of emotional support a carer is given and possible interaction effects with the stress experienced. We investigated the personality trait of Emotional Stability (ES) as it is interlinked with low tolerance for stress. Participants were presented with stressful scenarios experienced by a fictitious carer and description of their personality and asked to rank 6 emotional support messages. We predicted that people with low ES would be given more emotional support messages overall and that ES would affect the type of emotional support messages given in each scenario. We found that participants gave more praise to the high ES carer with a trend towards other support types for the low ES carer