The user’s experience with a recommender system is significantly shaped by the dynamics of user-algorithm interactions. These interactions are often evaluated using interaction qualities, such as controllability, trust, and autonomy, to gauge their impact. As part of our effort to systematically categorize these evaluations, we explored the suitability of the interaction qualities framework as proposed by Lenz, Dieffenbach and Hassenzahl. During this examination, we uncovered four challenges within the framework itself, and an additional external challenge. In studies examining the interaction between user control options and interaction qualities, interdependencies between concepts, inconsistent terminology, and the entity perspective (is it a user’s trust or a system’s trustworthiness) often hinder a systematic inventory of the findings. Additionally, our discussion underscored the crucial role of the decision context in evaluating the relation of algorithmic affordances and interaction qualities. We propose dimensions of decision contexts (such as ‘reversibility of the decision’, or ‘time pressure’). They could aid in establishing a systematic three-way relationship between context attributes, attributes of user control mechanisms, and experiential goals, and as such they warrant further research. In sum, while the interaction qualities framework serves as a foundational structure for organizing research on evaluating the impact of algorithmic affordances, challenges related to interdependencies and context-specific influences remain. These challenges necessitate further investigation and subsequent refinement and expansion of the framework.
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Our study introduces an open general-purpose platform for the embodiment of conversational AI systems. Conversational User-interface Based Embodiment (CUBE) is designed to streamline the integration of embodied solutions into text-based dialog managers, providing flexibility for customization depending on the specific use case and application. CUBE is responsible for naturally interacting with users by listening, observing, and responding to them. A detailed account of the design and implementation of the solution is provided, as well as a thorough examination of how it can be integrated by developers and AI dialogue manager integrators. Through interviews with developers, insight was gained into the advantages of such systems. Additionally, key areas that require further research were identified in the current challenges in achieving natural interaction between the user and the embodiments. CUBE bridges some of the gaps by providing controls to further develop natural non-verbal communication.
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BACKGROUND: Non-use of and dissatisfaction with ankle foot orthoses (AFOs) occurs frequently. The objective of this study is to gain insight in the conversation during the intake and examination phase, from the clients’ perspective, at two levels: 1) the attention for the activities and the context in which these activities take place, and 2) the quality of the conversation. METHODOLOGY: Semi-structured interviews were performed with 12 AFO users within a two-week period following intake and examination. In these interviews, and subsequent data analysis, extra attention was paid to the needs and wishes of the user, the desired activities and the environments in which these activities take place. RESULTS AND CONCLUSION: Activities and environments were seldom inquired about or discussed during the intake and examination phase. Also, activities were not placed in the context of their specific environment. As a result, profundity lacks. Consequently, orthotists based their designs on a ‘reduced reality’ because important and valuable contextual information that might benefit prescription and design of assistive devices was missed. A model is presented for mapping user activities and user environments in a systematic way. The term ‘user practices’ is introduced to emphasise the concept of activities within a specific environment.
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De maatschappelijke discussies over de invloed van AI op ons leven tieren welig. De terugkerende vraag is of AI-toepassingen – en dan vooral recommendersystemen – een dreiging of een redding zijn. De impact van het kiezen van een film voor vanavond, met behulp van Netflix' recommendersysteem, is nog beperkt. De impact van datingsites, navigatiesystemen en sociale media – allemaal systemen die met algoritmes informatie filteren of keuzes aanraden – is al groter. De impact van recommendersystemen in bijvoorbeeld de zorg, bij werving en selectie, fraudedetectie, en beoordelingen van hypotheekaanvragen is enorm, zowel op individueel als op maatschappelijk niveau. Het is daarom urgent dat juist recommendersystemen volgens de waarden van Responsible AI ontworpen worden: veilig, eerlijk, betrouwbaar, inclusief, transparant en controleerbaar.Om op een goede manier Responsible AI te ontwerpen moeten technische, contextuele én interactievraagstukken worden opgelost. Op het technische en maatschappelijke niveau is al veel vooruitgang geboekt, respectievelijk door onderzoek naar algoritmen die waarden als inclusiviteit in hun berekening meenemen, en door de ontwikkeling van wettelijke kaders. Over implementatie op interactieniveau bestaat daarentegen nog weinig concrete kennis. Bekend is dat gebruikers die interactiemogelijkheden hebben om een algoritme bij te sturen of aan te vullen, meer transparantie en betrouwbaarheid ervaren. Echter, slecht ontworpen interactiemogelijkheden, of een mismatch tussen interactie en context kosten juist tijd, veroorzaken mentale overbelasting, frustratie, en een gevoel van incompetentie. Ze verhullen eerder dan dat ze tot transparantie leiden.Het ontbreekt ontwerpers van interfaces (UX/UI designers) aan systematische concrete kennis over deze interactiemogelijkheden, hun toepasbaarheid, en de ethische grenzen. Dat beperkt hun mogelijkheid om op interactieniveau aan Responsible AI bij te dragen. Ze willen daarom graag een pattern library van interactiemogelijkheden, geannoteerd met onderzoek over de werking en inzetbaarheid. Dit bestaat nu niet en met dit project willen we een substantiële bijdrage leveren aan de ontwikkeling ervan.
Alcohol Use Disorder (AUD) involves uncontrollable drinking despite negative consequences, a challenge amplified in festivals. ARise is a project using Augmented Reality (AR) to prevent AUD by helping festival visitors refuse alcohol and other substances. Based on the first Augmented Reality Exposure Therapy (ARET) for clinical AUD treatment, ARise uses a smartphone app with AR glasses to project virtual humans that tempt visitors to drink alcohol. Users interact in a safe and personalized way with these virtual humans through phone, voice, and gesture interactions. The project gathers festival feedback on user experience, awareness, usability, and potential expansion to other substances.Societal issueHelping treatment of addiction and stimulate social inclusion.Benefit to societyMore people less patients: decrease health cost and increase in inclusion and social happiness.Collaborative partnersNovadic-Kentron, Thalamusa
Alcohol use disorder (AUD) is a pattern of alcohol use that involves having trouble controlling drinking behaviour, even when it causes health issues (addiction) or problems functioning in daily (social and professional) life. Moreover, festivals are a common place where large crowds of festival-goers experience challenges refusing or controlling alcohol and substance use. Studies have shown that interventions at festivals are still very problematic. ARise is the first project that wants to help prevent AUD at festivals using Augmented Reality (AR) as a tool to help people, particular festival visitors, to say no to alcohol (and other substances). ARise is based on the on the first Augmented Reality Exposure Therapy (ARET) in the world that we developed for clinical treatment of AUD. It is an AR smartphone driven application in which (potential) visitors are confronted with virtual humans that will try to seduce the user to accept an alcoholic beverage. These virtual humans are projected in the real physical context (of a festival), using innovative AR glasses. Using intuitive phone, voice and gesture interactions, it allows users to personalize the safe experience by choosing different drinks and virtual humans with different looks and levels of realism. ARET has been successfully developed and tested on (former) AUD patients within a clinical setting. Research with patients and healthcare specialists revealed the wish to further develop ARET as a prevention tool to reach people before being diagnosed with AUD and to extend the application for other substances (smoking and pills). In this project, festival visitors will experience ARise and provide feedback on the following topics: (a) experience, (b) awareness and confidence to refuse alcohol drinks, (c) intention to use ARise, (d) usability & efficiency (the level of realism needed), and (e) ideas on how to extend ARise with new substances.
