An interdisciplinary concept for human-centered explainable artificial intelligence - Investigating the impact of explainable AI on end-users

Since the 1950s, Artificial Intelligence (AI) applications have captivated people. However, this fascination has always been accompanied by disillusionment about the limitations of this technology. Today, machine learning methods such as Deep Neural Networks (DNN) are successfully used in various tasks. However, these methods also have limitations: Their complexity makes their decisions no longer comprehensible to humans - they are black-boxes. The research branch of Explainable AI (XAI) has addressed this problem by investigating how to make AI decisions comprehensible. This desire is not new. In the 1970s, developers of intrinsic explainable AI approaches, so-called white-boxes (e.g., rule-based systems), were dealing with AI explanations. Nowadays, with the increased use of AI systems in all areas of life, the design of comprehensible systems has become increasingly important. Developing such systems is part of Human-Centred AI (HCAI) research, which integrates human needs and abilities in the design of AI interfaces. For this, an understanding is needed of how humans perceive XAI and how AI explanations influence the interaction between humans and AI. One of the open questions concerns the investigation of XAI for end-users, i.e., people who have no expertise in AI but interact with such systems or are impacted by the system's decisions. This dissertation investigates the impact of different levels of interactive XAI of white- and black-box AI systems on end-users perceptions. Based on an interdisciplinary concept presented in this work, it is examined how the content, type, and interface of explanations of DNN (black box) and rule-based systems (white box) are perceived by end-users. How XAI influences end-users mental models, trust, self-efficacy, cognitive workload, and emotional state regarding the AI system is the centre of the investigation. At the beginning of the dissertation, general concepts regarding AI, explanations, and psychological constructs of mental models, trust, self-efficacy, cognitive load, and emotions are introduced. Subsequently, related work regarding the design and investigation of XAI for users is presented. This serves as a basis for the concept of a Human-Centered Explainable AI (HC-XAI) presented in this dissertation, which combines an XAI design approach with user evaluations. The author pursues an interdisciplinary approach that integrates knowledge from the research areas of (X)AI, Human-Computer Interaction, and Psychology. Based on this interdisciplinary concept, a five-step approach is derived and applied to illustrative surveys and experiments in the empirical part of this dissertation. To illustrate the first two steps, a persona approach for HC-XAI is presented, and based on that, a template for designing personas is provided. To illustrate the usage of the template, three surveys are presented that ask end-users about their attitudes and expectations towards AI and XAI. The personas generated from the survey data indicate that end-users often lack knowledge of XAI and that their perception of it depends on demographic and personality-related characteristics. Steps three to five deal with the design of XAI for concrete applications. For this, different levels of interactive XAI are presented and investigated in experiments with end-users. For this purpose, two rule-based systems (i.e., white-box) and four systems based on DNN (i.e., black-box) are used. These are applied for three purposes: Cooperation & collaboration, education, and medical decision support. Six user studies were conducted for this purpose, which differed in the interactivity of the XAI system used. The results show that end-users trust and mental models of AI depend strongly on the context of use and the design of the explanation itself. For example, explanations that a virtual agent mediates are shown to promote trust. The content and type of explanations are also perceived differently by users. The studies also show that end-users in different application contexts of XAI feel the desire for interactive explanations. The dissertation concludes with a summary of the scientific contribution, points out limitations of the presented work, and gives an outlook on possible future research topics to integrate explanations into everyday AI systems and thus enable the comprehensible handling of AI for all people.Seit den 1950er Jahren haben Anwendungen der Künstlichen Intelligenz (KI) die Menschen in ihren Bann gezogen. Diese Faszination wurde jedoch stets von Ernüchterung über die Grenzen dieser Technologie begleitet. Heute werden Methoden des maschinellen Lernens wie Deep Neural Networks (DNN) erfolgreich für verschiedene Aufgaben eingesetzt. Doch auch diese Methoden haben ihre Grenzen: Durch ihre Komplexität sind ihre Entscheidungen für den Menschen nicht mehr nachvollziehbar - sie sind Black-Boxes. Der Forschungszweig der Erklärbaren KI (engl. XAI) hat sich diesem Problem angenommen und untersucht, wie man KI-Entscheidungen nachvollziehbar machen kann. Dieser Wunsch ist nicht neu. In den 1970er Jahren beschäftigten sich die Entwickler von intrinsisch erklärbaren KI-Ansätzen, so genannten White-Boxes (z. B. regelbasierte Systeme), mit KI-Erklärungen. Heutzutage, mit dem zunehmenden Einsatz von KI-Systemen in allen Lebensbereichen, wird die Gestaltung nachvollziehbarer Systeme immer wichtiger. Die Entwicklung solcher Systeme ist Teil der Menschzentrierten KI (engl. HCAI) Forschung, die menschliche Bedürfnisse und Fähigkeiten in die Gestaltung von KI-Schnittstellen integriert. Dafür ist ein Verständnis darüber erforderlich, wie Menschen XAI wahrnehmen und wie KI-Erklärungen die Interaktion zwischen Mensch und KI beeinflussen. Eine der offenen Fragen betrifft die Untersuchung von XAI für Endnutzer, d.h. Menschen, die keine Expertise in KI haben, aber mit solchen Systemen interagieren oder von deren Entscheidungen betroffen sind. In dieser Dissertation wird untersucht, wie sich verschiedene Stufen interaktiver XAI von White- und Black-Box-KI-Systemen auf die Wahrnehmung der Endnutzer auswirken. Basierend auf einem interdisziplinären Konzept, das in dieser Arbeit vorgestellt wird, wird untersucht, wie der Inhalt, die Art und die Schnittstelle von Erklärungen von DNN (Black-Box) und regelbasierten Systemen (White-Box) von Endnutzern wahrgenommen werden. Wie XAI die mentalen Modelle, das Vertrauen, die Selbstwirksamkeit, die kognitive Belastung und den emotionalen Zustand der Endnutzer in Bezug auf das KI-System beeinflusst, steht im Mittelpunkt der Untersuchung. Zu Beginn der Arbeit werden allgemeine Konzepte zu KI, Erklärungen und psychologische Konstrukte von mentalen Modellen, Vertrauen, Selbstwirksamkeit, kognitiver Belastung und Emotionen vorgestellt. Anschließend werden verwandte Arbeiten bezüglich dem Design und der Untersuchung von XAI für Nutzer präsentiert. Diese dienen als Grundlage für das in dieser Dissertation vorgestellte Konzept einer Menschzentrierten Erklärbaren KI (engl. HC-XAI), das einen XAI-Designansatz mit Nutzerevaluationen kombiniert. Die Autorin verfolgt einen interdisziplinären Ansatz, der Wissen aus den Forschungsbereichen (X)AI, Mensch-Computer-Interaktion und Psychologie integriert. Auf der Grundlage dieses interdisziplinären Konzepts wird ein fünfstufiger Ansatz abgeleitet und im empirischen Teil dieser Arbeit auf exemplarische Umfragen und Experimente und angewendet. Zur Veranschaulichung der ersten beiden Schritte wird ein Persona-Ansatz für HC-XAI vorgestellt und darauf aufbauend eine Vorlage für den Entwurf von Personas bereitgestellt. Um die Verwendung der Vorlage zu veranschaulichen, werden drei Umfragen präsentiert, in denen Endnutzer zu ihren Einstellungen und Erwartungen gegenüber KI und XAI befragt werden. Die aus den Umfragedaten generierten Personas zeigen, dass es den Endnutzern oft an Wissen über XAI mangelt und dass ihre Wahrnehmung dessen von demografischen und persönlichkeitsbezogenen Merkmalen abhängt. Die Schritte drei bis fünf befassen sich mit der Gestaltung von XAI für konkrete Anwendungen. Hierzu werden verschiedene Stufen interaktiver XAI vorgestellt und in Experimenten mit Endanwendern untersucht. Zu diesem Zweck werden zwei regelbasierte Systeme (White-Box) und vier auf DNN basierende Systeme (Black-Box) verwendet. Diese werden für drei Zwecke eingesetzt: Kooperation & Kollaboration, Bildung und medizinische Entscheidungsunterstützung. Hierzu wurden sechs Nutzerstudien durchgeführt, die sich in der Interaktivität des verwendeten XAI-Systems unterschieden. Die Ergebnisse zeigen, dass das Vertrauen und die mentalen Modelle der Endnutzer in KI stark vom Nutzungskontext und der Gestaltung der Erklärung selbst abhängen. Es hat sich beispielsweise gezeigt, dass Erklärungen, die von einem virtuellen Agenten vermittelt werden, das Vertrauen fördern. Auch der Inhalt und die Art der Erklärungen werden von den Nutzern unterschiedlich wahrgenommen. Die Studien zeigen zudem, dass Endnutzer in unterschiedlichen Anwendungskontexten von XAI den Wunsch nach interaktiven Erklärungen verspüren. Die Dissertation schließt mit einer Zusammenfassung des wissenschaftlichen Beitrags, weist auf Grenzen der vorgestellten Arbeit hin und gibt einen Ausblick auf mögliche zukünftige Forschungsthemen, um Erklärungen in alltägliche KI-Systeme zu integrieren und damit den verständlichen Umgang mit KI für alle Menschen zu ermöglichen

The E-Cultural Adaption Framework (E-CAF) : adapting the local travel interface for Egyptian consumers

Although the Internet has become a global medium, which companies use to access individuals worldwide, it is argued in this thesis that cultural issues still need to be taken into account when designing Web sites. In fact, international marketers agree that culture in terms of consumers‘ values and beliefs remains a significant constraint for reaching international consumers. Professional analysts and designers generally agree that well-designed user interfaces improve the performance and appeal of the Web and help in reaching large numbers of consumers across cultures. Therefore, one way of improving the user-interface is by paying attention to users' culture, which means developing culturally adapted Web sites. The Web localisation literature addresses the users' ultural concerns by utilising some of the popular cultural models like those of Hofstede and Hall; however these tools are not appropriate for handling the cultural values affecting the online behaviour of consumers. Effective Web localisation can be achieved through an appropriate cultural framework that incorporates the cultural values that affect the online behaviour of consumers. This thesis introduces the electronic cultural adaption framework or E-CAF, as a structure for adapting local Web interfaces. The E-CAF, constructed for the travel domain, uses six cultural dimensions derived from the observation of behaviour and identifies unique cultural variables that affect online consumer behaviour. The E-CAF is constructed using grounded theory methodology and is then evaluated as a tool for adapting local Web interfaces. This includes discussing the applicability of the E-CAF as a tool for identifying online marketing strategies suitable for targeting consumers across cultures and using the E-CAF to assess and evaluate the cultural adaptation of three Egyptian local travel interfaces. Finally, the E-CAF is utilized as a means for developing questions that can help designers to collect the clients' designing requirements. This helps the designers to build an effective local interface based on an understanding of each client‘s special design requirements.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

The E-Cultural Adaption Framework (E-CAF) : adapting the local travel interface for Egyptian consumers

Although the Internet has become a global medium, which companies use to access individuals worldwide, it is argued in this thesis that cultural issues still need to be taken into account when designing Web sites. In fact, international marketers agree that culture in terms of consumers‘ values and beliefs remains a significant constraint for reaching international consumers. Professional analysts and designers generally agree that well-designed user interfaces improve the performance and appeal of the Web and help in reaching large numbers of consumers across cultures. Therefore, one way of improving the user-interface is by paying attention to users' culture, which means developing culturally adapted Web sites. The Web localisation literature addresses the users' ultural concerns by utilising some of the popular cultural models like those of Hofstede and Hall; however these tools are not appropriate for handling the cultural values affecting the online behaviour of consumers. Effective Web localisation can be achieved through an appropriate cultural framework that incorporates the cultural values that affect the online behaviour of consumers. This thesis introduces the electronic cultural adaption framework or E-CAF, as a structure for adapting local Web interfaces. The E-CAF, constructed for the travel domain, uses six cultural dimensions derived from the observation of behaviour and identifies unique cultural variables that affect online consumer behaviour. The E-CAF is constructed using grounded theory methodology and is then evaluated as a tool for adapting local Web interfaces. This includes discussing the applicability of the E-CAF as a tool for identifying online marketing strategies suitable for targeting consumers across cultures and using the E-CAF to assess and evaluate the cultural adaptation of three Egyptian local travel interfaces. Finally, the E-CAF is utilized as a means for developing questions that can help designers to collect the clients' designing requirements. This helps the designers to build an effective local interface based on an understanding of each client‘s special design requirements.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

THE IMPACT OF LEARNING STYLES AND CULTURAL BACKGROUND ON USERS’ EXPERIENCE OF WEBSITES

Many different types of people now use websites for many sources of information. Nevertheless, the diversity and complexity of the online information available on websites and the desire to make websites provide all information for all users, regardless their interest, ability or characteristics, means that websites can be overwhelming to users. Museum websites are a case in point, trying to provide information to a great diversity of users. For these reasons, there have been numerous efforts to individualize user experiences in websites. These efforts have been based on users’ individual or group differences such as their goals, interests, preferences, knowledge, backgrounds, demographic characteristics, experience, learning styles, and culture. This programme of research investigates whether learning styles as an individual difference and cultural background as a group difference can affect web users’ experience, performance and perceived usability by conducting a card sort study, an interview study and an ecologically valid study of users’ experience with museum websites. To investigate learning styles, the Felder-Silverman Learning Style Model was used with its associated Felder-Solomon Index of Learning Styles (ILS). The ILS was developed in English, making it unsuitable for Turkish learners, one of the target cultural groups for this research programme. Therefore, the ILS was translated into Turkish and adequate reliability and validity established by administering it twice over a four-week interval to 63 undergraduate students in Turkey. Henceforth, the Turkish version of the ILS will be referred as the Turkish Index of Learning Styles (T)ILS. The aim of the card sort study was to investigate user understandings of the organization of the museum and news websites and to reveal learning styles and cultural differences between participants’ categorizations and mental models of the information architectures. The study was conducted in UK and Turkey with 214 and 90 participants, respectively. Analysis of the data showed that participants have mental models that differ substantially from the typical websites in these domains. In addition, interesting and meaningful differences were found between participants with different learning styles profiles and among British, Chinese, Indian and Turkish participants. This study also made a methodological contribution, showing that the card sort method can be used to show learning styles and cultural differences. The aim of the interview study was to investigate the perceptions of museum personnel concerning the adaptation of websites in relation to both learning styles and cultural differences among visitors and to investigate whether they were interested in these issues. Five developers from Turkey and five developers from UK and USA were interviewed and content analysis was used to analyze their responses. The study showed that almost none of the interviewees were aware of the concept of learning styles, but the majority were very interested when they were told about it. Furthermore, a majority of interviewees thought learning styles had potential to make their websites more appealing to a wider range of visitors. Lastly, most interviewees were interested in the idea of dealing with cultural differences in other ways than mere translation of texts. The final study investigated how learning styles and cultural differences affect users’ experience, performance and perception of the usability of two museum websites. It was administered in the UK with an international sample of 210 participants. Participants were asked to perform a number of tasks on these websites, the tasking being carefully chosen to direct participants to aspects of the websites that would suit particular learning styles. This study showed significant differences among users depending on their learning styles and cultural background. This study also makes an important methodological contribution in that moves away from the paradigm of trying to manipulate online materials to match or clash with users’ learning styles or other preferences. The results of this research programme will be important for developers of museum and similar websites who want to take the advantage of developing supportive websites by focusing on users’ learning styles and cultural differences