5 research outputs found

    Closing the Loop: Testing ChatGPT to Generate Model Explanations to Improve Human Labelling of Sponsored Content on Social Media

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    Regulatory bodies worldwide are intensifying their efforts to ensure transparency in influencer marketing on social media through instruments like the Unfair Commercial Practices Directive (UCPD) in the European Union, or Section 5 of the Federal Trade Commission Act. Yet enforcing these obligations has proven to be highly problematic due to the sheer scale of the influencer market. The task of automatically detecting sponsored content aims to enable the monitoring and enforcement of such regulations at scale. Current research in this field primarily frames this problem as a machine learning task, focusing on developing models that achieve high classification performance in detecting ads. These machine learning tasks rely on human data annotation to provide ground truth information. However, agreement between annotators is often low, leading to inconsistent labels that hinder the reliability of models. To improve annotation accuracy and, thus, the detection of sponsored content, we propose using chatGPT to augment the annotation process with phrases identified as relevant features and brief explanations. Our experiments show that this approach consistently improves inter-annotator agreement and annotation accuracy. Additionally, our survey of user experience in the annotation task indicates that the explanations improve the annotators' confidence and streamline the process. Our proposed methods can ultimately lead to more transparency and alignment with regulatory requirements in sponsored content detection.Comment: Accepted to The World Conference on eXplainable Artificial Intelligence, Lisbon, Portugal, July 202

    Closing the Loop: Testing ChatGPT to Generate Model Explanations to Improve Human Labelling of Sponsored Content on Social Media

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    Regulatory bodies worldwide are intensifying their efforts to ensure transparency in influencer marketing on social media through instruments like the Unfair Commercial Practices Directive (UCPD) in the European Union, or Section 5 of the Federal Trade Commission Act. Yet enforcing these obligations has proven to be highly problematic due to the sheer scale of the influencer market. The task of automatically detecting sponsored content aims to enable the monitoring and enforcement of such regulations at scale. Current research in this field primarily frames this problem as a machine learning task, focusing on developing models that achieve high classification performance in detecting ads. These machine learning tasks rely on human data annotation to provide ground truth information. However, agreement between annotators is often low, leading to inconsistent labels that hinder the reliability of models. To improve annotation accuracy and, thus, the detection of sponsored content, we propose using chatGPT to augment the annotation process with phrases identified as relevant features and brief explanations. Our experiments show that this approach consistently improves inter-annotator agreement and annotation accuracy. Additionally, our survey of user experience in the annotation task indicates that the explanations improve the annotators' confidence and streamline the process. Our proposed methods can ultimately lead to more transparency and alignment with regulatory requirements in sponsored content detection

    A Closer Look into Recent Video-based Learning Research: A Comprehensive Review of Video Characteristics, Tools, Technologies, and Learning Effectiveness

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    People increasingly use videos on the Web as a source for learning. To support this way of learning, researchers and developers are continuously developing tools, proposing guidelines, analyzing data, and conducting experiments. However, it is still not clear what characteristics a video should have to be an effective learning medium. In this paper, we present a comprehensive review of 257 articles on video-based learning for the period from 2016 to 2021. One of the aims of the review is to identify the video characteristics that have been explored by previous work. Based on our analysis, we suggest a taxonomy which organizes the video characteristics and contextual aspects into eight categories: (1) audio features, (2) visual features, (3) textual features, (4) instructor behavior, (5) learners activities, (6) interactive features (quizzes, etc.), (7) production style, and (8) instructional design. Also, we identify four representative research directions: (1) proposals of tools to support video-based learning, (2) studies with controlled experiments, (3) data analysis studies, and (4) proposals of design guidelines for learning videos. We find that the most explored characteristics are textual features followed by visual features, learner activities, and interactive features. Text of transcripts, video frames, and images (figures and illustrations) are most frequently used by tools that support learning through videos. The learner activity is heavily explored through log files in data analysis studies, and interactive features have been frequently scrutinized in controlled experiments. We complement our review by contrasting research findings that investigate the impact of video characteristics on the learning effectiveness, report on tasks and technologies used to develop tools that support learning, and summarize trends of design guidelines to produce learning video

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

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    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 KĂŒnstlichen Intelligenz (KI) die Menschen in ihren Bann gezogen. Diese Faszination wurde jedoch stets von ErnĂŒchterung ĂŒber die Grenzen dieser Technologie begleitet. Heute werden Methoden des maschinellen Lernens wie Deep Neural Networks (DNN) erfolgreich fĂŒr verschiedene Aufgaben eingesetzt. Doch auch diese Methoden haben ihre Grenzen: Durch ihre KomplexitĂ€t sind ihre Entscheidungen fĂŒr den Menschen nicht mehr nachvollziehbar - sie sind Black-Boxes. Der Forschungszweig der ErklĂ€rbaren 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 beschĂ€ftigten sich die Entwickler von intrinsisch erklĂ€rbaren KI-AnsĂ€tzen, so genannten White-Boxes (z. B. regelbasierte Systeme), mit KI-ErklĂ€rungen. 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 BedĂŒrfnisse und FĂ€higkeiten in die Gestaltung von KI-Schnittstellen integriert. DafĂŒr ist ein VerstĂ€ndnis darĂŒber erforderlich, wie Menschen XAI wahrnehmen und wie KI-ErklĂ€rungen die Interaktion zwischen Mensch und KI beeinflussen. Eine der offenen Fragen betrifft die Untersuchung von XAI fĂŒr 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 interdisziplinĂ€ren Konzept, das in dieser Arbeit vorgestellt wird, wird untersucht, wie der Inhalt, die Art und die Schnittstelle von ErklĂ€rungen 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, ErklĂ€rungen und psychologische Konstrukte von mentalen Modellen, Vertrauen, Selbstwirksamkeit, kognitiver Belastung und Emotionen vorgestellt. Anschließend werden verwandte Arbeiten bezĂŒglich dem Design und der Untersuchung von XAI fĂŒr Nutzer prĂ€sentiert. Diese dienen als Grundlage fĂŒr das in dieser Dissertation vorgestellte Konzept einer Menschzentrierten ErklĂ€rbaren KI (engl. HC-XAI), das einen XAI-Designansatz mit Nutzerevaluationen kombiniert. Die Autorin verfolgt einen interdisziplinĂ€ren Ansatz, der Wissen aus den Forschungsbereichen (X)AI, Mensch-Computer-Interaktion und Psychologie integriert. Auf der Grundlage dieses interdisziplinĂ€ren Konzepts wird ein fĂŒnfstufiger 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 fĂŒr HC-XAI vorgestellt und darauf aufbauend eine Vorlage fĂŒr den Entwurf von Personas bereitgestellt. Um die Verwendung der Vorlage zu veranschaulichen, werden drei Umfragen prĂ€sentiert, in denen Endnutzer zu ihren Einstellungen und Erwartungen gegenĂŒber KI und XAI befragt werden. Die aus den Umfragedaten generierten Personas zeigen, dass es den Endnutzern oft an Wissen ĂŒber XAI mangelt und dass ihre Wahrnehmung dessen von demografischen und persönlichkeitsbezogenen Merkmalen abhĂ€ngt. Die Schritte drei bis fĂŒnf befassen sich mit der Gestaltung von XAI fĂŒr 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 fĂŒr drei Zwecke eingesetzt: Kooperation & Kollaboration, Bildung und medizinische EntscheidungsunterstĂŒtzung. Hierzu wurden sechs Nutzerstudien durchgefĂŒhrt, die sich in der InteraktivitĂ€t 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 ErklĂ€rung selbst abhĂ€ngen. Es hat sich beispielsweise gezeigt, dass ErklĂ€rungen, die von einem virtuellen Agenten vermittelt werden, das Vertrauen fördern. Auch der Inhalt und die Art der ErklĂ€rungen werden von den Nutzern unterschiedlich wahrgenommen. Die Studien zeigen zudem, dass Endnutzer in unterschiedlichen Anwendungskontexten von XAI den Wunsch nach interaktiven ErklĂ€rungen verspĂŒren. Die Dissertation schließt mit einer Zusammenfassung des wissenschaftlichen Beitrags, weist auf Grenzen der vorgestellten Arbeit hin und gibt einen Ausblick auf mögliche zukĂŒnftige Forschungsthemen, um ErklĂ€rungen in alltĂ€gliche KI-Systeme zu integrieren und damit den verstĂ€ndlichen Umgang mit KI fĂŒr alle Menschen zu ermöglichen
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