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This study aims to investigate whether artificial intelligence (AI) can be utilized in the user experience design process to enhance the accessibility of websites for users with diverse needs and abilities. With the implementation of the European Accessibility Act into Swedish law, many websites will need to meet specific accessibility requirements, including WCAG (Web Content Accessibility Guidelines). This is a substantial task, and AI has the potential to be a tool for UX designers to facilitate this work. Can accessibility tools utilizing AI be useful for UX designers and contribute to a more efficient design process? By following the Design Thinking methodology, the problem was identified, defined, and concretized. Previous research in the areas of accessibility, UX, and AI, with stakeholder interviews, and market analysis, provided a foundation for understanding the problem. Subsequently, three AI tools for accessibility were identified and evaluated through market analysis and a two-step evaluation involving the target audience of UX designers. The result shows that both UX designers and experts in AI and accessibility generally have a positive attitude toward the use of AI tools. Their willingness to use AI as a supportive tool confirms the potential of these technologies to improve the design process and result in more accessible user interfaces. When evaluating the accessibility of three different interfaces, the tools identified varying levels of accessibility shortcomings based on WCAG accessibility requirements. To ensure an accessible website according to the accessibility act, it may therefore require the use of multiple tools in combination or manual work. The results of the workshop with UX designers indicated that AI-generated accessibility suggestions can be relevant and contribute to efficiency. However, some limitations still make it important to involve human input in the design process., Denna studie syftar till att undersöka om artificiell intelligens (AI) kan användas i designprocessen för att förbättra tillgängligheten på webbplatser för användare med olika behov och förmågor. Eftersom tillgänglighetsdirektivet har blivit lag i Sverige kommer många webbplatser behöva uppfylla vissa tillgänglighetskrav. Detta är ett omfattande arbete, och AI kan potentiellt vara ett verktyg för UX-designers för att underlätta detta arbete. Kan tillgänglighetsverktyg som använder sig av AI vara användbara för UX-designers och bidra till en effektivare designprocess? Genom att följa metoden Design Thinking identifierades, definierades och konkretiserades problemet. Tidigare forskning inom områdena tillgänglighet, UX och AI, samt genomförandet av intervjuer med intressenter, skapade en grund för att förstå problemet. Därefter identifierades samt utvärderades tre AI-verktyg inom tillgänglighet genom en marknadsanalys och en tvåstegsutvärdering med målgruppen UX-designers. Resultatet visar att både UX-designers och experter inom AI och tillgänglighet överlag har en positiv inställning till användningen av AI-verktyg. Deras vilja att använda AI som ett stödverktyg bekräftar potentialen hos dessa teknologier att förbättra designprocessen och resultera i mer tillgängliga användargränssnitt. Vid utvärdering av tillgängligheten på tre olika gränssnitt identifierade verktygen olika mängd tillgänglighetsbrister utifrån WCAGs tillgänglighetskrav. För att säkerställa en tillgänglig hemsida enligt tillgänglighetsdirektivet kan det därmed krävas användning av flera verktyg i kombination eller manuellt arbete. Från workshopen med UX-designers visade resultatet att AI-genererade förslag inom tillgänglighet kan vara användbara och bidra till effektivitet. Däremot identifierades begränsningar som gör att det fortfarande är viktigt att involvera mänsklig input i designprocessen.

This study aims to investigate whether artificial intelligence (AI) can be utilized in the user experience design process to enhance the accessibility of websites for users with diverse needs and abilities. With the implementation of the European Accessibility Act into Swedish law, many websites will need to meet specific accessibility requirements, including WCAG (Web Content Accessibility Guidelines). This is a substantial task, and AI has the potential to be a tool for UX designers to facilitate this work. Can accessibility tools utilizing AI be useful for UX designers and contribute to a more efficient design process? By following the Design Thinking methodology, the problem was identified, defined, and concretized. Previous research in the areas of accessibility, UX, and AI, with stakeholder interviews, and market analysis, provided a foundation for understanding the problem. Subsequently, three AI tools for accessibility were identified and evaluated through market analysis and a two-step evaluation involving the target audience of UX designers. The result shows that both UX designers and experts in AI and accessibility generally have a positive attitude toward the use of AI tools. Their willingness to use AI as a supportive tool confirms the potential of these technologies to improve the design process and result in more accessible user interfaces. When evaluating the accessibility of three different interfaces, the tools identified varying levels of accessibility shortcomings based on WCAG accessibility requirements. To ensure an accessible website according to the accessibility act, it may therefore require the use of multiple tools in combination or manual work. The results of the workshop with UX designers indicated that AI-generated accessibility suggestions can be relevant and contribute to efficiency. However, some limitations still make it important to involve human input in the design process., Denna studie syftar till att undersöka om artificiell intelligens (AI) kan användas i designprocessen för att förbättra tillgängligheten på webbplatser för användare med olika behov och förmågor. Eftersom tillgänglighetsdirektivet har blivit lag i Sverige kommer många webbplatser behöva uppfylla vissa tillgänglighetskrav. Detta är ett omfattande arbete, och AI kan potentiellt vara ett verktyg för UX-designers för att underlätta detta arbete. Kan tillgänglighetsverktyg som använder sig av AI vara användbara för UX-designers och bidra till en effektivare designprocess? Genom att följa metoden Design Thinking identifierades, definierades och konkretiserades problemet. Tidigare forskning inom områdena tillgänglighet, UX och AI, samt genomförandet av intervjuer med intressenter, skapade en grund för att förstå problemet. Därefter identifierades samt utvärderades tre AI-verktyg inom tillgänglighet genom en marknadsanalys och en tvåstegsutvärdering med målgruppen UX-designers. Resultatet visar att både UX-designers och experter inom AI och tillgänglighet överlag har en positiv inställning till användningen av AI-verktyg. Deras vilja att använda AI som ett stödverktyg bekräftar potentialen hos dessa teknologier att förbättra designprocessen och resultera i mer tillgängliga användargränssnitt. Vid utvärdering av tillgängligheten på tre olika gränssnitt identifierade verktygen olika mängd tillgänglighetsbrister utifrån WCAGs tillgänglighetskrav. För att säkerställa en tillgänglig hemsida enligt tillgänglighetsdirektivet kan det därmed krävas användning av flera verktyg i kombination eller manuellt arbete. Från workshopen med UX-designers visade resultatet att AI-genererade förslag inom tillgänglighet kan vara användbara och bidra till effektivitet. Däremot identifierades begränsningar som gör att det fortfarande är viktigt att involvera mänsklig input i designprocessen.

This paper proposes a user study aimed at evaluating the impact of Class Activation Maps (CAMs) as an eXplainable AI (XAI) method in a radiological diagnostic task, the detection of thoracolumbar (TL) fractures from vertebral X-rays. In particular, we focus on two oft-neglected features of CAMs, that is granularity and coloring, in terms of what features, lower-level vs higher-level, should the maps highlight and adopting which coloring scheme, to bring better impact to the decision-making process, both in terms of diagnostic accuracy (that is effectiveness) and of user-centered dimensions, such as perceived confidence and utility (that is satisfaction), depending on case complexity, AI accuracy, and user expertise. Our findings show that lower-level features CAMs, which highlight more focused anatomical landmarks, are associated with higher diagnostic accuracy than higher-level features CAMs, particularly among experienced physicians. Moreover, despite the intuitive appeal of semantic CAMs, traditionally colored CAMs consistently yielded higher diagnostic accuracy across all groups. Our results challenge some prevalent assumptions in the XAI field and emphasize the importance of adopting an evidence-based and human-centered approach to design and evaluate AI- and XAI-assisted diagnostic tools. To this aim, the paper also proposes a hierarchy of evidence framework to help designers and practitioners choose the XAI solutions that optimize performance and satisfaction on the basis of the strongest evidence available or to focus on the gaps in the literature that need to be filled to move from opinionated and eminence-based research to one more based on empirical evidence and end-user work and preferences.

Companies automate their IT service management decision tasks by deploying increasingly intelligentsystems. However, there is a delicate balance: if the designers over-rely on the automatic capabilitiesoffered by the systems, then there is a risk that the end-users will not trust the algorithmically generatedsolution suggestions to be the correct ones for the task. A more balanced design approach that (i)considers human-centered intent and (ii) can provide managerial decision support that complementshuman capabilities in IT service management is thus needed. We define this as a Hybrid DecisionSupport System (HDSS). HDSSs are a class of decision support systems (DSSs) that extend the class oftraditional DSS to utilize the complementary capabilities of humans and machines for better solutionsto decision tasks. This study uses the action design research method to build, intervene, and evaluatethe designed HDSSs. Five design principles concerning how HDSSs should be built are presented.

Due to climate change, man-made conflicts, and rising inflation, a growing number of people around the world are struggling to have consistent access to safe and nutritious food. This phenomenon is known as food insecurity (FI). Therefore, we take in this thesis the first steps towards developing a monitoring process for assessing FI using Human-Centered AI (HCAI). We developed this process for, and in collaboration with, the Integrated Food Security Phase Classification (IPC). The IPC is an organization that helps countries classify levels of food insecurity in their regions to inform humanitarian decision-making. During our research process, we found that any form of HCAI for the IPC would need to be informed by input from their domain experts, and we concluded that we could not start implementing HCAI until we found a way to formalize their input in a way that was robust and suited their technical capabilities. To this end, we ran an experiment with 18 IPC experts in Malawi to see whether they could quantify their assumptions by setting thresholds for food security drivers. The results are encouraging but show that there is still much to be done to bridge the gap between domain knowledge and technical expertise. We also show in this thesis that there is a lack of real-life case studies on HCAI development and share therefore our lessons learned from our real-world HCAI case study on FI monitoring. In this way, we hope to promote the development of a practice-informed methodology for HCAI., Computer Science

The advancements in human-centered artificial intelligence (HCAI) systems for Industry 5.0 is a new phase of industrialization that places the worker at the center of the production process and uses new technologies to increase prosperity beyond jobs and growth. HCAI presents new objectives that were unreachable by either humans or machines alone, but this also comes with a new set of challenges. Our proposed method accomplishes this through the knowlEdge architecture, which enables human operators to implement AI solutions using a zero-touch framework. It relies on containerized AI model training and execution, supported by a robust data pipeline and rounded off with human feedback and evaluation interfaces. The result is a platform built from a number of components, spanning all major areas of the AI lifecycle. We outline both the architectural concepts and implementation guidelines and explain how they advance HCAI systems and Industry 5.0. In this article, we address the problems we encountered while implementing the ideas within the edge-to-cloud continuum. Further improvements to our approach may enhance the use of AI in Industry 5.0 and strengthen trust in AI systems., The research leading to these results has received funding from Horizon 2020 and the European Union’s Framework Programme for Research and Innovation (H2020/2014-2020) under grant agreement no. 957331., Peer Reviewed, Article signat per 20 autors/es: Enrico Alberti 1, Sergio Alvarez-Napagao 2, Victor Anaya 3, Marta Barroso 2, Cristian Barrué 4, Christian Beecks 5, Letizia Bergamasco 6, Sisay Adugna Chala 7, Victor Gimenez-Abalos 2, Alexander Graß 7, Daniel Hinjos 2, Maike Holtkemper 5, Natalia Jakubiak 4, Alexandros Nizamis 8, Edoardo Pristeri 6, Miquel Sànchez-Marrè 4, Georg Schlake 5, Jona Scholz 5, Gabriele Scivoletto 1, Stefan Walter 9 / 1 Nextworks Srl, Via Livornese 1027, 56122 Pisa, Italy; 2 Barcelona Supercomputing Center, Plaça Eusebi Güell 1-3, 08034 Barcelona, Spain; 3 Information Catalyst SL, Cl Reina 27, 4-7, 46800 Xativa, Spain; 4 Department of Computer Science, IDEAI Research Centre, Universitat Politècnica de Catalunya (UPC), Carrer Jordi Girona 1-3, 08034 Barcelona, Spain; 5 Department of Data Science, University of Hagen, 58097 Hagen, Germany; 6 LINKS Foundation, Via Pier Carlo Boggio 61, 10138 Torino, Italy; 7 Fraunhofer Institute for Applied Information Technology (FIT), Schloss Birlinghoven, 53757 Sankt Augustin, Germany; 8 Centre for Research and Technology Hellas-Information Technologies Institute (CERTH/ITI), Charilaou-Thermis, 57001 Thessaloniki, Greece; 9 VTT Technical Research Centre of Finland Ltd., Tekniikantie 21, 02150 Espoo, Finland, Objectius de Desenvolupament Sostenible::9 - Indústria, Innovació i Infraestructura, Objectius de Desenvolupament Sostenible::12 - Producció i Consum Responsables, Postprint (published version)

The evolution of clinical decision support (CDS) tools has been improved by usage of new technologies, yet there is an increased need to develop user-friendly, evidence-based, and expert-curated CDS solutions. In this paper, we show with a use-case how interdisciplinary expertise can be combined to develop CDS tool for hospital readmission prediction of heart failure patients. We also discuss how to make the tool integrated in clinical workflow by understanding end-user needs and have clinicians-in-the-loop during the different development stages. © 2023 European Federation for Medical Informatics (EFMI) and IOS Press., This research is part of the CAISR Health research profile.

The evolution of clinical decision support (CDS) tools has been improved by usage of new technologies, yet there is an increased need to develop user-friendly, evidence-based, and expert-curated CDS solutions. In this paper, we show with a use-case how interdisciplinary expertise can be combined to develop CDS tool for hospital readmission prediction of heart failure patients. We also discuss how to make the tool integrated in clinical workflow by understanding end-user needs and have clinicians-in-the-loop during the different development stages. © 2023 European Federation for Medical Informatics (EFMI) and IOS Press., This research is part of the CAISR Health research profile.

The evolution of clinical decision support (CDS) tools has been improved by usage of new technologies, yet there is an increased need to develop user-friendly, evidence-based, and expert-curated CDS solutions. In this paper, we show with a use-case how interdisciplinary expertise can be combined to develop CDS tool for hospital readmission prediction of heart failure patients. We also discuss how to make the tool integrated in clinical workflow by understanding end-user needs and have clinicians-in-the-loop during the different development stages. © 2023 European Federation for Medical Informatics (EFMI) and IOS Press., This research is part of the CAISR Health research profile.

The evolution of clinical decision support (CDS) tools has been improved by usage of new technologies, yet there is an increased need to develop user-friendly, evidence-based, and expert-curated CDS solutions. In this paper, we show with a use-case how interdisciplinary expertise can be combined to develop CDS tool for hospital readmission prediction of heart failure patients. We also discuss how to make the tool integrated in clinical workflow by understanding end-user needs and have clinicians-in-the-loop during the different development stages. © 2023 European Federation for Medical Informatics (EFMI) and IOS Press., This research is part of the CAISR Health research profile.

Artificial Intelligence can both empower individuals to face complex societal challenges and exacerbate problems and vulnerabilities, such as bias, inequalities, and polarization. For scientists, an open challenge is how to shape and regulate human-centered Artificial Intelligence ecosystems that help mitigate harms and foster beneficial outcomes oriented at the social good. In this tutorial, we discuss such an issue from two sides. First, we explore the network effects of Artificial Intelligence and their impact on society by investigating its role in social media, mobility, and economic scenarios. We further provide different strategies that can be used to model, characterize and mitigate the network effects of particular Artificial Intelligence driven individual behavior. Secondly, we promote the use of behavioral models as an addition to the data-based approach to get a further grip on emerging phenomena in society that depend on physical events for which no data are readily available. An example of this is tracking extremist behavior in order to prevent violent events. In the end, we illustrate some case studies in-depth and provide the appropriate tools to get familiar with these concepts., Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)Conference series: ACAI: ECCAI Advanced Course on Artificial Intelligence

To ensure that AI-infused systems work for disabled people, we need to bring accessibility datasets sourced from this community in the development lifecycle. However, there are many ethical and privacy concerns limiting greater data inclusion, making such datasets not readily available. We present a pair of studies where 13 blind participants engage in data capturing activities and refect with and without probing on various factors that infuence their decision to share their data via an AI dataset. We see how diferent factors infuence blind participants’ willingness to share study data as they assess risk-beneft tradeofs. The majority support sharing of their data to improve technology but also express concerns over commercial use, associated metadata, and the lack of transparency about the impact of their data. These insights have implications for the development of responsible practices for stewarding accessibility datasets, and can contribute to broader discussions in this area.

The ability to automatically infer relevant aspects of human users' thoughts and feelings is crucial for technologies to intelligently adapt their behaviors in complex interactions. Research on multimodal analysis has demonstrated the potential of technology to provide such estimates for a broad range of internal states and processes. However, constructing robust approaches for deployment in real-world applications remains an open problem. The MSECP-Wild workshop series is a multidisciplinary forum to present and discuss research addressing this challenge. Submissions to this 5th iteration span efforts relevant to multimodal data collection, modeling, and applications. In addition, our workshop program builds on discussions emerging in previous iterations, highlighting ethical considerations when building and deploying technology modeling internal states in the wild. For this purpose, we host a range of relevant keynote speakers and interactive activities., Pattern Recognition and Bioinformatics

As agile manufacturing expands and workforce mobility increases, the importance of efficient knowledge transfer among factory workers grows. Cognitive Assistants (CAs) with Large Language Models (LLMs), like GPT-3.5, can bridge knowledge gaps and improve worker performance in manufacturing settings. This study investigates the opportunities, risks, and user acceptance of LLM-powered CAs in two factory contexts: textile and detergent production. Several opportunities and risks are identified through a literature review, proof-of-concept implementation, and focus group sessions. Factory representatives raise concerns regarding data security, privacy, and the reliability of LLMs in high-stake environments. By following design guidelines regarding persistent memory, real-time data integration, security, privacy, and ethical concerns, LLM-powered CAs can become valuable assets in manufacturing settings and other industries., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Internet of Things, Human-Centred Artificial Intelligence

With artificial intelligence (AI) systems entering our working and leisure environments with increasing adaptation and learning capabilities, new opportunities arise for developing hybrid (human-AI) intelligence (HI) systems, comprising new ways of collaboration. However, there is not yet a structured way of specifying design solutions of collaboration for hybrid intelligence (HI) systems and there is a lack of best practices shared across application domains. We address this gap by investigating the generalization of specific design solutions into design patterns that can be shared and applied in different contexts. We present a human-centered bottom-up approach for the specification of design solutions and their abstraction into team design patterns. We apply the proposed approach for 4 concrete HI use cases and show the successful extraction of team design patterns that are generalizable, providing re-usable design components across various domains. This work advances previous research on team design patterns and designing applications of HI systems., BUS/TNO STAFF, Interactive Intelligence

Many industries face the challenge of capturing workers' knowledge to share it, particularly tacit knowledge. The operation of complex systems such as a manufacturing line is knowledge-intensive. Considering this knowledge's breadth and dynamic nature, existing knowledge-sharing solutions are inefficient and resource intensive. Conversational user interfaces are an efficient way to convey information that mimics how humans share knowledge; however, we know little about how to design them specifically for knowledge sharing, especially regarding tacit knowledge. In this work, we present an intelligent assistant that we have developed to support the elicitation of tacit knowledge from workers through systematic reflection. The system can interact with workers by voice or text and generate visualizations of shop floor data to support reflective prompts., Internet of Things, Human-Centred Artificial Intelligence

The evolution of clinical decision support (CDS) tools has been improved by usage of new technologies, yet there is an increased need to develop user-friendly, evidence-based, and expert-curated CDS solutions. In this paper, we show with a use-case how interdisciplinary expertise can be combined to develop CDS tool for hospital readmission prediction of heart failure patients. We also discuss how to make the tool integrated in clinical workflow by understanding end-user needs and have clinicians-in-the-loop during the different development stages. © 2023 European Federation for Medical Informatics (EFMI) and IOS Press., This research is part of the CAISR Health research profile.

Practitioners are urging using Artificial Intelligence (AI) to improve advertisements. Advertisers recognize the importance of incorporating AI into their strategies to remain competitive. In response to this demand, a Design Science Research (DSR) initiative has been started to create a Human-Centered AI (HCAI) tool to enhance advertisement suggestions by analyzing consumer behavior. This dissertation aims to build an advertisement optimization system with HCAI and produce an Information System Design Theory (ISDT) of that class of system. Through architectural models, methods, technological rules, and design principles, nascent Design Theory (DT) is created and serves as an initial stage towards achieving a more abstract design knowledge known as the ISDT. The action design research method is employed to construct and analyze the implemented system instance. The process involves multiple cycles of building, intervening, and evaluating. These cycles are conducted iteratively and incrementally, allowing for the gradual development of the suggested system while simultaneously generating valuable design knowledge. The system is developed and abstracted for design knowledge from both the development process and the actual tool. The dissertation presents nascent design knowledge in the form of models, technological rules, and design principles. Moreover, the dissertation places the nascent DT within the broader context of a more abstract design knowledge called ISDT. The results are then scrutinized based on various components of the DT, including purpose and scope, constructs, principles of form and function, artifact mutability, justificatory knowledge, testable propositions, principles of implementation, and expository instantiation. This dissertation discusses the DSR process, compared to various challenges encountered throughout the research project. Theoretical, empirical, and artefactual research contributions are outlined, and their implications for research and, Partly funded by The Knowledge Foundation, grants nr. 20160035, 20170215Två av sex delarbeten (övriga se rubriken Delarbeten/List of papers):Sahlin, Johannes, Håkan Sundell, Gideon Mbiydzenyuy, and Jesper Holgersson (2023). “Managing Consumer Concerns of Model-generated Advertisements.” In: Expert Systems with Applications, Submitted.— (2024). “Nascent Design Theory for Advertisement Optimization Systems with Human-centered Artificial Intelligence.” In: European Conference on Information Systems, Draft.

Practitioners are urging using Artificial Intelligence (AI) to improve advertisements. Advertisers recognize the importance of incorporating AI into their strategies to remain competitive. In response to this demand, a Design Science Research (DSR) initiative has been started to create a Human-Centered AI (HCAI) tool to enhance advertisement suggestions by analyzing consumer behavior. This dissertation aims to build an advertisement optimization system with HCAI and produce an Information System Design Theory (ISDT) of that class of system. Through architectural models, methods, technological rules, and design principles, nascent Design Theory (DT) is created and serves as an initial stage towards achieving a more abstract design knowledge known as the ISDT. The action design research method is employed to construct and analyze the implemented system instance. The process involves multiple cycles of building, intervening, and evaluating. These cycles are conducted iteratively and incrementally, allowing for the gradual development of the suggested system while simultaneously generating valuable design knowledge. The system is developed and abstracted for design knowledge from both the development process and the actual tool. The dissertation presents nascent design knowledge in the form of models, technological rules, and design principles. Moreover, the dissertation places the nascent DT within the broader context of a more abstract design knowledge called ISDT. The results are then scrutinized based on various components of the DT, including purpose and scope, constructs, principles of form and function, artifact mutability, justificatory knowledge, testable propositions, principles of implementation, and expository instantiation. This dissertation discusses the DSR process, compared to various challenges encountered throughout the research project. Theoretical, empirical, and artefactual research contributions are outlined, and their implications for research and, Partly funded by The Knowledge Foundation, grants nr. 20160035, 20170215Två av sex delarbeten (övriga se rubriken Delarbeten/List of papers):Sahlin, Johannes, Håkan Sundell, Gideon Mbiydzenyuy, and Jesper Holgersson (2023). “Managing Consumer Concerns of Model-generated Advertisements.” In: Expert Systems with Applications, Submitted.— (2024). “Nascent Design Theory for Advertisement Optimization Systems with Human-centered Artificial Intelligence.” In: European Conference on Information Systems, Draft.

Significant and rapid advancements in cancer research have been attributed to Artificial Intelligence (AI). However, AI’s role and impact on the clinical side has been limited. This discrepancy manifests due to the overlooked, yet profound, differences in the clinical and research practices in oncology. Our contribution seeks to scrutinize physicians' engagement with AI by interviewing 7 medical-imaging experts and disentangle its future alignment across the clinical and research workflows, diverging from the existing "one-size-fits-all" paradigm within Human-Centered AI discourses. Our analysis revealed that physicians' trust in AI is less dependent on their general acceptance of AI, but more on their contestable experiences with AI. Contestability, in clinical workflows, underpins the need for personal supervision of AI outcomes and processes, i.e., clinician-in-the-loop. Finally, we discuss tensions in the desired attributes of AI, such as explainability and control, contextualizing them within the divergent intentionality and scope of clinical and research workflows., Human-Centred Artificial Intelligence

Practitioners are urging using Artificial Intelligence (AI) to improve advertisements. Advertisers recognize the importance of incorporating AI into their strategies to remain competitive. In response to this demand, a Design Science Research (DSR) initiative has been started to create a Human-Centered AI (HCAI) tool to enhance advertisement suggestions by analyzing consumer behavior. This dissertation aims to build an advertisement optimization system with HCAI and produce an Information System Design Theory (ISDT) of that class of system. Through architectural models, methods, technological rules, and design principles, nascent Design Theory (DT) is created and serves as an initial stage towards achieving a more abstract design knowledge known as the ISDT. The action design research method is employed to construct and analyze the implemented system instance. The process involves multiple cycles of building, intervening, and evaluating. These cycles are conducted iteratively and incrementally, allowing for the gradual development of the suggested system while simultaneously generating valuable design knowledge. The system is developed and abstracted for design knowledge from both the development process and the actual tool. The dissertation presents nascent design knowledge in the form of models, technological rules, and design principles. Moreover, the dissertation places the nascent DT within the broader context of a more abstract design knowledge called ISDT. The results are then scrutinized based on various components of the DT, including purpose and scope, constructs, principles of form and function, artifact mutability, justificatory knowledge, testable propositions, principles of implementation, and expository instantiation. This dissertation discusses the DSR process, compared to various challenges encountered throughout the research project. Theoretical, empirical, and artefactual research contributions are outlined, and their implications for research and, Partly funded by The Knowledge Foundation, grants nr. 20160035, 20170215Två av sex delarbeten (övriga se rubriken Delarbeten/List of papers):Sahlin, Johannes, Håkan Sundell, Gideon Mbiydzenyuy, and Jesper Holgersson (2023). “Managing Consumer Concerns of Model-generated Advertisements.” In: Expert Systems with Applications, Submitted.— (2024). “Nascent Design Theory for Advertisement Optimization Systems with Human-centered Artificial Intelligence.” In: European Conference on Information Systems, Draft.

Learning to operate a complex system, such as an agile production line, can be a daunting task. The high variability in products and frequent reconfigurations make it difficult to keep documentation up-to-date and share new knowledge amongst factory workers. We introduce CLAICA, a Continuously Learning AI Cognitive Assistant that supports workers in the aforementioned scenario. CLAICA learns from (experienced) workers, formalizes new knowledge, stores it in a knowledge base, along with contextual information, and shares it when relevant. We conducted a user study with 83 participants who performed eight knowledge exchange tasks with CLAICA, completed a survey, and provided qualitative feedback. Our results provide a deeper understanding of how prior training, context expertise, and interaction modality affect the user experience of cognitive assistants. We draw on our results to elicit design and evaluation guidelines for cognitive assistants that support knowledge exchange in fast-paced and demanding environments, such as an agile production line., Internet of Things, Human-Centred Artificial Intelligence

Web Information Systems, Human-Centred Artificial Intelligence

Explainable AI (XAI) seeks to disclose how an AI system arrives at its outcomes. But the nature of the disclosure depends in part on who needs to understand the AI and the available explanation modalities (e.g., verbal and visual). Users’ preferences regarding explanation modalities might differ, as some might prefer spoken explanations compared to visual ones. However, we argue for broadening the explanation modalities, to consider also tangible and physical forms. In traditional product design, physical forms have mediated people’s interactions with objects; more recently interacting with physical forms has become prominent with IoT and smart devices, such as smart lighting and robotic vacuum cleaners. But how tangible interaction can support AI explanations is not yet well understood. In this second studio proposal on Graspable AI (GAI) we seek to explore design qualities of physical forms as an explanation modality for XAI. We anticipate that the design qualities of physical forms and their tangible interactivity can not only contribute to the explainability of AI through facilitating dialogue, relationships and human empowerment, but they can also contribute to critical and reflective discourses on AI. Therefore, this proposal contributes to design agendas that expand explainable AI into tangible modalities, supporting a more diverse range of users in their understanding of how a given AI works and the meanings of its outcomes.

The use of autonomous agents is an ever-growing possibility in our day-to-day life and, in some cases, already a reality. One future use might be autonomous robots performing last mile deliveries, a service the company HUGO delivery is currently developing. The goal of developing their autonomous delivery robot HUGO is to reduce the emissions from deliveries in the last mile by replacing delivery trucks with emission free autonomous robots. However, this new way of receiving deliveries introduces new design challenges since most people have little to no prior experience of interacting with autonomous agents. The user interface is therefore of great importance in making the user understand and be able to interact comfortably with the autonomous agent, thus also a key aspect in reaching user adoption. This thesis work examines how an interface for an autonomous food delivery service, such as the HUGO delivery, could be designed by applying a Human-Centered Artificial intelligence and Activity Centered Design focus in the design process, resulting in a design proposal for a web app. The conclusion of the thesis includes identification of the six essential interactions present in an autonomous food delivery service, as well as how HCAI and which of its guidelines can be applied when designing an interface for the interaction with an autonomous delivery robot.

The use of autonomous agents is an ever-growing possibility in our day-to-day life and, in some cases, already a reality. One future use might be autonomous robots performing last mile deliveries, a service the company HUGO delivery is currently developing. The goal of developing their autonomous delivery robot HUGO is to reduce the emissions from deliveries in the last mile by replacing delivery trucks with emission free autonomous robots. However, this new way of receiving deliveries introduces new design challenges since most people have little to no prior experience of interacting with autonomous agents. The user interface is therefore of great importance in making the user understand and be able to interact comfortably with the autonomous agent, thus also a key aspect in reaching user adoption. This thesis work examines how an interface for an autonomous food delivery service, such as the HUGO delivery, could be designed by applying a Human-Centered Artificial intelligence and Activity Centered Design focus in the design process, resulting in a design proposal for a web app. The conclusion of the thesis includes identification of the six essential interactions present in an autonomous food delivery service, as well as how HCAI and which of its guidelines can be applied when designing an interface for the interaction with an autonomous delivery robot.

Explainable AI (XAI) seeks to disclose how an AI system arrives at its outcomes. But the nature of the disclosure depends in part on who needs to understand the AI and the available explanation modalities (e.g., verbal and visual). Users' preferences regarding explanation modalities might differ, as some might prefer spoken explanations compared to visual ones. However, we argue for broadening the explanation modalities, to consider also tangible and physical forms. In traditional product design, physical forms have mediated people's interactions with objects; more recently interacting with physical forms has become prominent with IoT and smart devices, such as smart lighting and robotic vacuum cleaners. But how tangible interaction can support AI explanations is not yet well understood. In this second studio proposal on Graspable AI (GAI) we seek to explore design qualities of physical forms [12] as an explanation modality for XAI. We anticipate that the design qualities of physical forms and their tangible interactivity can not only contribute to the explainability of AI through facilitating dialogue [5], relationships [18] and human empowerment [15], but they can also contribute to critical and reflective discourses on AI [2, 13]. Therefore, this proposal contributes to design agendas that expand explainable AI into tangible modalities, supporting a more diverse range of users in their understanding of how a given AI works and the meanings of its outcomes., QC 20221107

Robots sharing their space with humans need to be proactive to be helpful. Proactive robots can act on their own initiatives in an anticipatory way to benefit humans. In this work, we investigate two ways to make robots proactive. One way is to recognize human intentions and to act to fulfill them, like opening the door that you are about to cross. The other way is to reason about possible future threats or opportunities and to act to prevent or to foster them, like recommending you to take an umbrella since rain has been forecast. In this article, we present approaches to realize these two types of proactive behavior. We then present an integrated system that can generate proactive robot behavior by reasoning on both factors: intentions and predictions. We illustrate our system on a sample use case including a domestic robot and a human. We first run this use case with the two separate proactive systems, intention-based and prediction-based, and then run it with our integrated system. The results show that the integrated system is able to consider a broader variety of aspects that are required for proactivity.

The sixth HUMANIZE workshop1 on Transparency and Explainability in Adaptive Systems through User Modeling Grounded in Psychological Theory took place in conjunction with the 27th annual meeting of the Intelligent User Interfaces (IUI)2 community that was hosted virtually by the University of Helsinki (Finland) on March 22, 2022. The 2022 edition of the workshop was held together with TExSS (Transparency and Explanations in Smart Systems) 3. The workshop provided a venue for researchers from different fields to interact by accepting contributions on the intersection of practical data mining methods and theoretical knowledge for personalization. A total of two papers was accepted for this edition of the workshop., Export Date: 2 May 2022; Conference Paper

Explainable AI (XAI) seeks to disclose how an AI system arrives at its outcomes. But the nature of the disclosure depends in part on who needs to understand the AI and the available explanation modalities (e.g., verbal and visual). Users’ preferences regarding explanation modalities might differ, as some might prefer spoken explanations compared to visual ones. However, we argue for broadening the explanation modalities, to consider also tangible and physical forms. In traditional product design, physical forms have mediated people’s interactions with objects; more recently interacting with physical forms has become prominent with IoT and smart devices, such as smart lighting and robotic vacuum cleaners. But how tangible interaction can support AI explanations is not yet well understood. In this second studio proposal on Graspable AI (GAI) we seek to explore design qualities of physical forms as an explanation modality for XAI. We anticipate that the design qualities of physical forms and their tangible interactivity can not only contribute to the explainability of AI through facilitating dialogue, relationships and human empowerment, but they can also contribute to critical and reflective discourses on AI. Therefore, this proposal contributes to design agendas that expand explainable AI into tangible modalities, supporting a more diverse range of users in their understanding of how a given AI works and the meanings of its outcomes.

The inevitable rise and development of artificial intelligence (AI) was not a sudden occurrence. The greater the effect that AI has on humans, the more pressing the need is for us to understand it. This paper addresses research on the use of AI to evaluate new design methods and tools that can be leveraged to advance AI research, education, policy, and practice to improve the human condition. AI has the potential to educate, train, and improve the performance of humans, making them better at their tasks and activities. The use of AI can enhance human welfare in numerous respects, such as through improving the productivity of food, health, water, education, and energy services. However, the misuse of AI due to algorithm bias and a lack of governance could inhibit human rights and result in employment, gender, and racial inequality. We envision that AI can evolve into human-centered AI (HAI), which refers to approaching AI from a human perspective by considering human conditions and contexts. Most current discussions on AI technology focus on how AI can enable human performance. However, we explore AI can also inhibit the human condition and advocate for an in-depth dialog between technology- and humanity-based researchers to improve understanding of HAI from various perspectives.

Applications using Artificial Intelligence (AI) have become commonplace and embedded in our daily lives. Much of our communication has transitioned from human–human interaction to human–technology or technology-mediated interaction. As technology is handed over control and streamlines choices and decision-making in different contexts, people are increasingly concerned about a potential threat to their autonomy. In this paper, we explore autonomy perception when interacting with AI-based applications in everyday contexts using a design fiction-based survey with 328 participants. We probed if providing users with explanations on “why” an application made certain choices or decisions influenced their perception of autonomy or reactance regarding the interaction with the applications. We also looked at changes in perception when users are aware of AI's presence in an application. In the social media context, we found that people perceived a greater reactance and lower sense of autonomy perhaps owing to the personal and identity-sensitive nature of the application context. Providing explanations on “why” in the navigation context, contributed to enhancing their autonomy perception, and reducing reactance since it influenced the users' subsequent actions based on the recommendation. We discuss our findings and the implications it has for the future development of everyday AI applications that respect human autonomy.

Applications using Artificial Intelligence (AI) have become commonplace and embedded in our daily lives. Much of our communication has transitioned from human–human interaction to human–technology or technology-mediated interaction. As technology is handed over control and streamlines choices and decision-making in different contexts, people are increasingly concerned about a potential threat to their autonomy. In this paper, we explore autonomy perception when interacting with AI-based applications in everyday contexts using a design fiction-based survey with 328 participants. We probed if providing users with explanations on “why” an application made certain choices or decisions influenced their perception of autonomy or reactance regarding the interaction with the applications. We also looked at changes in perception when users are aware of AI's presence in an application. In the social media context, we found that people perceived a greater reactance and lower sense of autonomy perhaps owing to the personal and identity-sensitive nature of the application context. Providing explanations on “why” in the navigation context, contributed to enhancing their autonomy perception, and reducing reactance since it influenced the users' subsequent actions based on the recommendation. We discuss our findings and the implications it has for the future development of everyday AI applications that respect human autonomy.

The fifth HUMANIZE workshop1 on Transparency and Explainability in Adaptive Systems through User Modeling Grounded in Psychological Theory took place in conjunction with the 26th annual meeting of the Intelligent User Interfaces (IUI)2 community in Texas, USA on April 17, 2021. The workshop provided a venue for researchers from different fields to interact by accepting contributions on the intersection of practical data mining methods and theoretical knowledge for personalization. A total of five papers was accepted for this edition of the workshop.

In the area of consumer robots that need to have rich social interactions with humans, one of the challenges is the complexity of computing the appropriate interactions in a cognitive, social and physical context. We propose a novel appproach for social robots based on the concept of Social Practices. By using social practices robots are able to be aware of their own social identities (given by the role in the social practice) and the identities of others and also be able to identify the different social contexts and the appropriate social interactions that go along with those contexts and identities., This work was supported by ANR project JointAction4HRI (ANR-16-CE33-0017)., Peer Reviewed, Postprint (author's final draft)