@inproceedings{MuchaJacobiRobert2019, author = {Mucha, Henrik and Jacobi, Ricarda and Robert, Sebastian}, title = {Partizipation und K{\"u}nstliche Intelligenz}, series = {Mensch und Computer 2019 - Workshopband}, booktitle = {Mensch und Computer 2019 - Workshopband}, organization = {Gesellschaft f{\"u}r Informatik e.V.}, pages = {19 -- 21}, year = {2019}, abstract = {Der Einsatz k{\"u}nstlicher Intelligenz (KI) wird unsere Lebenswirklichkeit nachhaltig beeinflussen. Mittels technischer Verfahren maschinellen Lernens oder tiefer neuronaler Netze k{\"o}nnen Modelle aus existierenden Datenbest{\"a}nden gelernt und Vorhersagen abgeleitet werden. Wir stellen skizzenartig zwei aktuelle Projekte vor, die sich u.a. mit den Potentialen und der menschzentrierten Gestaltung von Systemen k{\"u}nstlicher Intelligenz besch{\"a}ftigen. Wir betten dies schlaglichtartig in den Kontext der aktuellen Debatte {\"u}ber die Entwicklung und Gestaltung von KI Systemen ein. Im Zuge dessen umreißen wir Herausforderungen und Chancen f{\"u}r die partizipative und sozialverantwortliche Technologieentwicklung von komplexen und auf Verfahren der k{\"u}nstlichen Intelligenz basierenden Systemen. Schließlich fokussieren wir dabei auf die Arbeitsebene und beschreiben kurz, welche konkreten Aktivit{\"a}ten wir planen und stellen diese zur Diskussion.}, language = {de} } @inproceedings{MuchaRobertBreitschwerdtetal.2021, author = {Mucha, Henrik and Robert, Sebastian and Breitschwerdt, Ruediger and Fellmann, Michael}, title = {Interfaces for Explanations in Human-AI Interaction: Proposing a Design Evaluation Approach}, series = {Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA}, booktitle = {Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA}, pages = {327}, year = {2021}, abstract = {Explanations in Human-AI Interaction are communicated to human decision makers through interfaces. Yet, it is not clear what consequences the exact representation of such explanations as part of decision support systems (DSS) and working on machine learning (ML) models has on human decision making. We observe a need for research methods that allow for measuring the effect different eXplainable AI (XAI) interface designs have on people's decision making. In this paper, we argue for adopting research approaches from decision theory for HCI research on XAI interface design. We outline how we used estimation tasks in human-grounded design research in order to introduce a method and measurement for collecting evidence on XAI interface effects. To this end, we investigated representations of LIME explanations in an estimation task online study as proof-of-concept for our proposal.}, language = {en} } @inproceedings{MuchaMevissenRobertetal.2020, author = {Mucha, Henrik and Mevißen, Dennis and Robert, Sebastian and Jacobi, Ricarda and Meyer, Kirsten and Heusler, Winfried and Arztmann, Daniel}, title = {Co-Design Futures for AI and Space: A Workbook Sprint}, series = {Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems (CHI EA '20). Association for Computing Machinery, New York, NY, USA}, booktitle = {Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems (CHI EA '20). Association for Computing Machinery, New York, NY, USA}, pages = {1 -- 8}, year = {2020}, abstract = {Artificial Intelligence (AI) is continuously moving into our surroundings. In its various forms, it has the potential to disrupt most aspects of human life. Yet, the discourse around AI has long been by experts and for experts. In this paper, we argue for a participatory approach towards designing human-AI interactions. We outline how we used design methodology to organise an interdisciplinary workshop with a diverse group of students - a workbook sprint with 45 participants from four different programs and 13 countries - to develop speculative design futures in five focus areas. We then provide insights into our findings and share our lessons learned regarding our workshop topic - AI and Space - our process, and our research. We learned that involving non-experts in complex technical discourses - such as AI - through the structural rigour of design methodology is a viable approach. We then conclude by laying out how others might use our findings and initiate their own workbook sprint to explore complex technologies in a human-centred way.}, language = {en} } @inproceedings{MuchaRobertBreitschwerdtetal.2020, author = {Mucha, Henrik and Robert, Sebastian and Breitschwerdt, R{\"u}diger and Fellmann, Michael}, title = {Towards Participatory Design Spaces for Explainable AI Interfaces in Expert Domains}, series = {CEUR Workshop Proceedings XI-ML 2020}, booktitle = {CEUR Workshop Proceedings XI-ML 2020}, year = {2020}, abstract = {In this position paper, we lay out an approach to use participatory and co-design methodology to explore how users perceive and interact with explanations of artificially intelligent decision support systems. We describe how we intend to construct bottom-up participatory design spaces to systematically inform the design of interactive explanations in Human-AI interaction.}, language = {en} } @inproceedings{MuchaRobert2020, author = {Mucha, Henrik and Robert, Sebastian}, title = {Emerging Perspectives on Medical Decision Support: Co-Designing XAI}, series = {Fair \& Responsible AI Workshop, Conference on Human Factors in Computing Systems (CHI), 2020, Honolulu, Hawaii}, booktitle = {Fair \& Responsible AI Workshop, Conference on Human Factors in Computing Systems (CHI), 2020, Honolulu, Hawaii}, year = {2020}, abstract = {Artificial Intelligence (AI) and Machine Learning (ML) promise to significantly enhance the capabilities of decision support systems in medicine. Yet, if these systems fail to providean understandable rationale of the decision making process the adoption of this powerful technology will be difficult. Hence, there is growing interest in Explainable Artificial Intelligence (XAI). Explanations that speak the language of the user are a cornerstone for Fair AI. In this position paper, we reflect on research about XAI and designing decision support in medicine. From there, we lay out an approach to use co-design methodology to explore how users perceive and interact with explanations of decision support systems.}, language = {en} } @article{MuchaRobertBreitschwerdtetal.2022, author = {Mucha, Henrik and Robert, Sebastian and Breitschwerdt, R{\"u}diger and Fellmann, Michael}, title = {Usability of clinical decision support systems}, series = {Zeitschrift f{\"u}r Arbeitswissenschaft}, journal = {Zeitschrift f{\"u}r Arbeitswissenschaft}, doi = {10.1007/s41449-022-00324-8}, year = {2022}, abstract = {Usability is considered a major success factor for current and future decision support systems. Such systems are increasingly used to assist human decision-makers in high-stakes tasks in complex domains such as health care, jurisdiction or finance. Yet, many if not most expert systems—especially in health care—fail to deliver the degree of quality in terms of usability that its expert users are used to from their personal digital consumer products. In this article, we focus on clinical decision support systems (CDSS) as an example for how important a human-centered design approach is when designing complex software in complex contexts. We provide an overview of CDSS classes, discuss the importance of systematically exploring mental models of users, and formulate challenges and opportunities of future design work on CDSS. We further provide a case study from a current research project to illustrate how we used codesign as a practical approach to produce usable software in a real-world context. Practical Relevance: We make a point for usability to be considered a major success factor and non-negotiable characteristic of expert software. With software evolving into virtual coworkers in terms of supporting human decision-making in complex, high-risk domains, the necessity of and demand for systems that are unambiguously understandable and interpretable for their expert users have never been higher. We show that this is a real-world problem with high practical relevance by describing our work in the domain of clinical decision support systems (CDSS) as an example. We introduce the topic and a classification of CDSS. Thus, we highlight a conceptual framework of how to approach complex domains from a technology designer's point of view. We continue by explaining why usability must be regarded as a major goal in software development. We derive challenges and opportunities that may well be transferred to other domains. Finally, be including a real-world example from our own professional work we propose a practical approach towards taking the challenges and exploiting the associated opportunities.}, language = {en} }