arXiv subjects

Claudia Müller-Birn

Publications and source records attributed to Claudia Müller-Birn.

17 recordsLinked to original sources

arXiv2026

What Clinicians Need: Designing, Developing and Evaluating an AI-Based Decision Support System for Autism Assessment

AI methods promise to support autism spectrum condition (ASC) diagnostics in adults, a complex and time-consuming process, that is characterized by a shortage of specialized clinicians. To date, clinicians' needs and their interaction with such AI-based support remain underexplored. Our work aims to develop and evaluate an AI-based clinical decision support system (CDSS) for ASC assessment, and to investigate how it impacts clinicians' decision-making. By interviewing clinicians of varying experience levels, we identified five challenges and derived design strategies. Based on that, we developed SIT-CARE, a CDSS, which provides AI-based recommendations and data visualizations of clinically relevant nonverbal behavior. Through an evaluation study with newly recruited clinicians, we found that SIT-CARE led to different decision paths in regard to the ASC assessment, which are reflected in clinicians' mental models and decision changes. Overall, SIT-CARE demonstrated potential in improving initial diagnostic assessments, supporting in-depth diagnosis and empowering less experienced clinicians.

cs.HC↗

arXiv2025

Designing Value-Centered Consent Interfaces: A Mixed-Methods Approach to Support Patient Values in Data-Sharing Decisions

In the digital health domain, ethical data collection practices are crucial for ensuring the availability of quality datasets that drive medical advancement. Data donation, allowing patients to share their medical data for secondary research purposes, presents a promising resource for such datasets. Yet, current consent user interfaces mediating data-sharing decisions are found to favor data collectors' values over those of data subjects. This raises ethical concerns about the use of data collected, as well as concerning the quality of the resulting datasets. Seeking to establish value-centered data collection practices in digital health, we investigate the design of consent user interfaces that support end-users in making value-congruent health data-sharing decisions. Focusing our research efforts on the situated context of health data donation at the psychosomatic unit of a university hospital, we demonstrate how a human-centered design can ground technology within the perspective of a vulnerable group. We employed an exploratory sequential mixed-method approach consisting of five phases: Participatory workshops elicit patient values, informing the design of a proposed Value-Centered Consent Interface. An online experiment demonstrates our interface element's effect, increasing value congruence in data-sharing decisions. Our proposed consent user interface design is then adapted to the research context through a co-creation workshop with domain experts and a user interface evaluation with patients. Our work contributes to recent discourse in CSCW concerning ethical implications of new data practices within their socio-technological context by exploring patient values on medical data-sharing, introducing a novel consent interface leveraging reflection to support value-congruent decision-making, and providing a situated evaluation of the proposed consent user interface with patients.

cs.HC↗

arXiv2025

Design Requirements for Patient-Centered Digital Health Applications: Supporting Patients' Values in Postoperative Delirium Prevention

Postoperative delirium (POD) is among the most common complications after surgeries for older adults and can entail long-term adverse health consequences. Active patient participation in POD prevention presents a central factor in reducing these risks. To support patient engagement through a digital health application, we use value sensitive design approaches to identify the requirements for a patient-centered digital health application supporting patient engagement in POD prevention. Through interviews with medical professionals and patient representatives, we construct a patient journey, which serves as the basis for twelve patient value journey interviews. In these interviews, patients from the high-risk group for POD revisit their recent experience of undergoing surgery to elicit barriers, needs, and values concerning POD prevention from a patient perspective. An analysis of the patient interviews derives four design requirements for a digital health application supporting patients regarding POD prevention: the adaptation of patient-centered communication, the provision of procedural transparency, fostering patient empowerment through consistent guidance, and explicitly addressing relatives as mediators and supporters for a patient after a POD occurrence.

cs.HC↗

arXiv2023

Identifying Explanation Needs of End-users: Applying and Extending the XAI Question Bank

Explanations in XAI are typically developed by AI experts and focus on algorithmic transparency and the inner workings of AI systems. Research has shown that such explanations do not meet the needs of users who do not have AI expertise. As a result, explanations are often ineffective in making system decisions interpretable and understandable. We aim to strengthen a socio-technical view of AI by following a Human-Centered Explainable Artificial Intelligence (HC-XAI) approach, which investigates the explanation needs of end-users (i.e., subject matter experts and lay users) in specific usage contexts. One of the most influential works in this area is the XAI Question Bank (XAIQB) by Liao et al. The authors propose a set of questions that end-users might ask when using an AI system, which in turn is intended to help developers and designers identify and address explanation needs. Although the XAIQB is widely referenced, there are few reports of its use in practice. In particular, it is unclear to what extent the XAIQB sufficiently captures the explanation needs of end-users and what potential problems exist in the practical application of the XAIQB. To explore these open questions, we used the XAIQB as the basis for analyzing 12 think-aloud software explorations with subject matter experts. We investigated the suitability of the XAIQB as a tool for identifying explanation needs in a specific usage context. Our analysis revealed a number of explanation needs that were missing from the question bank, but that emerged repeatedly as our study participants interacted with an AI system. We also found that some of the XAIQB questions were difficult to distinguish and required interpretation during use. Our contribution is an extension of the XAIQB with 11 new questions. In addition, we have expanded the descriptions of all new and existing questions to facilitate their use.

cs.HC↗

arXiv2023

"Am I Private and If So, how Many?" -- Using Risk Communication Formats for Making Differential Privacy Understandable

Mobility data is essential for cities and communities to identify areas for necessary improvement. Data collected by mobility providers already contains all the information necessary, but privacy of the individuals needs to be preserved. Differential privacy (DP) defines a mathematical property which guarantees that certain limits of privacy are preserved while sharing such data, but its functionality and privacy protection are difficult to explain to laypeople. In this paper, we adapt risk communication formats in conjunction with a model for the privacy risks of DP. The result are privacy notifications which explain the risk to an individual's privacy when using DP, rather than DP's functionality. We evaluate these novel privacy communication formats in a crowdsourced study. We find that they perform similarly to the best performing DP communications used currently in terms of objective understanding, but did not make our participants as confident in their understanding. We also discovered an influence, similar to the Dunning-Kruger effect, of the statistical numeracy on the effectiveness of some of our privacy communication formats and the DP communication format used currently. These results generate hypotheses in multiple directions, for example, toward the use of risk visualization to improve the understandability of our formats or toward adaptive user interfaces which tailor the risk communication to the characteristics of the reader.

cs.HC↗

arXiv2023

Critical-Reflective Human-AI Collaboration: Exploring Computational Tools for Art Historical Image Retrieval

Just as other disciplines, the humanities explore how computational research approaches and tools can meaningfully contribute to scholarly knowledge production. We approach the design of computational tools through the analytical lens of 'human-AI collaboration.' However, there is no generalizable concept of what constitutes 'meaningful' human-AI collaboration. In terms of genuinely human competencies, we consider criticality and reflection as guiding principles of scholarly knowledge production. Although (designing for) reflection is a recurring topic in CSCW and HCI discourses, it has not been centered in work on human-AI collaboration. We posit that integrating both concepts is a viable approach to supporting 'meaningful' human-AI collaboration in the humanities. Our research, thus, is guided by the question of how critical reflection can be enabled in human-AI collaboration. We address this question with a use case that centers on computer vision (CV) tools for art historical image retrieval. Specifically, we conducted a qualitative interview study with art historians and extended the interviews with a think-aloud software exploration. We observed and recorded our participants' interaction with a ready-to-use CV tool in a possible research scenario. We found that critical reflection, indeed, constitutes a core prerequisite for 'meaningful' human-AI collaboration in humanities research contexts. However, we observed that critical reflection was not fully realized during interaction with the CV tool. We interpret this divergence as supporting our hypothesis that computational tools need to be intentionally designed in such a way that they actively scaffold and support critical reflection during interaction. Based on our findings, we suggest four empirically grounded design implications for 'critical-reflective human-AI collaboration'.

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arXiv2022

"Am I Private and If So, how Many?" - Communicating Privacy Guarantees of Differential Privacy with Risk Communication Formats

Decisions about sharing personal information are not trivial, since there are many legitimate and important purposes for such data collection, but often the collected data can reveal sensitive information about individuals. Privacy-preserving technologies, such as differential privacy (DP), can be employed to protect the privacy of individuals and, furthermore, provide mathematically sound guarantees on the maximum privacy risk. However, they can only support informed privacy decisions, if individuals understand the provided privacy guarantees. This article proposes a novel approach for communicating privacy guarantees to support individuals in their privacy decisions when sharing data. For this, we adopt risk communication formats from the medical domain in conjunction with a model for privacy guarantees of DP to create quantitative privacy risk notifications. We conducted a crowd-sourced study with 343 participants to evaluate how well our notifications conveyed the privacy risk information and how confident participants were about their own understanding of the privacy risk. Our findings suggest that these new notifications can communicate the objective information similarly well to currently used qualitative notifications, but left individuals less confident in their understanding. We also discovered that several of our notifications and the currently used qualitative notification disadvantage individuals with low numeracy: these individuals appear overconfident compared to their actual understanding of the associated privacy risks and are, therefore, less likely to seek the needed additional information before an informed decision. The promising results allow for multiple directions in future research, for example, adding visual aids or tailoring privacy risk communication to characteristics of the individuals.

cs.HC↗

arXiv2022

Unfolding Values through Systematic Guidance: Conducting a Value-Centered Participatory Workshop for a Patient-Oriented Data Donation

Routinely collected clinical patient data posits a valuable resource for data-driven medical innovation. Such secondary data use for medical research purposes is dependent on the patient's consent. To gain an understanding of the patients' values and needs regarding medical data donations, we developed a participatory workshop method, integrating approaches from value-sensitive and reflective design to explore patients' values and translate them into hypothetical, ideal design solutions. The data gathered in the workshop are used to derive practicable design requirements for patient-oriented data donation technologies. In this paper, we introduce the workshop process and evaluate its application.

cs.HC↗

arXiv2022

Coding IxD: Enabling Interdisciplinary Education by Sparking Reflection

Educating students from diverse disciplinary backgrounds is challenging. In this article, we report on our interdisciplinary course coding interaction and design (Coding IxD), which is designed for computer science and design students alike. This course has been developed over several years by consciously deliberating on existing hurdles within the educational concept. First, we motivate the need for Coding IxD and introduce the teaching principles that helped shape the course's general structure. Our teaching principles materialize in four method-based phases derived from research through design. Each phase consists of several methods that emerged to be suitable in an interdisciplinary context. Then, based on two selected student projects, we exemplify how interdisciplinary teams can arrive at novel interactive prototypes. We conclude by reflecting on our teaching practice as essential for a meaningful learning experience.

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arXiv2021

Explanation Strategies as an Empirical-Analytical Lens for Socio-Technical Contextualization of Machine Learning Interpretability

During a research project in which we developed a machine learning (ML) driven visualization system for non-ML experts, we reflected on interpretability research in ML, computer-supported collaborative work and human-computer interaction. We found that while there are manifold technical approaches, these often focus on ML experts and are evaluated in decontextualized empirical studies. We hypothesized that participatory design research may support the understanding of stakeholders' situated sense-making in our project, yet, found guidance regarding ML interpretability inexhaustive. Building on philosophy of technology, we formulated explanation strategies as an empirical-analytical lens explicating how technical explanations mediate the contextual preferences concerning people's interpretations. In this paper, we contribute a report of our proof-of-concept use of explanation strategies to analyze a co-design workshop with non-ML experts, methodological implications for participatory design research, design implications for explanations for non-ML experts and suggest further investigation of technological mediation theories in the ML interpretability space.

cs.HC↗

arXiv2021

Investigating Modes of Activity and Guidance for Mediating Museum Exhibits in Mixed Reality

We present an exploratory case study describing the design and realisation of a ''pure mixed reality'' application in a museum setting, where we investigate the potential of using Microsoft's HoloLens for object-centred museum mediation. Our prototype supports non-expert visitors observing a sculpture by offering interpretation that is linked to visual properties of the museum object. The design and development of our research prototype is based on a two-stage visitor observation study and a formative study we conducted prior to the design of the application. We present a summary of our findings from these studies and explain how they have influenced our user-centred content creation and the interaction design of our prototype. We are specifically interested in investigating to what extent different constructs of initiative influence the learning and user experience. Thus, we detail three modes of activity that we realised in our prototype. Our case study is informed by research in the area of human-computer interaction, the humanities and museum practice. Accordingly, we discuss core concepts, such as gaze-based interaction, object-centred learning, presence, and modes of activity and guidance with a transdisciplinary perspective.

cs.HC↗

arXiv2021

Situated Case Studies for a Human-Centered Design of Explanation User Interfaces

Researchers and practitioners increasingly consider a human-centered perspective in the design of machine learning-based applications, especially in the context of Explainable Artificial Intelligence (XAI). However, clear methodological guidance in this context is still missing because each new situation seems to require a new setup, which also creates different methodological challenges. Existing case study collections in XAI inspired us; therefore, we propose a similar collection of case studies for human-centered XAI that can provide methodological guidance or inspiration for others. We want to showcase our idea in this workshop by describing three case studies from our research. These case studies are selected to highlight how apparently small differences require a different set of methods and considerations. With this workshop contribution, we would like to engage in a discussion on how such a collection of case studies can provide a methodological guidance and critical reflection.

cs.HC↗

arXiv2020

Examining the Impact of Algorithm Awareness on Wikidata's Recommender System Recoin

The global infrastructure of the Web, designed as an open and transparent system, has a significant impact on our society. However, algorithmic systems of corporate entities that neglect those principles increasingly populated the Web. Typical representatives of these algorithmic systems are recommender systems that influence our society both on a scale of global politics and during mundane shopping decisions. Recently, such recommender systems have come under critique for how they may strengthen existing or even generate new kinds of biases. To this end, designers and engineers are increasingly urged to make the functioning and purpose of recommender systems more transparent. Our research relates to the discourse of algorithm awareness, that reconsiders the role of algorithm visibility in interface design. We conducted online experiments with 105 participants using MTurk for the recommender system Recoin, a gadget for Wikidata. In these experiments, we presented users with one of a set of three different designs of Recoin's user interface, each of them exhibiting a varying degree of explainability and interactivity. Our findings include a positive correlation between comprehension of and trust in an algorithmic system in our interactive redesign. However, our results are not conclusive yet, and suggest that the measures of comprehension, fairness, accuracy and trust are not yet exhaustive for the empirical study of algorithm awareness. Our qualitative insights provide a first indication for further measures. Our study participants, for example, were less concerned with the details of understanding an algorithmic calculation than with who or what is judging the result of the algorithm.

cs.HC↗

arXiv2020

Seeker or Avoider? User Modeling for Inspiration Deployment in Large-Scale Ideation

People react differently to inspirations shown to them during brainstorming. Existing research on large-scale ideation systems has investigated this phenomenon through aspects of timing, inspiration similarity and inspiration integration. However, these approaches do not address people's individual preferences. In the research presented, we aim to address this lack with regards to inspirations. In a first step, we conducted a co-located brainstorming study with 15 participants, which allowed us to differentiate two types of ideators: Inspiration seekers and inspiration avoiders. These insights informed the study design of the second step, where we propose a user model for classifying people depending on their ideator types, which was translated into a rule-based and a random forest-based classifier. We evaluated the validity of our user model by conducting an online experiment with 380 participants. The results confirmed our proposed ideator types, showing that, while seekers benefit from the availability of inspiration, avoiders were influenced negatively. The random forest classifier enabled us to differentiate people with a 73 \% accuracy after only three minutes of ideation. These insights show that the proposed ideator types are a promising user model for large-scale ideation. In future work, this distinction may help to design more personalized large-scale ideation systems that recommend inspirations adaptively.

cs.HC↗

arXiv2019

Wikidata from a Research Perspective -- A Systematic Mapping Study of Wikidata

Wikidata is one of the most edited knowledge bases which contains structured data. It serves as the data source for many projects in the Wikimedia sphere and beyond. Since its inception in October 2012, it has been increasingly growing in term of both its community and its content. This growth is reflected by an expanding number of research focusing on Wikidata. Our study aims to provide a general overview of the research performed on Wikidata through a systematic mapping study in order to identify the current topical coverage of existing research as well as the white spots which need further investigation. In this study, 67 peer-reviewed research from journals and conference proceedings were selected, and classified into meaningful categories. We describe this data set descriptively by showing the publication frequency, the publication venue and the origin of the authors and reveal current research focuses. These especially include aspects concerning data quality, including questions related to language coverage and data integrity. These results indicate a number of future research directions, such as, multilingualism and overcoming language gaps, the impact of plurality on the quality of Wikidata's data, Wikidata's potential in various disciplines, and usability of user interface.

cs.DL↗

arXiv2019

Discovering the Sweet Spot of Human-Computer Configurations: A Case Study in Information Extraction

Interactive intelligent systems, i.e., interactive systems that employ AI technologies, are currently present in many parts of our social, public and political life. An issue reoccurring often in the development of these systems is the question regarding the level of appropriate human and computer contributions. Engineers and designers lack a way of systematically defining and delimiting possible options for designing such systems in terms of levels of automation. In this paper, we propose, apply and reflect on a method for human-computer configuration design. It supports the systematic investigation of the design space for developing an interactive intelligent system. We illustrate our method with a use case in the context of collaborative ideation. Here, we developed a tool for information extraction from idea content. A challenge was to find the right level of algorithmic support, whereby the quality of the information extraction should be as high as possible, but, at the same time, the human effort should be low. Such contradicting goals are often an issue in system development; thus, our method proposed helped us to conceptualize and explore the design space. Based on a critical reflection on our method application, we want to offer a complementary perspective to the value-centered design of interactive intelligent systems. Our overarching goal is to contribute to the design of so-called hybrid systems where humans and computers are partners.

cs.HC↗

arXiv2019

PreCall: A Visual Interface for Threshold Optimization in ML Model Selection

Machine learning systems are ubiquitous in various kinds of digital applications and have a huge impact on our everyday life. But a lack of explainability and interpretability of such systems hinders meaningful participation by people, especially by those without a technical background. Interactive visual interfaces (e.g., providing means for manipulating parameters in the user interface) can help tackle this challenge. In this paper we present PreCall, an interactive visual interface for ORES, a machine learning-based web service for Wikimedia projects such as Wikipedia. While ORES can be used for a number of settings, it can be challenging to translate requirements from the application domain into formal parameter sets needed to configure the ORES models. Assisting Wikipedia editors in finding damaging edits, for example, can be realized at various stages of automatization, which might impact the precision of the applied model. Our prototype PreCall attempts to close this translation gap by interactively visualizing the relationship between major model metrics (recall, precision, false positive rate) and a parameter (the threshold between valuable and damaging edits). Furthermore, PreCall visualizes the probable results for the current model configuration to improve the human's understanding of the relationship between metrics and outcome when using ORES. We describe PreCall's components and present a use case that highlights the benefits of our approach. Finally, we pose further research questions we would like to discuss during the workshop.

cs.LG↗