arXiv subjects

Christian Meske

Publications and source records attributed to Christian Meske.

17 recordsLinked to original sources

arXiv2026

Thinking Twice: A Sequential Approach to Nudge Towards Reflective Judgment in GenAI-Assisted Decision Making

Humans increasingly use generative AI (GenAI) for decision-making assistance, yet tend to engage in such situations using quick, intuitive (System 1) rather than deliberate, analytical assessment (System 2) as reflected in Dual Process Theory. Such engagement reduces the effectiveness of human-GenAI interactions and increases associated risks. Our research investigates how to design human-GenAI interactions that promote the transition from System 1 to System 2 thinking in decision-making. Based on the Three-Stage-Model, which suggests humans switch to System 2 when conflicts are detected, we conducted an experiment (n=130) testing sequential human-GenAI interaction as a cognitive forcing nudge during problem-solving tasks. This approach, where humans decide first before receiving AI assistance, enables detection of conflicting decisions. Compared to non-sequential settings (concurrent or no AI assistance), results show the sequential condition significantly enhanced both decision performance and GenAI prompt utilization, providing valuable insights for designing more reflective and thus effective human-GenAI interactions.

cs.HC↗

arXiv2026

Leveraging generative AI for human understanding: meta-requirements and design principles for Explanatory AI as an emerging paradigm

Artificial intelligence (AI) systems increasingly support decision-making across critical domains, yet explainable AI (XAI) approaches prioritise algorithmic transparency over human comprehension. While XAI reveals computational processes for model validation, end users require explanations integrating domain knowledge, contextual reasoning, and professional frameworks. This disconnect reveals a design challenge: existing approaches fail to address how practitioners need to understand and act upon AI recommendations. This paper introduces Explanatory AI as an emerging complementary paradigm where AI systems leverage generative and multimodal capabilities to serve as explanatory partners for human understanding. Unlike traditional XAI, which answers 'How did the algorithm decide?' for validation purposes, Explanatory AI addresses 'Why does this make sense?' for practitioners making informed decisions. Through theory-informed design, we synthesise multidisciplinary perspectives from cognitive science, communication research, and education with empirical evidence from healthcare and AI expert interviews. Our analysis identifies five dimensions distinguishing Explanatory AI from XAI: explanatory purpose, communication mode, epistemic stance, adaptivity, and cognitive design. We derive five meta-requirements and formulate ten design principles prescribing how to build such systems, illustrated through five UI mockups exemplifying implementation. Explanatory AI operates standalone or combined with XAI, serving distinct purposes in creating systems that genuinely support human reasoning and decision-making.

cs.AI↗

arXiv2026

The GenAI Who Knew Too Little: Revisiting Transactive Memory Systems in Human GenAI Collaboration

Generative artificial intelligence (GenAI) is rapidly transforming knowledge work, creating new collaborative dynamics that challenge tradi-tional human-human interaction patterns. This study investigates how Transactive Memory Systems (TMS) manifest in human-GenAI collabora-tion. Through qualitative interpretive interviews with 14 knowledge work-ers and thematic analysis, we identified distinct TMS manifestations across its three main dimensions: (1) regarding specialization, expertise recogni-tion exhibited asymmetry. Humans developed awareness of GenAI capabili-ties while GenAI demonstrated only temporary recognition of human exper-tise; (2) in terms of credibility, humans formed ambivalent trust relation-ships with GenAI, simultaneously attributing expertise to the latter while implementing additional verification processes; and (3) concerning coor-dination, where humans primarily directed more passive GenAI systems lacking initiative, resulting in hierarchical interaction patterns and unidi-rectional information flow. Our findings reveal that TMS manifests differ-ently in human-GenAI compared to human-human collaboration, necessitat-ing theoretical adaptations including trust markers, acknowledging GenAI's absent metamemory, and identifying its temporary memory.

cs.HC↗

arXiv2026

Governance of Generative Artificial Intelligence for Companies

Generative Artificial Intelligence (GenAI) like ChatGPT has swiftly entered organizations without adequate governance, posing both opportunities and risks. Limited research addresses organizational governance from both technical and business perspectives. This gap is particularly relevant for international businesses, where differences in regulation, language, and business environments complicate governance. While multiple frameworks for AI governance exist, this needed diversity is lacking for GenAI. This review paper fills this gap by surveying recent literature to better understand the fundamental characteristics of GenAI and to adapt existing governance frameworks specifically to GenAI. The resulting framework delineates scope, objectives, and governance mechanisms designed to both harness business opportunities and mitigate risks associated with GenAI integration. We theorize a distinctive property of GenAI governance: its scope is endogenous to use. Unlike conventional organizational AI, for which governance is organized around a fixed artifact (e.g., model, intended use), GenAI allows users to reconfigure the artifact (e.g., its behavior and risk). Consequently, the object of governance is not fixed ex ante but is partly constituted through use.

cs.AI↗

arXiv2026

Wisdom Of The (AI) Crowd: Investigating Artificial Swarm Intelligence In Large Language Models

Human swarm intelligence demonstrates remarkable collective accuracy but faces scalability constraints in cost, coordination, and time. We investigate whether large language models (LLMs) can approximate swarm intelligence effects through artificial swarms, addressing a critical gap in understanding AI-based aggregation mechanisms. We conducted a controlled experiment with 960 manually executed prompts across three proprietary models (GPT-5, Gemini 2.5 Pro, Claude Sonnet 4.5), testing intra-model sampling and inter-model aggregation on eight estimation tasks. Results reveal consistent error reduction through intra- and inter-model aggregation, with significant error reductions up to 37 percentage points in MAPE across different aggregation strategies. We observed small to large effect sizes for positive correlations (Spearman's $ρ=0.242-0.568$, all $p<0.001$) between relative confidence interval widths and relative estimation errors, suggesting LLMs possess metacognitive awareness when assessing uncertainty. We discuss implications for research and practice, providing actionable insights for deploying LLM swarms in organizational decision-making.

cs.AI↗

arXiv2026

When Shortest Isn't Safest: A Design Science Approach to Senior-Friendly Pedestrian Routing

Older adults' independent mobility enables out-of-home participation, well-being and health, yet pedestrian navigation systems still optimize primarily for distance or time, often overlooking barriers, safety thresholds, and supportive infrastructure that shape late-life walking decisions. We present a senior-friendly pedestrian routing artefact developed through echeloned Design Science Re-search, translating lived mobility constraints into prescriptive design knowledge. Based on 11 semi-structured interviews, we derive initial Design Requirements (DRs) and Design Principles (DPs) for barrier-aware, amenity-sensitive routing and execution-relevant explanations. We instantiate these in an OpenStreetMap pedestrian network enriched with amenities (benches, toilets, and shelters) and height data, and implemented an A*-based routing engine with configurable costs and explanation payloads. In a field-based walking study, 14 older adults com-pared artefact-generated routes with baselines and provided ratings and qualitative feedback; the senior-friendly route was preferred overall. Thematic analysis further showed that infrastructure maintenance, seasonal conditions, traffic exposure, and social context shape route acceptance. We synthesize these insights into refined DRs and DPs emphasizing context-aware hazard modeling, multi-route transparency, landmark-grounded explanations, social-context sensitivity, and stage-appropriate information. Our contributions provide actionable guidance for practitioners developing senior-friendly pedestrian navigation systems.

cs.AI↗

arXiv2026

Vibe Coding as a Reconfiguration of Intent Mediation in Software Development: Definition, Implications, and Research Agenda

Software development is undergoing a fundamental transformation as vibe coding becomes widespread, with large portions of contemporary codebases now being generated by Artificial Intelligence (AI). The disconnect between rapid adoption and limited conceptual understanding highlights the need for an inquiry into this emerging paradigm. Drawing on an intent perspective and historical analysis, we define vibe coding as a software development paradigm where humans and Generative AI (GenAI) engage in collaborative flow to co-create software artifacts through natural language dialogue, shifting the mediation of developer intent from deterministic instruction to probabilistic inference. By intent mediation, we refer to the fundamental process through which developers translate their conceptual goals into representations that computational systems can execute. Our results show that vibe coding redistributes epistemic labor between humans and machines, shifting expertise from technical implementation toward collaborative orchestration. We identify key opportunities, including democratization, acceleration, and systemic leverage, alongside risks such as black-box codebases, responsibility gaps, and ecosystem bias. We conclude with a research agenda spanning human-, technology-, and organization-centered directions to guide future investigations of this paradigm.

cs.SE↗

arXiv2025

Artificial Intelligence Governance for Businesses

Artificial Intelligence (AI) governance regulates the exercise of authority and control over the management of AI. It aims at leveraging AI through effective use of data and minimization of AI-related cost and risk. While topics such as AI governance and AI ethics are thoroughly discussed on a theoretical, philosophical, societal and regulatory level, there is limited work on AI governance targeted to companies and corporations. This work views AI products as systems, where key functionality is delivered by machine learning (ML) models leveraging (training) data. We derive a conceptual framework by synthesizing literature on AI and related fields such as ML. Our framework decomposes AI governance into governance of data, (ML) models and (AI) systems along four dimensions. It relates to existing IT and data governance frameworks and practices. It can be adopted by practitioners and academics alike. For practitioners the synthesis of mainly research papers, but also practitioner publications and publications of regulatory bodies provides a valuable starting point to implement AI governance, while for academics the paper highlights a number of areas of AI governance that deserve more attention.

cs.AI↗

arXiv2024

Investigating the Role of Explainability and AI Literacy in User Compliance

AI is becoming increasingly common across different domains. However, as sophisticated AI-based systems are often black-boxed, rendering the decision-making logic opaque, users find it challenging to comply with their recommendations. Although researchers are investigating Explainable AI (XAI) to increase the transparency of the underlying machine learning models, it is unclear what types of explanations are effective and what other factors increase compliance. To better understand the interplay of these factors, we conducted an experiment with 562 participants who were presented with the recommendations of an AI and two different types of XAI. We find that users' compliance increases with the introduction of XAI but is also affected by AI literacy. We also find that the relationships between AI literacy XAI and users' compliance are mediated by the users' mental model of AI. Our study has several implications for successfully designing AI-based systems utilizing XAI.

cs.AI↗

arXiv2021

Deceptive AI Explanations: Creation and Detection

Artificial intelligence (AI) comes with great opportunities but can also pose significant risks. Automatically generated explanations for decisions can increase transparency and foster trust, especially for systems based on automated predictions by AI models. However, given, e.g., economic incentives to create dishonest AI, to what extent can we trust explanations? To address this issue, our work investigates how AI models (i.e., deep learning, and existing instruments to increase transparency regarding AI decisions) can be used to create and detect deceptive explanations. As an empirical evaluation, we focus on text classification and alter the explanations generated by GradCAM, a well-established explanation technique in neural networks. Then, we evaluate the effect of deceptive explanations on users in an experiment with 200 participants. Our findings confirm that deceptive explanations can indeed fool humans. However, one can deploy machine learning (ML) methods to detect seemingly minor deception attempts with accuracy exceeding 80% given sufficient domain knowledge. Without domain knowledge, one can still infer inconsistencies in the explanations in an unsupervised manner, given basic knowledge of the predictive model under scrutiny.

cs.LG↗

arXiv2021

Fake or Credible? Towards Designing Services to Support Users' Credibility Assessment of News Content

Fake news has become omnipresent in digitalized areas such as social media platforms. While being disseminated online, it also poses a threat to individuals and societies offline, for example, in the context of democratic elections. Research and practice have investigated the detection of fake news with behavioral science or method-related perspectives. However, to date, we lack design knowledge on presenting fake news warnings to users to support their individual news credibility assessment. We present the journey through the first design cycle on developing a fake news detection service focusing on the user interface design. The design is grounded in concepts from the field of source credibility theory and instantiated in a prototype that was qualitatively evaluated. The 13 participants communicated their interest in a lightweight application that aids in the news credibility assessment and rated the design features as useful as well as desirable.

cs.HC↗

arXiv2020

Transparency and Trust in Human-AI-Interaction: The Role of Model-Agnostic Explanations in Computer Vision-Based Decision Support

Computer Vision, and hence Artificial Intelligence-based extraction of information from images, has increasingly received attention over the last years, for instance in medical diagnostics. While the algorithms' complexity is a reason for their increased performance, it also leads to the "black box" problem, consequently decreasing trust towards AI. In this regard, "Explainable Artificial Intelligence" (XAI) allows to open that black box and to improve the degree of AI transparency. In this paper, we first discuss the theoretical impact of explainability on trust towards AI, followed by showcasing how the usage of XAI in a health-related setting can look like. More specifically, we show how XAI can be applied to understand why Computer Vision, based on deep learning, did or did not detect a disease (malaria) on image data (thin blood smear slide images). Furthermore, we investigate, how XAI can be used to compare the detection strategy of two different deep learning models often used for Computer Vision: Convolutional Neural Network and Multi-Layer Perceptron. Our empirical results show that i) the AI sometimes used questionable or irrelevant data features of an image to detect malaria (even if correctly predicted), and ii) that there may be significant discrepancies in how different deep learning models explain the same prediction. Our theoretical discussion highlights that XAI can support trust in Computer Vision systems, and AI systems in general, especially through an increased understandability and predictability.

cs.HC↗

arXiv2020

Status Quo, Critical Reflection and Road Ahead of Digital Nudging in Information Systems Research -- A Discussion with Markus Weinmann and Alexey Voinov

Research on Digital Nudging has become increasingly popular in the Information Systems (IS) community. This paper presents an overview of the current progress, a critical reflection and an outlook to further research regarding Digital Nudging in IS. For this purpose, we conducted a comprehensive literature review as well as an interview with Markus Weinmann from Rotterdam School of Management at Erasmus University, one of the first scholars who introduced Digital Nudging to the IS community, and Alexey Voinov, director of the Centre on Persuasive Systems for Wise Adaptive Living at University of Technology Sydney. The findings uncover a gap between what we know about what constitutes Digital Nudging and how consequent requirements can actually be put into practice. In this context, the original concept of Nudging bears inherent challenges, e.g. regarding the focus on the individuals' welfare, which hence also apply to Digital Nudging. Moreover, we need a better understanding of how Nudging in digital choice environments differs from that in the offline world. To further distinguish itself from other disciplines that already tested various nudges in many different domains, Digital Nudging Research in IS may benefit from a strong Design Science perspective, going beyond the test of effectiveness and providing specific design principles for the different types of digital nudges.

cs.HC↗

arXiv2020

Enterprise Social Networks as Digital Infrastructures -- Understanding the Utilitarian Value of Social Media at the Workplace

In this study, we first show that while both the perceived usefulness and perceived enjoyment of enterprise social networks impact employees' intentions for continuous participation, the utilitarian value significantly outpaces its hedonic value. Second, we prove that the network's utilitarian value is constituted by its digital infrastructure characteristics: versatility, adaptability, interconnectedness and invisibility-in-use. The study is set within a software engineering company and bases on quantitative survey research, applying partial least squares structural equation modeling.

cs.SI↗

arXiv2020

Ethical Guidelines for the Construction of Digital Nudges

Under certain circumstances, humans tend to behave in irrational ways, leading to situations in which they make undesirable choices. The concept of digital nudging addresses these limitations of bounded rationality by establishing a libertarian paternalist alternative to nudge users in virtual environments towards their own preferential choices. Thereby, choice architectures are designed to address biases and heuristics involved in cognitive thinking. As research on digital nudging has become increasingly popular in the Information Systems community, an increasing necessity for ethical guidelines has emerged around this concept to safeguard its legitimization in distinction to e.g. persuasion or manipulation. However, reflecting on ethical debates regarding digital nudging in academia, we find that current conceptualizations are scare. This is where on the basis of existing literature, we provide a conceptualization of ethical guidelines for the design of digital nudges, and thereby aim to ensure the applicability of nudging mechanisms in virtual environments.

cs.HC↗

arXiv2020

Do you comply with AI? -- Personalized explanations of learning algorithms and their impact on employees' compliance behavior

Machine Learning algorithms are technological key enablers for artificial intelligence (AI). Due to the inherent complexity, these learning algorithms represent black boxes and are difficult to comprehend, therefore influencing compliance behavior. Hence, compliance with the recommendations of such artifacts, which can impact employees' task performance significantly, is still subject to research - and personalization of AI explanations seems to be a promising concept in this regard. In our work, we hypothesize that, based on varying backgrounds like training, domain knowledge and demographic characteristics, individuals have different understandings and hence mental models about the learning algorithm. Personalization of AI explanations, related to the individuals' mental models, may thus be an instrument to affect compliance and therefore employee task performance. Our preliminary results already indicate the importance of personalized explanations in industry settings and emphasize the importance of this research endeavor.

cs.CY↗

arXiv2016

Gamify Employee Collaboration - A Critical Review of Gamification Elements in Social Software

Social software solutions in enterprises such as IBM Connections are said to have the potential to support communication and collaboration among employees. However, companies are faced to manage the adoption of such collaborative tools and therefore need to raise the employees' acceptance and motivation. To solve these problems, developers started to implement Gamification elements in social software tools, which aim to increase users' motivation. In this paper, we critically examine the status quo of the current market of leading social software solutions and seek to find out which Gamification approaches are implementated in these collaborative tools. Our findings show, that most of the major social collaboration solutions do not offer Gamification features by default, but leave the integration to a various number of third party plug-in vendors. Furthermore we identify a trend in which Gamification solutions majorly focus on rewarding quantitative improvement of work activities, neglecting qualitative performance. Subsequently, current solutions do not match recent findings in research and ignore risks that can lower the employees' motivation and work performance in the long run.

cs.CY↗