arXiv subjects

Martina Ziefle

Publications and source records attributed to Martina Ziefle.

11 recordsLinked to original sources

arXiv2026

From Survey Personas to LLM Agents: A Generative Agent-based Simulation of Mobility Policy Preference Dynamics

Large language models (LLMs) have been increasingly used to simulate socially complex and interaction-driven tasks. However, most existing studies rely on hand-crafted personas. Since persona design strongly shapes how agents interpret context and make decisions, developing empirically grounded agent profiles is a significant aspect in this underexplored research area. To address this limitation, we propose a survey-grounded generative agent-based modeling (GABM) simulation framework that translates real survey respondents into generative LLM agents. The main objective of our framework is to demonstrate how careful persona design enables realistic simulation of decision-making using LLMs for facilitating behavioral experiments. We illustrate the framework's applicability through a case study of mobility policy preference dynamics in Germany, focusing on public support for phasing out new internal combustion engine vehicles, which is part of the European Union's net-zero target. Our benchmark is based on 514 survey respondents, each translated into a natural-language persona, grounded in demographic characteristics, political orientation, mobility behavior, fuel experience, and climate-related attitudes. The simulation goal is to examine how support evolves over time, how agents switch positions across rounds, and how responses differ across survey-grounded personas under changing social and policy contexts.

cs.CY↗

arXiv2026

Empirical assessment of ChatGPT's answering capabilities in natural science and engineering

ChatGPT is a powerful language model from OpenAI that is arguably able to comprehend and generate text. ChatGPT is expected to greatly impact society, research, and education. An essential step to understand ChatGPT's expected impact is to study its domain-specific answering capabilities. Here, we perform a systematic empirical assessment of its abilities to answer questions across the natural science and engineering domains. We collected 594 questions on natural science and engineering topics from 198 faculty members across five faculties at Delft University of Technology. After collecting the answers from ChatGPT, the participants assessed the quality of the answers using a systematic scheme. Our results show that the answers from ChatGPT are, on average, perceived as ''mostly correct''. Two major trends are that the rating of the ChatGPT answers significantly decreases (i) as the educational level of the question increases and (ii) as we evaluate skills beyond scientific knowledge, e.g., critical attitude.

cs.HC↗

arXiv2026

Human Autonomy and Sense of Agency in Human-Robot Interaction: A Systematic Literature Review

Human autonomy and sense of agency are increasingly recognised as critical for user well-being, motivation, and the ethical deployment of robots in human-robot interaction (HRI). Given the rapid development of artificial intelligence, robot capabilities and their potential to function as colleagues and companions are growing. This systematic literature review synthesises 22 empirical studies selected from an initial pool of 728 articles published between 2011 and 2024. Articles were retrieved from major scientific databases and identified based on empirical focus and conceptual relevance, namely, how to preserve and promote human autonomy and sense of agency in HRI. Derived through thematic synthesis, five clusters of potentially influential factors are revealed: robot adaptiveness, communication style, anthropomorphism, presence of a robot and individual differences. Measured through psychometric scales or the intentional binding paradigm, perceptions of autonomy and agency varied across industrial, educational, healthcare, care, and hospitality settings. The review underscores the theoretical differences between both concepts, but their yet entangled use in HRI. Despite increasing interest, the current body of empirical evidence remains limited and fragmented, underscoring the necessity for standardised definitions, more robust operationalisations, and further exploratory and qualitative research. By identifying existing gaps and highlighting emerging trends, this review contributes to the development of human-centered, autonomy-supportive robot design strategies that uphold ethical and psychological principles, ultimately supporting well-being in human-robot interaction.

cs.HC↗

arXiv2026

Media Framing Moderates Risk-Benefit Perceptions and Value Tradeoffs in Human-Robot Collaboration

Public acceptance of industrial human-robot collaboration (HRC) is shaped by how risks and benefits are perceived by affected employees. Positive or negative media framing may shape and shift how individuals evaluate HRC. This study examines how message framing moderates the effects of perceived risks and perceived benefits on overall attributed value. In a pre-registered study, participants (N = 1150) were randomly assigned to read either a positively or negatively framed newspaper article in one of three industrial contexts (autonomy, employment, safety) about HRC in production. Subsequently, perceived risks, benefits, and value were measured using reliable and publicly available psychometric scales. Two multiple regressions (one per framing condition) tested for main and interaction effects. Framing influenced absolute evaluations of risk, benefits, and value. In both frames, risks and benefits significantly predicted attributed value. Under positive framing, only main effects were observed (risks: beta = -0.52; benefits: beta = 0.45). Under negative framing, both predictors had stronger main effects (risks: beta = -0.69; benefits: beta = 0.63) along with a significant negative interaction (beta = -0.32), indicating that higher perceived risk diminishes the positive effect of perceived benefits. Model fit was higher for the positive frame (R^2 = 0.715) than for the negative frame (R^2 = 0.583), indicating greater explained variance in value attributions. Framing shapes the absolute evaluation of HRC and how risks and benefits are cognitively integrated in trade-offs. Negative framing produces stronger but interdependent effects, whereas positive framing supports additive evaluations. These findings highlight the role of strategic communication in fostering acceptance of HRC and underscore the need to consider framing in future HRC research.

cs.HC↗

arXiv2025

Trust and Human Autonomy after Cobot Failures: Communication is Key for Industry 5.0

Collaborative robots (cobots) are a core technology of Industry 4.0. Industry 4.0 uses cyber-physical systems, IoT and smart automation to improve efficiency and data-driven decision-making. Cobots, as cyber-physical systems, enable the introduction of lightweight automation to smaller companies through their flexibility, low cost and ability to work alongside humans, while keeping humans and their skills in the loop. Industry 5.0, the evolution of Industry 4.0, places the worker at the centre of its principles: The physical and mental well-being of the worker is the main goal of new technology design, not just productivity, efficiency and safety standards. Within this concept, human trust in cobots and human autonomy are important. While trust is essential for effective and smooth interaction, the workers' perception of autonomy is key to intrinsic motivation and overall well-being. As failures are an inevitable part of technological systems, this study aims to answer the question of how system failures affect trust in cobots as well as human autonomy, and how they can be recovered afterwards. Therefore, a VR experiment (n = 39) was set up to investigate the influence of a cobot failure and its severity on human autonomy and trust in the cobot. Furthermore, the influence of transparent communication about the failure and next steps was investigated. The results show that both trust and autonomy suffer after cobot failures, with the severity of the failure having a stronger negative impact on trust, but not on autonomy. Both trust and autonomy can be partially restored by transparent communication.

cs.HC↗

arXiv2025

Perception Gaps in Risk, Benefit, and Value Between Experts and Public Challenge Socially Accepted AI

Artificial Intelligence (AI) is reshaping many societal domains, raising critical questions about its risks, benefits, and the potential misalignment between public and academic perspectives. This study examines how the general public (N=1110) -- individuals who interact with or are impacted by AI technologies -- and academic AI experts (N=119) -- those elites shaping AI development -- perceive AI's capabilities and impact across 71 scenarios. These scenarios span domains such as sustainability, healthcare, job performance, societal inequality, art, and warfare. Participants evaluated these scenarios across four dimensions using the psychometric model: likelihood, perceived risk and benefit, and overall value (or sentiment). The results suggest significant differences: experts consistently anticipate higher probabilities, perceive lower risks, report greater benefits, and express more positive sentiment toward AI compared to the non-experts. Moreover, both groups apply different weighting schemes: experts discount risk more heavily relative to benefit than non-experts. Visual mappings of these evaluations uncover areas convergent evaluations (e.g., AI performing medical diagnoses or criminal use) as well as tension points (e.g., decision of legal cases, political decision making), highlighting areas where communication and policy interventions may be needed. These findings underscore a critical translational challenge: if AI research and deployment are to align with societal priorities, the perception gap between developers and the public must be better understood and addressed. Our results provide an empirical foundation for value-sensitive AI governance and trust-building strategies across stakeholder groups.

cs.CY↗

arXiv2025

Cultural Dimensions of AI Perception: Charting Expectations, Risks, Benefits, Tradeoffs, and Value in Germany and China

As artificial intelligence (AI) continues to advance, understanding public perceptions -- including biases, risks, and benefits -- is essential for guiding research priorities and AI alignment, shaping public discourse, and informing policy. This exploratory study investigates cultural differences in mental models of AI using 71 imaginaries of AI's potential futures. Drawing on cross-cultural convenience samples from Germany (N=52) and China (N=60), we identify significant differences in expectations, evaluations, and risk-benefit tradeoffs. Participants from Germany generally provided more cautious assessments, whereas participants from China expressed greater optimism regarding AI's societal benefits. Chinese participants exhibited relatively balanced risk-benefit tradeoffs ($β=-0.463$ for risk and $β=+0.484$ for benefit, $r^2=.630$). In contrast, German participants placed greater emphasis on AI's benefits and comparatively less on risks ($β=-0.337$ for risk and $β=+0.715$ for benefit, $r^2=.839$). Visual cognitive maps illustrate these contrasts, offering new perspectives on how cultural contexts shape AI acceptance. Our findings highlight key factors influencing public perception and provide insights for aligning AI with societal values and promoting equitable and culturally sensitive integration of AI technologies.

cs.CY↗

arXiv2024

Demonstrating Data-to-Knowledge Pipelines for Connecting Production Sites in the World Wide Lab

The digital transformation of production requires new methods of data integration and storage, as well as decision making and support systems that work vertically and horizontally throughout the development, production, and use cycle. In this paper, we propose Data-to-Knowledge (and Knowledge-to-Data) pipelines for production as a universal concept building on a network of Digital Shadows (a concept augmenting Digital Twins). We show a proof of concept that builds on and bridges existing infrastructure to 1) capture and semantically annotates trajectory data from multiple similar but independent robots in different organisations and use cases in a data lakehouse and 2) an independent process that dynamically queries matching data for training an inverse dynamic foundation model for robotic control. The article discusses the challenges and benefits of this approach and how Data-to-Knowledge pipelines contribute efficiency gains and industrial scalability in a World Wide Lab as a research outlook.

cs.RO↗

arXiv2024

Mapping Public Perception of Artificial Intelligence: Expectations, Risk-Benefit Tradeoffs, and Value As Determinants for Societal Acceptance

Understanding public perception of artificial intelligence (AI) and the tradeoffs between potential risks and benefits is crucial, as these perceptions might shape policy decisions, influence innovation trajectories for successful market strategies, and determine individual and societal acceptance of AI technologies. Using a representative sample of 1100 participants from Germany, this study examines mental models of AI. Participants quantitatively evaluated 71 statements about AI's future capabilities (e.g., autonomous driving, medical care, art, politics, warfare, and societal divides), assessing the expected likelihood of occurrence, perceived risks, benefits, and overall value. We present rankings of these projections alongside visual mappings illustrating public risk-benefit tradeoffs. While many scenarios were deemed likely, participants often associated them with high risks, limited benefits, and low overall value. Across all scenarios, 96.4% ($r^2=96.4\%$) of the variance in value assessment can be explained by perceived risks ($β=-.504$) and perceived benefits ($β=+.710$), with no significant relation to expected likelihood. Demographics and personality traits influenced perceptions of risks, benefits, and overall evaluations, underscoring the importance of increasing AI literacy and tailoring public information to diverse user needs. These findings provide actionable insights for researchers, developers, and policymakers by highlighting critical public concerns and individual factors essential to align AI development with individual values.

cs.CY↗

arXiv2019

Putting Privacy into Perspective -- Comparing Technical, Legal, and Users' View of Data Sensitivity

Web 2.0, social media, cloud computing, and IoT easily connect people around the globe, overcoming time and space barriers, and offering manifold benefits. However, the technological advances and increased user participation generate novel challenges for protecting users' privacy. From the user perspective, data disclosure depends, in part, on the perceived sensitivity of that data, and thus on a risk assessment of data disclosure. But in light of the new technological opportunities to process and combine data, it is questionable whether users are able to adequately evaluate the risks of data disclosures. As mediating authority, data protection laws try to protect user data, granting enhanced protection to 'special categories' of data. In this publication, the legal, technological, and user perspectives on data sensitivity are presented and compared. From a technological perspective, all data can be referred to as 'potentially sensitive.' The legal and user perspective on data sensitivity deviate as some data types are granted special protection by the law but are not perceived as very sensitive by the users, and vice versa. Merging the three perspectives, the implications for informational self-determination are discussed.

cs.CY↗

arXiv2018

The Users' Perspective on the Privacy-Utility Trade-offs in Health Recommender Systems

Privacy is a major good for users of personalized services such as recommender systems. When applied to the field of health informatics, privacy concerns of users may be amplified, but the possible utility of such services is also high. Despite availability of technologies such as k-anonymity, differential privacy, privacy-aware recommendation, and personalized privacy trade-offs, little research has been conducted on the users' willingness to share health data for usage in such systems. In two conjoint-decision studies (sample size n=521), we investigate importance and utility of privacy-preserving techniques related to sharing of personal health data for k-anonymity and differential privacy. Users were asked to pick a preferred sharing scenario depending on the recipient of the data, the benefit of sharing data, the type of data, and the parameterized privacy. Users disagreed with sharing data for commercial purposes regarding mental illnesses and with high de-anonymization risks but showed little concern when data is used for scientific purposes and is related to physical illnesses. Suggestions for health recommender system development are derived from the findings.

cs.CY↗