Human-AI Collaborations and Ethical Issues
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Item Galactic Companions from Star Wars: C-3PO or R2-D2? Understanding Dual Types of Anthropomorphism in Humanoids(2025-01-07) Kim, Joohee; Im, IlRecent advancements in generative AI and humanoid robotics have facilitated the rapid development of humanoids that think, move, and interact in ways that closely resemble human behavior, significantly contributing to the study of anthropomorphism in human-AI interaction. Despite the extensive research in this area, the dynamic nature of anthropomorphism—how it evolves through cognitive processes and the effects of these changes—remains largely unexplored. Therefore, we propose dual types of anthropomorphism: reflexive anthropomorphism (automatic attribution of humanlike characteristics) and reflective anthropomorphism (conscious attribution of humanlike qualities) based on the dual process theory of higher cognition. A preliminary study was conducted with 120 participants using a scenario survey that included images of humanoids with and without human-like faces, as well as a video of a simulated chat interface between the user and a chatbot. The study result highlights that an AI agent's appearance significantly influences both reflexive and reflective anthropomorphism, with reflective anthropomorphism being particularly important for shaping users' anthropomorphic responses.Item Wherefore Art Thou: Mapping Public Debates about Image-Generative AI(2025-01-07) Banks, Jaime; Li, JianghuiWith the mainstreaming of image-generative AI in 2023, debates over the value, ethics, and legality of these technologies significantly engaged both public and scholarly communities. However, the current empirical examination of these debates on naturalistic data is sparse. This study fills this gap by offering descriptive, longitudinal account of public online debates about AI art. Inductive analysis identified ten themes in these debates, addressing not only law and ethics, but also nuanced (sometimes philosophical) debates about technology, labor, art, and learning. Longitudinal analysis revealed most discussion themes are relatively stable over time. Although most external events don’t generally elevate related discussions, debates about Fair Use and Competing Interests are more responsive to law or policy events, and themes such as Human versus Machine Learning and Tech Philosophies are more reactive to technology development events.Item Building Trust in AI: Exploring the Impact of AI Competence Framing(2025-01-07) Gonzalez, Victoria; Amo, Laura; Das Smith, SanjuktaTrust in AI is crucial for its successful integration into organizations and society. Despite the increasing exposure of employees to AI-driven analyses, their adoption is not guaranteed solely by the presence of AI systems. Trust in AI is multifaceted, influenced by perceptions of its competence and accuracy. To investigate the impact of AI competence on user trust and error tolerance, we conducted an experiment using vignettes with varying AI positioning and accuracy. Our results highlight the importance of AI competence framing in shaping user trust; we found that a strong portrait of AI capabilities boosts trust and planned action. This study fills a gap in the existing literature by exploring the relationship between AI competence framing and trust in a professional context, providing insights for the implementation of AI in the workplace, and emphasizing the significance of building trust in AI for its adoption and effective use.Item The Influence of Artificial Intelligence Autonomy on Physicians Work Outcomes in Healthcare: A Lab-in-the-Field Experiment(2025-01-07) Grüning, Maximilian; Henkenjohann, Richard; Fuchs, Moritz; Dratsch, Thomas; Pinto Dos Santos, Daniel; Kim, Moon; Matthies, Philipp; Mukhopadhyay, Anirban; Trenz, ManuelArtificial intelligence (AI) has emerged as a powerful tool for complex decision-making. However, little is known about how AI-based systems should be integrated into clinical practice to improve patient and provider outcomes. We argue that AI-based systems are no longer passive tools and can assume certain responsibilities for tasks. A novel aspect of our study is the exploration of distribution of autonomy between AI and physicians, which can significantly impact medical care. Therefore, we shed light on how AI autonomy influence physician-AI interactions and work outcomes. We perform a lab-in-the-field experiment on diagnosis and reporting by physicians using explainable AI (XAI) in their workflow. We find that an autonomous XAI has positive effects on objective performance and intention-to-use in diagnosis and reporting. Further, we contribute to information systems (IS) research and practice by highlighting that reduced autonomy of physicians could lead to better work outcomes and improve medical care.Item Assessing Sequential Databases for Spontaneous and Posed Facial Expression Recognition(2025-01-07) Gebele, Jens; Brune, Philipp; Schwab, Frank; Von Mammen, SebastianAdvancements in AI for recognizing facial expressions of emotion rely heavily on the quality of underlying data. We present a comparative analysis of sequential databases for spontaneous (real) and posed (fake) facial expressions, introducing a modular, metric-based framework for evaluating data quality. This framework allows for flexible selection and weighting of metrics, making it adaptable to a wide range of research needs. Applied to 13 databases, it identifies key characteristics of an ideal data set, particularly for AI systems that distinguish between spontaneous and posed facial expressions. Our findings offer practical solutions to optimize data quality, laying a foundation for ensuring high-quality data in future emotion recognition research.Item Introduction to the Minitrack on Human-AI Collaborations and Ethical Issues(2025-01-07) Chen, Xunyu; Kim, Dan; Yoon, Victoria