arXiv subjects

Christoph Lutz

Publications and source records attributed to Christoph Lutz.

3 recordsLinked to original sources

arXiv2026

Anthropomorphic Behaviors of AI

Anthropomorphism in artificial intelligence (AI) is a growing area of interest, as AI systems increasingly exhibit human-like expressions, behaviors and interaction styles. This research serves as a systematic observation and categorization of anthropomorphic behaviors in AI outputs. Anthropomorphic behavior refers to the deliberate or emergent manifestation of human-like expressions or linguistic cues in system outputs, such as demonstrating empathy. Using a behaviorally driven taxonomy, our study identifies key forms of anthropomorphic behaviors in the responses of ChatGPT and examines their implications for the theory, practice and ethics of AI systems. The taxonomy enables more nuanced detection of anthropomorphism, offering value to developers and policymakers in balancing the benefits with the potential risks. This work contributes to the academic discourse by providing a foundation for future efforts to refine, expand, and automate the detection of anthropomorphic behavior across diverse AI applications.

cs.HC↗

arXiv2026

Navigating the muddy waters of bias in artificial intelligence research: Understanding divergent meanings and conceptions

As artificial intelligence (AI) pervades many decision-making domains, AI bias grows in importance. Although there is increasing awareness of the social and ethical consequences of biased AI, understanding bias from the perspective of those who develop these systems, such as the AI research community, is less clear. In this study, we employ topic modeling on 6520 articles to explore how the AI research community interprets the concept of bias. Our results show that the definition of bias is dispersed and complex within the community, often exhibiting even divergent conceptions (some even view and introduce bias as a tunable statistical parameter rather than an undesirable issue). The research community as a whole needs to engage more effectively with the concept of bias and establish a more cohesive understanding of it. We specifically argue that, although some sub-communities view bias as an issue that can be captured and mitigated through technical, computational, or statistical methods, it is not solely a technical problem. It instead involves contextual, social, and ethical factors that require broader sociotechnical perspectives and solutions.

cs.CY↗

arXiv2005

Surmounting Barriers: The Benefit of Hydrodynamic Interactions

We experimentally and theoretically investigate the collective behavior of three colloidal particles that are driven by a constant force along a toroidal trap. Due to hydrodynamic interactions, a characteristic limit cycle is observed. When we additionally apply a periodic sawtooth potential, we find a novel caterpillar-like motional sequence that is dominated by hydrodynamic interactions and promotes the surmounting of potential barriers by the particles.

cond-mat.soft↗