Mauro Cherubini

Pooja S. B. Rao, Laxminarayen Nagarajan Venkatesan, Mauro Cherubini et al.

Artificial Intelligence (AI) is increasingly used in hiring, with large language models (LLMs) having the potential to influence or even make hiring decisions. However, this raises pressing concerns about bias, fairness, and trust, particularly across diverse cultural contexts. Despite their growing role, few studies have systematically examined the potential biases in AI-driven hiring evaluation across cultures. In this study, we conduct a systematic analysis of how LLMs assess job interviews across cultural and identity dimensions. Using two datasets of interview transcripts, 100 from UK and 100 from Indian job seekers, we first examine cross-cultural differences in LLM-generated scores for hirability and related traits. Indian transcripts receive consistently lower scores than UK transcripts, even when they were anonymized, with disparities linked to linguistic features such as sentence complexity and lexical diversity. We then perform controlled identity substitutions (varying names by gender, caste, and region) within the Indian dataset to test for name-based bias. These substitutions do not yield statistically significant effects, indicating that names alone, when isolated from other contextual signals, may not influence LLM evaluations. Our findings underscore the importance of evaluating both linguistic and social dimensions in LLM-driven evaluations and highlight the need for culturally sensitive design and accountability in AI-assisted hiring.

Pooja S. B. Rao, Sanja Šćepanović, Dinesh Babu Jayagopi et al.

We analyzed nearly 460,000 AI model cards from Hugging Face to examine how developers report risks. From these, we extracted around 3,000 unique risk mentions and built the \emph{AI Model Risk Catalog}. We compared these with risks identified by researchers in the MIT Risk Repository and with real-world incidents from the AI Incident Database. Developers focused on technical issues like bias and safety, while researchers emphasized broader social impacts. Both groups paid little attention to fraud and manipulation, which are common harms arising from how people interact with AI. Our findings show the need for clearer, structured risk reporting that helps developers think about human-interaction and systemic risks early in the design process. The catalog and paper appendix are available at: https://social-dynamics.net/ai-risks/catalog.

Gabriela Villalobos-Zúñiga, Mauro Cherubini

Tracking physical activity reliably is becoming central to many research efforts. In the last years specialized hardware has been proposed to measure movement. However, asking study participants to carry additional devices has drawbacks. We focus on using mobile devices as motion sensors. In the paper we detail several issues that we found while using this technique in a longitudinal study involving hundreds of participants for several months. We hope to sparkle a lively discussion at the workshop and attract interest in this method from other researchers.

Gabriela Villalobos Zuñiga, Mauro Cherubini

Older users population is rapidly increasing all over the World. Presently, we observe efforts in the human-computer interaction domain aiming to improve life quality of age 65 and over through the use of mobile apps. Nonetheless, these efforts focus primary on interface and interaction de- sign. Little work has focused on the study of motivation to use and adherence to, of elderly to technology. Developing specific design guidelines for this population is relevant, however it should be parallel to the study of desire of elderly to embrace specific technology in their life. Designers should not be limited to technology design but consider as well how to fully convey the value that technology can bring to the lives of the users and motivate adoption. This position paper discusses techniques that might nudge elderly towards the use of new technology.