Assistive Robots and Reasonable Work Assignment Reduce Perceived Stigma toward Persons with Disabilities

Robots are becoming more prominent in assisting persons with disabilities (PwD). Whilst there is broad consensus that robots can assist in mitigating physical impairments, the extent to which they can facilitate social inclusion remains equivocal. In fact, the exposed status of assisted workers could likewise lead to reduced or increased perceived stigma by other workers. We present a vignette study on the perceived cognitive and behavioral stigma toward PwD in the workplace. We designed four experimental conditions depicting a coworker with an impairment in work scenarios: overburdened work, suitable work, and robot-assisted work only for the coworker, and an offer of robot-assisted work for everyone. Our results show that cognitive stigma is significantly reduced when the work task is adapted to the person's abilities or augmented by an assistive robot. In addition, offering robot-assisted work for everyone, in the sense of universal design, further reduces perceived cognitive stigma. Thus, we conclude that assistive robots reduce perceived cognitive stigma, thereby supporting the use of collaborative robots in work scenarios involving PwDs.

Feeling Machines: Ethics, Culture, and the Rise of Emotional AI

This paper explores the growing presence of emotionally responsive artificial intelligence through a critical and interdisciplinary lens. Bringing together the voices of early-career researchers from multiple fields, it explores how AI systems that simulate or interpret human emotions are reshaping our interactions in areas such as education, healthcare, mental health, caregiving, and digital life. The analysis is structured around four central themes: the ethical implications of emotional AI, the cultural dynamics of human-machine interaction, the risks and opportunities for vulnerable populations, and the emerging regulatory, design, and technical considerations. The authors highlight the potential of affective AI to support mental well-being, enhance learning, and reduce loneliness, as well as the risks of emotional manipulation, over-reliance, misrepresentation, and cultural bias. Key challenges include simulating empathy without genuine understanding, encoding dominant sociocultural norms into AI systems, and insufficient safeguards for individuals in sensitive or high-risk contexts. Special attention is given to children, elderly users, and individuals with mental health challenges, who may interact with AI in emotionally significant ways. However, there remains a lack of cognitive or legal protections which are necessary to navigate such engagements safely. The report concludes with ten recommendations, including the need for transparency, certification frameworks, region-specific fine-tuning, human oversight, and longitudinal research. A curated supplementary section provides practical tools, models, and datasets to support further work in this domain.

Communicating Through Avatars in Industry 5.0: A Focus Group Study on Human-Robot Collaboration

The integration of collaborative robots (cobots) in industrial settings raises concerns about worker well-being, particularly due to reduced social interactions. Avatars - designed to facilitate worker interactions and engagement - are promising solutions to enhance the human-robot collaboration (HRC) experience. However, real-world perspectives on avatar-supported HRC remain unexplored. To address this gap, we conducted a focus group study with employees from a German manufacturing company that uses cobots. Before the discussion, participants engaged with a scripted, industry-like HRC demo in a lab setting. This qualitative approach provided valuable insights into the avatar's potential roles, improvements to its behavior, and practical considerations for deploying them in industrial workcells. Our findings also emphasize the importance of personalized communication and task assistance. Although our study's limitations restrict its generalizability, it serves as an initial step in recognizing the potential of adaptive, context-aware avatar interactions in real-world industrial environments.