Traditional GAN (Generative Adversarial Network) architectures often reproduce biases present in their training data, leading to synthetic data that may unfairly impact certain subgroups. Past efforts to improve fairness in GANs usually target single demographic categories, like sex or race, but overlook intersectionality. Our approach addresses this gap by integrating an intersectionality framework with explainability techniques to identify and select problematic sensitive features. These insights are then used to develop intersectional fairness constraints integrated into the GAN training process. We aim to enhance fairness and maintain diverse subgroup representation by addressing intersections of multiple demographic attributes. Specifically, we adjusted the loss functions of two state-of-the-art GAN models for tabular data, including an intersectional demographic parity constraint. Our evaluations indicate that this approach significantly improves fairness in synthetically generated datasets. We compared the outcomes using Adult, and Diabetes datasets when considering the intersection of two sensitive features versus focusing on a single sensitive attribute, demonstrating the effectiveness of our method in capturing more complex biases.

Welfare technology has recently reached older adults in long-term care facilities (LTCFs).Many Swedish municipalities are introducing emerging technologies such as virtual reality, robotic assistive devices, and social robots in LTCFs as part of everyday care.However, not only older adults are affected by these deployments.Healthcare workers are left to master these technologiesand integrate them into existing care practices.Previous research has identified an increase in work-related stress associated with the introduction of technology for healthcare workers.The literature is, however, sparse on how healthcare workers in LTCFs are affected by the introduction.Therefore, we explored different factors that could affect healthcare workers' technostress through an online survey and semi-structured interviews to get a deeper understanding of how healthcare workers are experiencing deployments of welfare technology.The main findings showed that some of the healthcare workers are finding it difficult to adopt and use welfare technology due to, for example, older age, language difficulties, or a negative attitude toward technology.We conclude that municipalities and LTCFs need to invest in their healthcare workers in order to achieve better on-boarding and reduce technostress.

Feminism is more than (and often not even) an interest in women's issues. For our robotics research, we use feminist theory as an analytical toolbox, filled with terms and insights to make visible and probe questions of power, representation, and expectations about and between humans and robots in the entangled encounters produced by social robots. Some of these questions are related to gender. Feminist theory gives us a vocabulary to talk about the materiality of robots, but also their positioning in our social encounters, real and imaginary? and how they position us, the users, in those encounters. This keynote will present some of the theoretical insights from feminism and intersectionality that we have found useful & generative; discuss how and where we apply them to our studies of social robots; and reflect on our experiences using these concepts to teach engineering students.

Human-Robot Interaction (HRI) is inherently a human-centric field of technology. The role of feminist theories in related fields (e.g. Human-Computer Interaction, Data Science) are taken as a starting point to present a vision for Feminist HRI which can support better, more ethical HRI practice everyday, as well as a more activist research and design stance. We first define feminist design for an HRI audience and use a set of feminist principles from neighboring fields to examine existent HRI literature, showing the progress that has been made already alongside some additional potential ways forward. Following this we identify a set of reflexive questions to be posed throughout the HRI design, research and development pipeline, encouraging a sensitivity to power and to individuals' goals and values. Importantly, we do not look to present a definitive, fixed notion of Feminist HRI, but rather demonstrate the ways in which bringing feminist principles to our field can lead to better, more ethical HRI, and to discuss how we, the HRI community, might do this in practice.

Domestic robots are already commonplace in many homes, while humanoid companion robots like Pepper are increasingly becoming part of different kinds of care work. Drawing on fieldwork at a robotics lab, as well as our personal encounters with domestic robots, we use here the metaphor of “hard-to-reach corners” to explore the socio-technical limitations of companion robots and our differing abilities to respond to these limitations. This article presents “hard-to-reach-corners” as a problematic for design interaction, offering them as an opportunity for thinking about context and intersectional aspects of adaptation.