Conversational agents that mimic people have raised questions about the ethics of anthropomorphizing machines with human social identity cues. Critics have also questioned assumptions of identity neutrality in humanlike agents. Recent work has revealed that intersectional Japanese pronouns can elicit complex and sometimes evasive impressions of agent identity. Yet, the role of other “neutral” non-pronominal self-referents (NPSR) and voice as a socially expressive medium remains unexplored. In a crowdsourcing study, Japanese participants (N = 204) evaluated three ChatGPT voices (Juniper, Breeze, and Ember) using seven self-referents. We found strong evidence of voice gendering alongside the potential of intersectional self-referents to evade gendering, i.e., ambiguity through neutrality and elusiveness. Notably, perceptions of age and formality intersected with gendering as per sociolinguistic theories, especiallyぼく (boku) andわたくし (watakushi). This work provides a nuanced take on agent identity perceptions and champions intersectional and culturally-sensitive work on voice agents.
Competition is typically centered on balance, fairness, and symmetric play. However, in mixed-ability competition, symmetric play is often not possible or desirable. Currently, it is not clear what can or should be done in the pursuit of the design of inclusive competitive experiences (in sports and games). In this paper, we interview 15 people with motor or visual disabilities who actively engage in competitive activities (e.g., Paralympics, competitive gaming). We focus on understanding engagement and fairness perspectives within mixed-ability competitive scenarios, highlighting the obstacles and opportunities these interactions present. We relied on thematic analysis to examine the motivations to compete, team structures and roles, perspectives on ability disclosure and rankings, and a reflection on the role of technology in mediating competition. We contribute with an understanding of (1) how competition is experienced, (2) key factors influencing inclusive and fair competition, and (3) reflections for the design of inclusive competitive experiences.
Assistive technologies, such as smartphone-based object-recognition (OR) apps, provide visual assistance to people who are blind or low-vision to enable increased independent participation in society. While previous research has explored the functional accessibility of object-recognition technologies, little attention has been given to their social accessibility, particularly in interdependent socio-cultural contexts of the Global South. Through a mixed-methods approach, employing a seven-day diary study followed by one-on-one interviews with seven OR app users in India, we explore their experiences in depth. Our findings highlight the nuances of what interdependence looks like in a multicultural, Indian society, as people navigate public and private spheres with a camera-based assistive technology designed for independent, western contexts. We argue for the necessity to design assistive technologies following the interdependence framework that accommodates the social and cultural context of the Global South. Additionally, we propose design guidelines for assistive technologies in community-oriented societies, emphasizing community-centered approaches, cultural alignment, and locally adaptable designs.
Despite numerous efforts to mitigate their biases, ML systems continue to harm already-marginalized people. While predominant ML approaches assume bias can be removed and fair models can be created, we show that these are not always possible, nor desirable, goals. We reframe the problem of ML bias by creating models to identify biased language, drawing attention to a dataset’s biases rather than trying to remove them. Then, through a workshop, we evaluated the models for a specific use case: workflows of information and heritage professionals. Our findings demonstrate the limitations of ML for identifying bias due to its contextual nature, the way in which approaches to mitigating it can simultaneously privilege and oppress different communities, and its inevitability. We demonstrate the need to expand ML approaches to bias and fairness, providing a mixed-methods approach to investigating the feasibility of removing bias or achieving fairness in a given ML use case.
Many technologists who work in robotics and AI bristle at the idea that human worker displacement is problematic. Others wish to account for workers' needs, but face pervasive myths about the impacts of these technologies. This paper aims to clear the air by refuting five common arguments for automation: 1) the jobs being automated are undesirable, 2) labor shortages necessitate automation, 3) by "augmenting rather than automating'" labor displacement will be prevented, 4) there will be new and better job creation, and 5) automation will give us all more leisure time. The advent of foundational models has led to an industrial gold rush, accelerating deployment without careful consideration of responsible and sustaiable design and deployment of these technologies. Despite technologists' best intentions, this path of pervasive automation we are on is not a good one, and we offer suggestions for how technologists, designers, and decision makers can push for worker-centered technological change moving forward.
North Korean defectors (NKDs) face significant challenges when transitioning to South Korean society. Leaving their homes permanently and adapting to a new, digitally connected environment for the first time presents difficulties, compounded by the pervasive stigma associated with their identities. Although technology alone cannot solve these issues, it can play a role in easing their transition. In this study, we conducted eight speculative co-creation sessions with 22 NKDs to identify their main challenges and envision potential technological interventions. We propose the conceptualization of thirteen technologies aimed at addressing key issues NKDs face related to identity stigma, disconnection from their past, and challenges of adapting to a highly digital society. Through this empirical research on underrepresented populations undergoing significant life transitions, we provide insights into how future technologies can support other marginalized individuals as they navigate pervasive stigma and establish new lives in a digital society.
With the rapid development and release of generative AI (genAI) applications, policy discourses primarily take place on an expert level. Little space is given to laypeople - who have to adapt to and adopt the genAI innovations - to share their opinions and experiences. Addressing this gap, we organized 6h/3.5h laypeople dialogues in Nigeria, Japan, and Germany in July and August 2024. During the dialogues, participants discussed what a desirable future in light of genAI development could look like in one of three contexts: education, public service, and arts & culture. Participants explored the consequences of technology deployment, assessed the risks, mapped stakeholders, and derived measures to achieve a desirable goal. This study contributes to policy debates on genAI by providing recommendations derived from participants' identified requirements and suggested measures for genAI to create value and to foster a socially desirable future. We reflect on the results through a cross-national lens.