This work examines how leading generative artificial intelligence companies construct and communicate the concept of "safety" through public-facing documents. Drawing on critical discourse analysis, we analyze a corpus of corporate safety-related statements to explicate how authority, responsibility, and legitimacy are discursively established. These discursive strategies consolidate legitimacy for corporate actors, normalize safety as an experimental and anticipatory practice, and push a perceived participatory agenda toward safe technologies. We argue that uncritical uptake of these discourses risks reproducing corporate priorities and constraining alternative approaches to governance and design. The contribution of this work is twofold: first, to situate safety as a sociotechnical discourse that warrants critical examination; second, to caution human-computer interaction scholars against legitimizing corporate framings, instead foregrounding accountability, equity, and justice. By interrogating safety discourses as artifacts of power, this paper advances a critical agenda for human-computer interaction scholarship on artificial intelligence.

The European Union (EU) has spearheaded the regulation of artificial intelligence (AI) with the AI Act, which regulates AI systems based on the risks they pose to fundamental rights and other protected values. AI systems that pose unacceptable risks are prohibited, high-risk AI systems must comply with mandatory requirements, and minimal risk AI systems are encouraged—but not required—to adopt voluntary standards. Motivated by concerns that the AI Act may not reflect the public’s opinions, we investigate how laypeople (N = 1,421) assess 48 different AI systems concerning their risk and regulation. We find that people believe all 48 AI systems pose moderate levels of risk and should be regulated (albeit without outright prohibitions). Our findings challenge the AI Act’s tiered approach, showing that people might support horizontal regulation requiring minimal standards for AI systems, and provide implications for developers seeking to develop AI aligned with public expectations.

Algorithmic fairness research often disregards concerns related to systemic injustice.We study how contextualizing algorithms within systemic injustice impacts lay perceptions of algorithmic discrimination.Using the hiring domain as a case-study, we conduct a 2x3 between-participants experiment (𝑁 =716), studying how people's views of algorithmic fairness are influenced by information about (i) systemic injustice in historical hiring decisions and (ii) algorithms' propensity to perpetuate biases learned from past human decisions.We find that shedding light on systemic injustice has heterogeneous effects: participants from historically advantaged groups became more negative about discriminatory algorithms, while those from disadvantaged groups reported more positive attitudes.Explaining that algorithms learn from past human decisions had null effects on people's views, adding nuances to calls for improving public understanding of algorithms.Our findings reveal that contextualizing algorithms in systemic injustice can have unintended consequences and show how different ways of framing existing inequalities influence perceptions of injustice.

HCI and CSCW research has witnessed increasing efforts to address diversity and inclusion in research and design practice, as evidenced by the growing body of research with populations deemed as vulnerable, marginalized, or underserved. However, this work has been largely limited to a population-specific approach, i.e., identifying certain populations as vulnerable and gathering their individual experiences. Drawing primarily from human-centered security and privacy research, we identify three key challenges faced by this population-specific approach: (1) It is limited in addressing user diversity within the target population; (2) It may fail to capture the complex social reality of vulnerability; and (3) It runs the risk of perpetuating othering and stereotypes. To address these limitations, we propose a socio-ecological perspective on vulnerability adapted from the Ecological System Theory (EST). We argue that a socio-ecological perspective of vulnerability can guide researchers to look beyond static and stigmatizing definitions of vulnerability --- instead, focus on the situations, relations, and structures that lead to vulnerability, eventually enabling transferable knowledge of vulnerability across populations. We demonstrate how the socio-ecological lens maps onto existing work and generates new insights in the case of older adults' security and privacy, as well as its potential for being applied to other contexts such as reproductive privacy and responsible artificial intelligence. We end by providing concrete recommendations on how HCI and CSCW research can better operationalize vulnerability in scholarship and design practice.

Artificial intelligence (AI) systems can cause harm to people. This research examines how individuals react to such harm through the lens of blame. Building upon research suggesting that people blame AI systems, we investigated how several factors influence people’s reactive attitudes towards machines, designers, and users. The results of three studies (N = 1,153) indicate differences in how blame is attributed to these actors. Whether AI systems were explainable did not impact blame directed at them, their developers, and their users. Considerations about fairness and harmfulness increased blame towards designers and users but had little to no effect on judgments of AI systems. Instead, what determined people’s reactive attitudes towards machines was whether people thought blaming them would be a suitable response to algorithmic harm. We discuss implications, such as how future decisions about including AI systems in the social and moral spheres will shape laypeople’s reactions to AI-caused harm.

The COVID-19 pandemic has been the single most important global agenda in the past two years. In addition to its health and economic impacts, it has affected people’s psychological states, including a rise in depression and domestic violence. We traced how the overall emotional states of individual Twitter users changed before and after the pandemic. Our data, including more than 9 million tweets posted by 9,493 users, suggest that the threat posed by the virus did not upset the emotional equilibrium of social media. In early 2020, COVID-related tweets skyrocketed in number and were filled with negative emotions; however, this emotional outburst was short-lived. We found that users who had expressed positive emotions in the pre-COVID period remained positive after the initial outbreak, while the opposite was true for those who regularly expressed negative emotions. Individuals achieved such emotional consistency by selectively focusing on emotion-reinforcing topics. The implications are discussed in light of an emotionally motivated confirmation bias, which we conceptualize as emotion bubbles that demonstrate the public’s resilience to a global health risk.

Determining who is responsible for online misinformation is an important problem. This research offers a multifaceted view of the public's perception of who is responsible for online misinformation. Via two studies, we surveyed how people attribute responsibility separately for creating, disseminating, and failing to prevent the dissemination of false information online. Study 1 (N=99) employed a mixed-methods approach to identify a series of actors deemed responsible for each aspect of misinformation. Its open-ended methodology suggested that participants tended to externalize responsibility, which we explored further in the subsequent study. Study 2 (N=496) found that the responsible entities differed for the three distinct aspects of misinformation: online users, news media, and interest groups were associated with creating falsehoods, whereas social media platforms were predominantly seen as accountable for failing to prevent them. Our data shows that blame was directed towards those on the opposite side of the political spectrum, indicating substantial polarization. Most critically, people did not seem to associate themselves with online misinformation and externalized responsibility towards "other users." We discuss implications, including the need to promote personal accountability among users and the social demand for accountable social media platforms and news media.

How to attribute responsibility for autonomous artificial intelligence (AI) systems’ actions has been widely debated across the humanities and social science disciplines. This work presents two experiments (N=200 each) that measure people’s perceptions of eight different notions of moral responsibility concerning AI and human agents in the context of bail decision-making. Using real-life adapted vignettes, our experiments show that AI agents are held causally responsible and blamed similarly to human agents for an identical task. However, there was a meaningful difference in how people perceived these agents’ moral responsibility; human agents were ascribed to a higher degree of present-looking and forward-looking notions of responsibility than AI agents. We also found that people expect both AI and human decision-makers and advisors to justify their decisions regardless of their nature. We discuss policy and HCI implications of these findings, such as the need for explainable AI in high-stakes scenarios.

Whether to give rights to artificial intelligence (AI) and robots has been a sensitive topic since the European Parliament proposed advanced robots could be granted "electronic personalities." Numerous scholars who favor or disfavor its feasibility have participated in the debate. This paper presents an experiment (N=1270) that 1) collects online users' first impressions of 11 possible rights that could be granted to autonomous electronic agents of the future and 2) examines whether debunking common misconceptions on the proposal modifies one's stance toward the issue. The results indicate that even though online users mainly disfavor AI and robot rights, they are supportive of protecting electronic agents from cruelty (i.e., favor the right against cruel treatment). Furthermore, people's perceptions became more positive when given information about rights-bearing non-human entities or myth-refuting statements. The style used to introduce AI and robot rights significantly affected how the participants perceived the proposal, similar to the way metaphors function in creating laws. For robustness, we repeated the experiment over a more representative sample of U.S. residents (N=164) and found that perceptions gathered from online users and those by the general population are similar.