AI safety is a contentious topic. While some prominent figures of the AI community have argued that destructive general artificial intelligence (AI) is on the horizon, others derided their warning as a marketing stunt to sell large language models (LLMs). "If the call for'AI safety' is couched in terms of protecting humanity from rogue AIs, it very conveniently displaces accountability away from the corporations scaling harm in the name of profits," tweeted Emily Bender, a professor of computational linguistics at the University of Washington. Focusing on potential future harm from ever more powerful AI systems distracts from harm that is already happening today. Most of us do not set out to make software that is actively harmful.

Bias evaluation benchmarks and dataset and model documentation have emerged as central processes for assessing the biases and harms of artificial intelligence (AI) systems. However, these auditing processes have been criticized for their failure to integrate the knowledge of marginalized communities and consider the power dynamics between auditors and the communities. Consequently, modes of bias evaluation have been proposed that engage impacted communities in identifying and assessing the harms of AI systems (e.g., bias bounties). Even so, asking what marginalized communities want from such auditing processes has been neglected. In this paper, we ask queer communities for their positions on, and desires from, auditing processes. To this end, we organized a participatory workshop to critique and redesign bias bounties from queer perspectives. We found that when given space, the scope of feedback from workshop participants goes far beyond what bias bounties afford, with participants questioning the ownership, incentives, and efficacy of bounties. We conclude by advocating for community ownership of bounties and complementing bounties with participatory processes (e.g., co-creation).

We present Queer in AI as a case study for community-led participatory design in AI. We examine how participatory design and intersectional tenets started and shaped this community's programs over the years. We discuss different challenges that emerged in the process, look at ways this organization has fallen short of operationalizing participatory and intersectional principles, and then assess the organization's impact. Queer in AI provides important lessons and insights for practitioners and theorists of participatory methods broadly through its rejection of hierarchy in favor of decentralization, success at building aid and programs by and for the queer community, and effort to change actors and institutions outside of the queer community. Finally, we theorize how communities like Queer in AI contribute to the participatory design in AI more broadly by fostering cultures of participation in AI, welcoming and empowering marginalized participants, critiquing poor or exploitative participatory practices, and bringing participation to institutions outside of individual research projects. Queer in AI's work serves as a case study of grassroots activism and participatory methods within AI, demonstrating the potential of community-led participatory methods and intersectional praxis, while also providing challenges, case studies, and nuanced insights to researchers developing and using participatory methods.

While Bilenko's short purple pixie haircut wasn't particularly notable amongst the diverse team that brought Dr. Brainlove to Burning Man, it did cause her to stand out when she attended scientific conferences. "AI is a profession that excludes a lot of people from participating, and that's a huge problem," she says. Chatting with a handful of non-binary and queer scientists among the thousands attending a key research conference in the field, they brainstormed how to address these issues. Since then, Queer in AI members have worked to become a more visible presence in the larger AI community, returning to that conference and others each year to host social gatherings, lead mentoring sessions, and give research talks. Through their original research and advocacy, they encourage and highlight new findings to address various concerns, such as the ethical use of AI, privacy and safety, and how binary-based model assumptions can harm members of the queer community.

Advances in algorithmic fairness have largely omitted sexual orientation and gender identity. We explore queer concerns in privacy, censorship, language, online safety, health, and employment to study the positive and negative effects of artificial intelligence on queer communities. These issues underscore the need for new directions in fairness research that take into account a multiplicity of considerations, from privacy preservation, context sensitivity and process fairness, to an awareness of sociotechnical impact and the increasingly important role of inclusive and participatory research processes. Most current approaches for algorithmic fairness assume that the target characteristics for fairness--frequently, race and legal gender--can be observed or recorded. Sexual orientation and gender identity are prototypical instances of unobserved characteristics, which are frequently missing, unknown or fundamentally unmeasurable. This paper highlights the importance of developing new approaches for algorithmic fairness that break away from the prevailing assumption of observed characteristics.

A few months ago, a friend introduced me to this peculiar horror game he was playing on Nintendo Switch. The game, Dead by Daylight, originally came out in 2016, but quickly enveloped my life. Working from home with minimal social interaction and looming financial precarity put a heavy strain on my mental health, and a horror video game where I'm constantly fighting for survival felt like a kind of virtual exposure therapy. If you haven't played before, here are the gameplay basics for Dead by Daylight. Five players are in each round: one killer and a team of four survivors.

What perhaps sets these new brands apart from their predecessors, then, is their push to expand the visibility of the queer community. For instance, one user might not know much about another offline, but he might know little things about him from having scrolled through his geotagged social media page. He might even recognize him from his profile photos walking down the street, or in the audience of, say, a recent panel about digital content by and for the queer community. Far from keeping queer men on the fringes, these apps are fueling a novel knowingness among users--on the app, yes, but also offline, when users go out to create and engage with open communities. These apps are playing host to conversations--silent and verbal, private and public--about what, exactly, the queer experience can entail.