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).

These notions vary across conceptualization Intersectionality is a critical framework that, through inquiry and (e.g., group, individual fairness [8]) and operationalization (e.g., praxis, allows us to examine how social inequalities persist through pre/in/post-processing [2]) [54]; nevertheless, the literature generally domains of structure and discipline. Given AI fairness' raison d'être agrees on the goal of minimizing negative outcomes across of "fairness," we argue that adopting intersectionality as an analytical demographic groups, including groups associated with multiple, framework is pivotal to effectively operationalizing fairness. "intersectional" demographic attributes (e.g., Black women) [92]. Through a critical review of how intersectionality is discussed in However, Kong [66] observes that AI fairness papers often narrowly 30 papers from the AI fairness literature, we deductively and inductively: interpret intersectional subgroup fairness as intersectionality, the 1) map how intersectionality tenets operate within the critical framework from which the term originates [29, 67]. This AI fairness paradigm and 2) uncover gaps between the conceptualization myopic conceptualization of intersectionality has non-trivial consequences and operationalization of intersectionality. We find that for just AI design and epistemology (i.e., ways of knowing).

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

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.

Progress in NLP is increasingly measured through benchmarks; hence, contextualizing progress requires understanding when and why practitioners may disagree about the validity of benchmarks. We develop a taxonomy of disagreement, drawing on tools from measurement modeling, and distinguish between two types of disagreement: 1) how tasks are conceptualized and 2) how measurements of model performance are operationalized. To provide evidence for our taxonomy, we conduct a meta-analysis of relevant literature to understand how NLP tasks are conceptualized, as well as a survey of practitioners about their impressions of different factors that affect benchmark validity. Our meta-analysis and survey across eight tasks, ranging from coreference resolution to question answering, uncover that tasks are generally not clearly and consistently conceptualized and benchmarks suffer from operationalization disagreements. These findings support our proposed taxonomy of disagreement. Finally, based on our taxonomy, we present a framework for constructing benchmarks and documenting their limitations.

AI, machine learning, and data science methods are already pervasive in our society and technology, affecting all of our lives in many subtle ways. Trustworthy AI has become an important topic because trust in AI systems and their creators has been lost, or was never present in the first place. Researchers, corporations, and governments have long and painful histories of excluding marginalized groups from technology development, deployment, and oversight. As a direct result of this exclusion, these technologies have long histories of being less useful or even harmful to minoritized groups. This infuriating history illustrates that industry cannot be trusted to self-regulate and why trust in commercial AI systems and development has been lost. We argue that any AI development, deployment, and monitoring framework that aspires to trust must incorporate both feminist, non-exploitative participatory design principles and strong, outside, and continual monitoring and testing. We additionally explain the importance of considering aspects of trustworthiness beyond just transparency, fairness, and accountability, specifically, to consider justice and shifting power to the people and disempowered as core values to any trustworthy AI system. Creating trustworthy AI starts by funding, supporting, and empowering groups like Queer in AI so the field of AI has the diversity and inclusion to credibly and effectively develop trustworthy AI. Through our years of work and advocacy, we have developed expert knowledge around questions of if and how gender, sexuality, and other aspects of identity should be used in AI systems and how harms along these lines should be mitigated. Based on this, we discuss a gendered approach to AI, and further propose a queer epistemology and analyze the benefits it can bring to AI.

Gender is widely discussed in the context of language tasks and when examining the stereotypes propagated by language models. However, current discussions primarily treat gender as binary, which can perpetuate harms such as the cyclical erasure of non-binary gender identities. These harms are driven by model and dataset biases, which are consequences of the non-recognition and lack of understanding of non-binary genders in society. In this paper, we explain the complexity of gender and language around it, and survey non-binary persons to understand harms associated with the treatment of gender as binary in English language technologies. We also detail how current language representations (e.g., GloVe, BERT) capture and perpetuate these harms and related challenges that need to be acknowledged and addressed for representations to equitably encode gender information.