AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …

We survey 146 papers analyzing" bias" in NLP systems, finding that their motivations are
often vague, inconsistent, and lacking in normative reasoning, despite the fact that …

Generative AI (GenAI) has shown remarkable capabilities in generating diverse and realistic
content across different formats like images, videos, and text. In Generative AI, human …

Ethical deliberation has proved a consistent feature of Human-Computer Interaction (HCI)
since its earliest years, spanning the respectful involvement of research participants to …

Warning: This paper contains examples of gender non-affirmative language which could be
offensive, upsetting, and/or triggering. Transgender and non-binary (TGNB) individuals …

Advances in algorithmic fairness have largely omitted sexual orientation and gender identity.
We explore queer concerns in privacy, censorship, language, online safety, health, and …

We propose a new research framework by which the nascent discipline of human-AI
teaming can be explored within experimental environments in preparation for transferal to …

Critical scholarship has elevated the problem of gender bias in data sets used to train virtual
assistants (VAs). Most work has focused on explicit biases in language, especially against …

Gender inequality is embedded in our communication practices and perpetuated in
translation technologies. This becomes particularly apparent when translating into …

Much research has sought to evaluate the degree to which large language models reflect
social biases. We complement such work with an approach to elucidating the connections …