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Found 15 papers, 1 papers with code
The Cake that is Intelligence and Who Gets to Bake it: An AI Analogy and its Implications for Participation
In a widely popular analogy by Turing Award Laureate Yann LeCun, machine intelligence has been compared to cake - where unsupervised learning forms the base, supervised learning adds the icing, and reinforcement learning is the cherry on top.
The Root Shapes the Fruit: On the Persistence of Gender-Exclusive Harms in Aligned Language Models
Natural-language assistants are designed to provide users with helpful responses while avoiding harmful outputs, largely achieved through alignment to human preferences.
Survey of Bias In Text-to-Image Generation: Definition, Evaluation, and Mitigation
In this survey, we review prior studies on dimensions of bias: Gender, Skintone, and Geo-Culture.
Tokenization Matters: Navigating Data-Scarce Tokenization for Gender Inclusive Language Technologies
Our paper is the first to link LLM misgendering to tokenization and deficient neopronoun grammar, indicating that LLMs unable to correctly treat neopronouns as pronouns are more prone to misgender.
Should they? Mobile Biometrics and Technopolicy meet Queer Community Considerations
To facilitate these functionalities, smartphones rely on integrated sensors like accelerometers and gyroscopes.
Bound by the Bounty: Collaboratively Shaping Evaluation Processes for Queer AI Harms
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.
Evaluating the Social Impact of Generative AI Systems in Systems and Society
Generative AI systems across modalities, ranging from text (including code), image, audio, and video, have broad social impacts, but there is no official standard for means of evaluating those impacts or for which impacts should be evaluated.
ChatGPT for Us: Preserving Data Privacy in ChatGPT via Dialogue Text Ambiguation to Expand Mental Health Care Delivery
Large language models have been useful in expanding mental health care delivery.
"I'm fully who I am": Towards Centering Transgender and Non-Binary Voices to Measure Biases in Open Language Generation
Transgender and non-binary (TGNB) individuals disproportionately experience discrimination and exclusion from daily life.
Queer In AI: A Case Study in Community-Led Participatory AI
We present Queer in AI as a case study for community-led participatory design in AI.
Factoring the Matrix of Domination: A Critical Review and Reimagination of Intersectionality in AI Fairness
Through a critical review of how intersectionality is discussed in 30 papers from the AI fairness literature, we deductively and inductively: 1) map how intersectionality tenets operate within the AI fairness paradigm and 2) uncover gaps between the conceptualization and operationalization of intersectionality.
Auditing Algorithmic Fairness in Machine Learning for Health with Severity-Based LOGAN
Therefore, ML auditing tools must be (1) better aligned with ML4H auditing principles and (2) able to illuminate and characterize communities vulnerable to the most harm.
Improving Adversarial Robustness to Sensitivity and Invariance Attacks with Deep Metric Learning
Intentionally crafted adversarial samples have effectively exploited weaknesses in deep neural networks.
Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologies
Gender is widely discussed in the context of language tasks and when examining the stereotypes propagated by language models.
COVID-19 and Big Data: Multi-faceted Analysis for Spatio-temporal Understanding of the Pandemic with Social Media Conversations
We provide a multi-faceted analysis of critical properties exhibited by these conversations on social media regarding the novel coronavirus pandemic.
