2 citations · 7 across the 19 of their papers we have counts for
21 papers
"AI is (not) the new...": A Diagnostic Analogy Framework for Generative AI's Cultural Impacts
Rida Qadri, Vinodkumar Prabhakaran, Remi Denton
Generative AI is reshaping the cultural infrastructures through which knowledge is found, synthesized, and held accountable. To make sense of this shift, scholars and policymakers…
Humanlike AI Design Increases Anthropomorphism but Yields Divergent Outcomes on Engagement and Trust Globally
Robin Schimmelpfennig, Mark Díaz, Vinodkumar Prabhakaran +1
Over a billion users globally interact with AI systems engineered to mimic human traits. This development raises concerns that anthropomorphism, the attribution of human characteri…
Taxonomy of User Needs and Actions
Renee Shelby, Fernando Diaz, Vinodkumar Prabhakaran
The growing ubiquity of conversational AI highlights the need for frameworks that capture not only users' instrumental goals but also the situated, adaptive, and social practices t…
"Just a strange pic": Evaluating 'safety' in GenAI Image safety annotation tasks from diverse annotators' perspectives
Ding Wang, Mark Díaz, Charvi Rastogi +10
Understanding what constitutes safety in AI-generated content is complex. While developers often rely on predefined taxonomies, real-world safety judgments also involve personal, s…
Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models
Charvi Rastogi, Tian Huey Teh, Pushkar Mishra +10
Current text-to-image (T2I) models often fail to account for diverse human experiences, leading to misaligned systems. We advocate for pluralistic alignment, where an AI understand…
Decoding Safety Feedback from Diverse Raters: A Data-driven Lens on Responsiveness to Severity
Pushkar Mishra, Charvi Rastogi, Stephen R. Pfohl +9
Ensuring the safety of Generative AI requires a nuanced understanding of pluralistic viewpoints. In this paper, we introduce a novel data-driven approach for analyzing ordinal safe…
Towards Geo-Culturally Grounded LLM Generations
Piyawat Lertvittayakumjorn, David Kinney, Vinodkumar Prabhakaran +2
Generative large language models (LLMs) have demonstrated gaps in diverse cultural awareness across the globe. We investigate the effect of retrieval augmented generation and searc…
Risks of Cultural Erasure in Large Language Models
Rida Qadri, Aida M. Davani, Kevin Robinson +1
Large language models are increasingly being integrated into applications that shape the production and discovery of societal knowledge such as search, online education, and travel…
A Comprehensive Framework to Operationalize Social Stereotypes for Responsible AI Evaluations
Aida Davani, Sunipa Dev, Héctor Pérez-Urbina +1
Societal stereotypes are at the center of a myriad of responsible AI interventions targeted at reducing the generation and propagation of potentially harmful outcomes. While these…
Insights on Disagreement Patterns in Multimodal Safety Perception across Diverse Rater Groups
Charvi Rastogi, Tian Huey Teh, Pushkar Mishra +10
AI systems crucially rely on human ratings, but these ratings are often aggregated, obscuring the inherent diversity of perspectives in real-world phenomenon. This is particularly…
Beyond Aesthetics: Cultural Competence in Text-to-Image Models
Nithish Kannen, Arif Ahmad, Marco Andreetto +5
Text-to-Image (T2I) models are being increasingly adopted in diverse global communities where they create visual representations of their unique cultures. Current T2I benchmarks pr…
(Unfair) Norms in Fairness Research: A Meta-Analysis
Jennifer Chien, A. Stevie Bergman, Kevin R. McKee +5
Algorithmic fairness has emerged as a critical concern in artificial intelligence (AI) research. However, the development of fair AI systems is not an objective process. Fairness i…
D3CODE: Disentangling Disagreements in Data across Cultures on Offensiveness Detection and Evaluation
Aida Mostafazadeh Davani, Mark Díaz, Dylan Baker +1
While human annotations play a crucial role in language technologies, annotator subjectivity has long been overlooked in data collection. Recent studies that have critically examin…
GeniL: A Multilingual Dataset on Generalizing Language
Aida Mostafazadeh Davani, Sagar Gubbi, Sunipa Dev +2
Generative language models are transforming our digital ecosystem, but they often inherit societal biases, for instance stereotypes associating certain attributes with specific ide…
Naive Bayes-based Context Extension for Large Language Models
Jianlin Su, Murtadha Ahmed, Wenbo +3
Large Language Models (LLMs) have shown promising in-context learning abilities. However, conventional In-Context Learning (ICL) approaches are often impeded by length limitations…
SeeGULL Multilingual: a Dataset of Geo-Culturally Situated Stereotypes
Mukul Bhutani, Kevin Robinson, Vinodkumar Prabhakaran +2
While generative multilingual models are rapidly being deployed, their safety and fairness evaluations are largely limited to resources collected in English. This is especially pro…
ViSAGe: A Global-Scale Analysis of Visual Stereotypes in Text-to-Image Generation
Akshita Jha, Vinodkumar Prabhakaran, Remi Denton +5
Recent studies have shown that Text-to-Image (T2I) model generations can reflect social stereotypes present in the real world. However, existing approaches for evaluating stereotyp…
Disentangling Perceptions of Offensiveness: Cultural and Moral Correlates
Aida Davani, Mark Díaz, Dylan Baker +1
Perception of offensiveness is inherently subjective, shaped by the lived experiences and socio-cultural values of the perceivers. Recent years have seen substantial efforts to bui…
SoUnD Framework: Analyzing (So)cial Representation in (Un)structured (D)ata
Mark Díaz, Sunipa Dev, Emily Reif +2
The unstructured nature of data used in foundation model development is a challenge to systematic analyses for making data use and documentation decisions. From a Responsible AI pe…
A Taxonomy of Rater Disagreements: Surveying Challenges & Opportunities from the Perspective of Annotating Online Toxicity
Wenbo Zhang, Hangzhi Guo, Ian D Kivlichan +3
Toxicity is an increasingly common and severe issue in online spaces. Consequently, a rich line of machine learning research over the past decade has focused on computationally det…
GRASP: A Disagreement Analysis Framework to Assess Group Associations in Perspectives
Vinodkumar Prabhakaran, Christopher Homan, Lora Aroyo +6
Human annotation plays a core role in machine learning -- annotations for supervised models, safety guardrails for generative models, and human feedback for reinforcement learning,…