1 citations · 1 across the 5 of their papers we have counts for
6 papers
A Unified Framework to Quantify Cultural Intelligence of AI
Sunipa Dev, Vinodkumar Prabhakaran, Rutledge Chin Feman +16
As generative AI technologies are increasingly being launched across the globe, assessing their competence to operate in different cultural contexts is exigently becoming a priorit…
Cultural Perspectives and Expectations for Generative AI: A Global Survey Approach
Erin van Liemt, Renee Shelby, Andrew Smart +5
There is a lack of empirical evidence about global attitudes around whether and how GenAI should represent cultures. This paper assesses understandings and beliefs about culture as…
How Tech Workers Contend with Hazards of Humanlikeness in Generative AI
Mark Díaz, Renee Shelby, Eric Corbett +1
Generative AI's humanlike qualities are driving its rapid adoption in professional domains. However, this anthropomorphic appeal raises concerns from HCI and responsible AI scholar…
Measuring What Matters: Connecting AI Ethics Evaluations to System Attributes, Hazards, and Harms
Shalaleh Rismani, Renee Shelby, Leah Davis +2
Over the past decade, an ecosystem of measures has emerged to evaluate the social and ethical implications of AI systems, largely shaped by high-level ethics principles. These meas…
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…
Debiasing Text Safety Classifiers through a Fairness-Aware Ensemble
Olivia Sturman, Aparna Joshi, Bhaktipriya Radharapu +2
Increasing use of large language models (LLMs) demand performant guardrails to ensure the safety of inputs and outputs of LLMs. When these safeguards are trained on imbalanced data…