63 citations · 65 across the 7 of their papers we have counts for
9 papers
Homogenization in Multi-Agent Systems
Prakhar Ganesh, Kyra Wilson, Luca Zappella +4
Multi-agent systems (MAS) leverage interactions between agents to perform complex tasks. Despite their success, we show that these interactions can also lead to homogenization, i.e…
Applications of Risk Science to AI Fairness Evaluation: Principles, Challenges, and Best Practices
Kyra Wilson, Sabrina Kang, Saloni Dash +1
Scholarly work which aims to describe potential societal impacts (e.g., risks) of proliferating technology (especially related to artificial intelligence or other algorithmic syste…
Resume Screening, Fast and Slow: (Biased) AI Recommendations' Influence on Human Decision Making
Kyra Wilson, Mattea Sim, Anna-Maria Gueorguieva +2
AI is increasingly being used collaboratively with people to make decisions in high-stakes domains, but this new paradigm is still not well-understood in many respects -- particula…
CIRCLE: A Framework for Evaluating AI from a Real-World Lens
Reva Schwartz, Carina Westling, Morgan Briggs +12
This study proposes CIRCLE, a six-stage, lifecycle-based framework to bridge the reality gap between model-centric performance metrics and AI system outcomes in deployment. Current…
No Thoughts Just AI: Biased LLM Hiring Recommendations Alter Human Decision Making and Limit Human Autonomy
Kyra Wilson, Mattea Sim, Anna-Maria Gueorguieva +1
In this study, we conduct a resume-screening experiment (N=528) where people collaborate with simulated AI models exhibiting race-based preferences (bias) to evaluate candidates fo…
Bias Amplification in Stable Diffusion's Representation of Stigma Through Skin Tones and Their Homogeneity
Kyra Wilson, Sourojit Ghosh, Aylin Caliskan
Text-to-image generators (T2Is) are liable to produce images that perpetuate social stereotypes, especially in regards to race or skin tone. We use a comprehensive set of 93 stigma…
Bias is a Math Problem, AI Bias is a Technical Problem: 10-year Literature Review of AI/LLM Bias Research Reveals Narrow [Gender-Centric] Conceptions of 'Bias', and Academia-Industry Gap
Sourojit Ghosh, Kyra Wilson
The rapid development of AI tools and implementation of LLMs within downstream tasks has been paralleled by a surge in research exploring how the outputs of such AI/LLM systems emb…
Intrinsic Bias is Predicted by Pretraining Data and Correlates with Downstream Performance in Vision-Language Encoders
Kshitish Ghate, Isaac Slaughter, Kyra Wilson +2
While recent work has found that vision-language models trained under the Contrastive Language Image Pre-training (CLIP) framework contain intrinsic social biases, the extent to wh…
Gender, Race, and Intersectional Bias in Resume Screening via Language Model Retrieval
Kyra Wilson, Aylin Caliskan
Artificial intelligence (AI) hiring tools have revolutionized resume screening, and large language models (LLMs) have the potential to do the same. However, given the biases which…