2 citations · 2 across the 2 of their papers we have counts for
5 papers
Textual Data Bias Detection and Mitigation -- An Extensible Pipeline with Experimental Evaluation
Rebekka Görge, Sujan Sai Gannamaneni, Tabea Naeven +10
Textual data used to train large language models (LLMs) exhibits multifaceted bias manifestations encompassing harmful language and skewed demographic distributions. Regulations su…
Diverse Human Value Alignment for Large Language Models via Ethical Reasoning
Jiahao Wang, Songkai Xue, Jinghui Li +1
Ensuring that Large Language Models (LLMs) align with the diverse and evolving human values across different regions and cultures remains a critical challenge in AI ethics. Current…
Distributionally Robust Performative Prediction
Songkai Xue, Yuekai Sun
Performative prediction aims to model scenarios where predictive outcomes subsequently influence the very systems they target. The pursuit of a performative optimum (PO) -- minimiz…
Statistical inference for individual fairness
Subha Maity, Songkai Xue, Mikhail Yurochkin +1
As we rely on machine learning (ML) models to make more consequential decisions, the issue of ML models perpetuating or even exacerbating undesirable historical biases (e.g., gende…
Auditing ML Models for Individual Bias and Unfairness
Songkai Xue, Mikhail Yurochkin, Yuekai Sun
We consider the task of auditing ML models for individual bias/unfairness. We formalize the task in an optimization problem and develop a suite of inferential tools for the optimal…