Evaluating LLMs for Gender Disparities in Notable Persons
Robert H. Smith School of Business, University of Maryland（马里兰大学罗伯特·H·史密斯商学院）；University of Antwerp（安特卫普大学）；Stern School of Business, New York University（纽约大学斯特恩商学院）
This study examines the use of Large Language Models (LLMs) for retrieving factual information, addressing concerns over their propensity to produce factually incorrect "hallucinated" responses or to altogether decline to even answer prompt at all. Specifically, it investigates the presence of gender-based biases in LLMs' responses to factual inquiries. This paper takes a multi-pronged approach to evaluating GPT models by evaluating fairness across multiple dimensions of recall, hallucinations and declinations. Our findings reveal discernible gender disparities in the responses generated by GPT-3.5. While advancements in GPT-4 have led to improvements in performance, they have not fully eradicated these gender disparities, notably in instances where responses are declined. The study further explores the origins of these disparities by examining the influence of gender associations in prompts and the homogeneity in the responses.
ChatGPT Exhibits Gender and Racial Biases in Acute Coronary Syndrome Management
Stanford Cardiovascular Institute（斯坦福心血管研究所）；Stanford School of Medicine（斯坦福医学院）；Greenstone Biosciences（格林斯通生物科学公司）；Stanford School of Engineering（斯坦福工程学院）；NewYork–Presbyterian Hospital/Weill Cornell Medical Center（纽约长老会医院/威尔康奈尔医学中心）
Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement
Università della Svizzera italiana \& University of Amsterdam；University of Amsterdam The Netherland；Unviersity of Amsterdam The Netherland；University of Amsterdam
Assessing Algorithmic Bias in Language-Based Depression Detection: A Comparison of DNN and LLM Approaches
Computer Science University of Colorado Boulder Boulder, CO, USA；Inst. Cognitive Science \& Computer Science University of Colorado Boulder Boulder, CO, USA
Gender and content bias in Large Language Models: a case study on Google Gemini 2.0 Flash Experimental
University of Perugia（佩鲁贾大学）；Chapin Hall at University of Chicago（芝加哥大学查平学院）；Berdyansk State Pedagogical University（别尔江斯克国立师范大学）；Università di Bologna（博洛尼亚大学）；University of North Texas（北得克萨斯大学）；Chapin Hall（查平学院）
Gender Bias in Explainability: Investigating Performance Disparity in Post-hoc Methods
Technical University of Munich（慕尼黑技术大学）；School of Computation, Information and Technology（计算、信息与技术学院）；Department of Computer Science（计算机科学系）；Technische Universität Berlin（柏林技术大学）；BIFOLD – Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）
Inferring gender from name: a large scale performance evaluation study
Columbia Business School, Columbia University（哥伦比亚大学商学院）；Data Science Institute, Columbia University（哥伦比亚大学数据科学研究所）；Department of Sociology, Columbia University（哥伦比亚大学社会学系）
