本文研究了高等教育场景下大语言模型（Large Language Model，LLM）生成的教师评价中的性别偏见，重点关注GPT-4在六个学科中生成的评价内容。通过采用包含比值比（Odds Ratio，OR）分析、词嵌入关联测试（Word Embedding Association Test，WEAT）、情感分析和语境分析的综合分析框架，本文识别出反映社会刻板印象的性别关联语言模式。具体而言，与亲和力和支持相关的词汇更频繁地用于描述女性教师，而与趣味性相关的词汇则主要用于描述男性教师，这与社群性和能动性行为的概念相一致。本研究还发现，男性特征形容词与男性名字之间存在中等至强的关联，尽管职业与家庭相关词汇并未清晰体现出性别偏见。这些发现与此前关于社会规范和刻板印象的研究一致，进一步证实了LLM生成的文本会反映现有偏见的观点。
英文摘要：
This paper investigates gender bias in Large Language Model (LLM)-generated teacher evaluations in higher education setting, focusing on evaluations produced by GPT-4 across six academic subjects. By applying a comprehensive analytical framework that includes Odds Ratio (OR) analysis, Word Embedding Association Test (WEAT), sentiment analysis, and contextual analysis, this paper identified patterns of gender-associated language reflecting societal stereotypes. Specifically, words related to approachability and support were used more frequently for female instructors, while words related to entertainment were predominantly used for male instructors, aligning with the concepts of communal and agentic behaviors. The study also found moderate to strong associations between male salient adjectives and male names, though career and family words did not distinctly capture gender biases. These findings align with prior research on societal norms and stereotypes, reinforcing the notion that LLM-generated text reflects existing biases.
Unveiling and Mitigating Bias in Mental Health Analysis with Large Language Models
Stanford University（斯坦福大学）；Meta Platforms, Inc.（元平台公司）；ETH Zürich（苏黎世联邦理工学院）；University Medical Center Utrecht（乌得勒支大学医学中心）；Leiden University Medical Center（莱顿大学医学中心）
Few-Shot Fairness: Unveiling LLM's Potential for Fairness-Aware Classification
Indian Institute of Technology Delhi（印度德里理工学院）；Delhi Technological University（德里理工大学）；Indian Institute of Science Education and Research Kolkata（印度加尔各答科学教育与研究学院）
ZPD-SCA: Unveiling the Blind Spots of LLMs in Assessing Students' Cognitive Abilities
School of Psychology, South China Normal University（南方科技大学心理学院）；Information Hub, Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）信息中心）；School of AI, Guangzhou University（广州大学人工智能学院）；College of Cyber Security, Jinan University（济南大学网络安全学院）
Where Should I Study? Biased Language Models Decide! Evaluating Fairness in LMs for Academic Recommendations
Centre for Responsible AI (CeRAI), Wadhwani School of Data Science and AI (WSAI)（负责任人工智能中心（CeRAI）、Wadhwani数据科学与人工智能学校（WSAI））；Indian Institute of Technology Madras（印度理工学院马德拉斯学院）
Unveiling the Learning Mind of Language Models: A Cognitive Framework and Empirical Study
Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou), China（香港科学与技术大学人工智能研究所）；Department of Computer Science and Engineering, The Hong Kong University of Science and Technology Hong Kong SAR, China（香港科学与技术大学计算机科学与工程系）；Microsoft Research Asia（微软亚洲研究院）；University of Cambridge（剑桥大学）；Microsoft（微软）
DECASTE: Unveiling Caste Stereotypes in Large Language Models through Multi-Dimensional Bias Analysis
IBM Research, San Jose, CA, USA（IBM研究院，美国加利福尼亚州圣何塞）；Dartmouth College, Hanover, NH, USA（达特茅斯学院，美国新罕布什尔州汉诺威）；IBM Research, Hursley, Winchester, UK（IBM研究院，英国赫里福德郡温切斯特）；IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA（IBM托马斯·J·沃森研究中心，美国纽约州亚当斯）；IBM Research, Sao Paulo, Brazil（IBM研究院，巴西圣保罗）
ParlAI Vote: A Web Platform for Analyzing Gender and Political Bias in Large Language Models
Purdue University（普渡大学）；Johns Hopkins University（约翰霍普金斯大学）；Vanderbilt University（范德比大学）；University of Toronto（多伦多大学）；The University of Melbourne（墨尔本大学）
Contextual word embeddings such as BERT have achieved state of the art performance in numerous NLP tasks. Since they are optimized to capture the statistical properties of training data, they tend to pick up on and amplify social stereotypes present in the data as well. In this study, we (1)~propose a template-based method to quantify bias in BERT; (2)~show that this method obtains more consistent results in capturing social biases than the traditional cosine based method; and (3)~conduct a case study, evaluating gender bias in a downstream task of Gender Pronoun Resolution. Although our case …
Unveiling Biases while Embracing Sustainability: Assessing the Dual Challenges of Automatic Speech Recognition Systems
IDIAP（IDIAP研究所）；MBZUAI（穆罕默德·本·扎耶德人工智能大学）；Erisha Labs（Erisha实验室）；Université de Lorraine（洛林大学）；CNRS（法国国家科学研究中心）；LORIA（洛里亚实验室）；INESC-ID（INESC-ID研究所）；IST（里斯本高等技术学院）；Universidade de Lisboa（里斯本大学）
College of Information and Communications（信息与传播学院）；University of South Carolina（南卡罗来纳大学）；University of Arkansas（阿肯色大学）
Sexual harassment in academia is often a hidden problem because victims are usually reluctant to report their experiences. Recently, a web survey was developed to provide an opportunity to share thousands of sexual harassment experiences in academia. Using an efficient approach, this study collected and investigated more than 2,000 sexual harassment experiences to better understand these unwanted advances in higher education. This paper utilized text mining to disclose hidden topics and explore their weight across three variables: harasser gender, institution type, and victim's field of study.…
