Named Entity Recognition (NER) is often the first step towards automated Knowledge Base (KB) generation from raw text. In this work, we assess the bias in various Named Entity Recognition (NER) systems for English across different demographic groups with synthetically generated corpora. Our analysis reveals that models perform better at identifying names from specific demographic groups across two datasets. We also identify that debiased embeddings do not help in resolving this issue. Finally, we observe that character-based contextualized word representation models such as ELMo results in the least bias across demographics. Our work can shed light on potential biases in automated KB generation due to systematic exclusion of named entities belonging to certain demographics.
Google Research（谷歌研究院）；University of Maryland（马里兰大学）
The goal of question answering (QA) is to answer any question. However, major QA datasets have skewed distributions over gender, profession, and nationality. Despite that skew, model accuracy analysis reveals little evidence that accuracy is lower for people based on gender or nationality; instead, there is more variation on professions (question topic). But QA's lack of representation could itself hide evidence of bias, necessitating QA datasets that better represent global diversity.
Emerging Challenges in Personalized Medicine: Assessing Demographic Effects on Biomedical Question Answering Systems
University of Colorado Boulder（科罗拉多大学博尔德分校）；University of Colorado Denver（科罗拉多大学丹佛分校）；Memorial Healthcare System（纪念医疗系统）；Johannes Gutenberg University Mainz（美因茨约翰内斯·古腾堡大学）
University of California, Irvine（加州大学欧文分校）；Apple（苹果公司）
Retrieval is a core component for open-domain NLP tasks. In open-domain tasks, multiple entities can share a name, making disambiguation an inherent yet under-explored problem. We propose an evaluation benchmark for assessing the entity disambiguation capabilities of these retrievers, which we call Ambiguous Entity Retrieval (AmbER) sets. We define an AmbER set as a collection of entities that share a name along with queries about those entities. By covering the set of entities for polysemous names, AmbER sets act as a challenging test of entity disambiguation. We create AmbER sets for three p…
FIZ Karlsruhe – Leibniz Institute for Information Infrastructure（FIZ卡尔斯鲁厄——莱布尼茨信息基础设施研究所）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
Representation Learning of words and Knowledge Graphs (KG) into low dimensional vector spaces along with its applications to many real-world scenarios have recently gained momentum. In order to make use of multiple KG embeddings for knowledge-driven applications such as question answering, named entity disambiguation, knowledge graph completion, etc., alignment of different KG embedding spaces is necessary. In addition to multilinguality and domain-specific information, different KGs pose the problem of structural differences making the alignment of the KG embeddings more challenging. This pap…
Academic case reports lack diversity: Assessing the presence and diversity of sociodemographic and behavioral factors related to Post COVID-19 Condition
Dalla Lana School of Public Health, University of Toronto（多伦多大学达拉纳公共卫生学院）；York University（约克大学）；Vector Institute（矢量研究院）；Public Health Ontario（安大略省公共卫生局）
Electronic Health Records (EHR) store clinical documentation as base64 encoded attachments in FHIR DocumentReference resources, which makes semantic question answering difficult. Traditional vector database methods often miss nuanced clinical relationships. The Clinical Entity Augmented Retrieval (CLEAR) method, introduced by Lopez et al. 2025, uses entity aware retrieval and achieved improved performance with an F1 score of 0.90 versus 0.86 for embedding based retrieval, while using over 70 percent fewer tokens. We developed a Clinical Notes QA Evaluation Platform to validate CLEAR against ze…
Assessing the Reliability of LLMs Annotations in the Context of Demographic Bias and Model Explanation
Department of Methodology and Statistics, Utrecht University, The Netherlands（方法论与统计学系，乌特列支大学，荷兰）；Department of Information and Computing Sciences, Utrecht University, The Netherlands（信息与计算科学系，乌特列支大学，荷兰）；Queen Mary University of London, London, United Kingdom（伦敦女王玛丽大学，伦敦，英国）
Carnegie Mellon University（卡内基梅隆大学）；Stanford University（斯坦福大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Emory University（埃默里大学）
Stepping from sentence-level to document-level, the research on relation extraction (RE) confronts increasing text length and more complicated entity interactions. Consequently, it is more challenging to encode the key information sources--relevant contexts and entity types. However, existing methods only implicitly learn to model these critical information sources while being trained for RE. As a result, they suffer the problems of ineffective supervision and uninterpretable model predictions. In contrast, we propose to explicitly teach the model to capture relevant contexts and entity types …
Zero- and Few-shot Named Entity Recognition and Text Expansion in Medication Prescriptions using ChatGPT
Chiang Mai University（清迈大学）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Universitätsklinikum Erlangen（埃尔朗根大学医院）；Center of Data Analytics and Knowledge Synthesis for Health Care（医疗保健数据分析与知识综合中心）；Medical University of Graz（格拉茨医科大学）
Bias in the Ear of the Listener: Assessing Sensitivity in Audio Language Models Across Linguistic, Demographic, and Positional Variations
Department of Computer Science and Information Engineering, National Taiwan University, Taiwan（国家台湾大学计算机科学与信息工程系）；Institute of Information Science, Academia Sinica, Taiwan（台湾学术院信息科学研究所）；AI Research Center (AINTU), National Taiwan University, Taiwan（国家台湾大学人工智能研究中心（AINTU））
Cross-Care: Assessing the Healthcare Implications of Pre-training Data on Language Model Bias
Harvard（哈佛大学）；Mass General Brigham（麻省总医院布里格姆医疗系统）；MIT（麻省理工学院）；Universitat Pompeu Fabra（庞培法布拉大学）；Aarhus University（奥胡斯大学）；University of North Carolina（北卡罗来纳大学）；Technical University of Munich（慕尼黑工业大学）；University of Zurich（苏黎世大学）；University of St. Gallen（圣加仑大学）；Maastricht University（马斯特里赫特大学）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）
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
The Voice of Equity: A Systematic Evaluation of Bias Mitigation Techniques for Speech-Based Cognitive Impairment Detection Across Architectures and Demographics
Columbia University Irving Medical Center（哥伦比亚大学欧文医学中心）；Columbia University School of Nursing（哥伦比亚大学护理学院）；Columbia University（哥伦比亚大学）
Federal University of Rio Grande do Sul（南里奥格兰德州联邦大学）
Recently there has been a growing concern about machine bias, where trained statistical models grow to reflect controversial societal asymmetries, such as gender or racial bias. A significant number of AI tools have recently been suggested to be harmfully biased towards some minority, with reports of racist criminal behavior predictors, Iphone X failing to differentiate between two Asian people and Google photos' mistakenly classifying black people as gorillas. Although a systematic study of such biases can be difficult, we believe that automated translation tools can be exploited through gend…
