Stop! In the Name of Flaws: Disentangling Personal Names and Sociodemographic Attributes in NLP
Personal names simultaneously differentiate individuals and categorize them in ways that are important in a given society. While the natural language processing community has thus associated personal names with sociodemographic characteristics in a variety of tasks, researchers have engaged to varying degrees with the established methodological problems in doing so. To guide future work that uses names and sociodemographic characteristics, we provide an overview of relevant research: first, we present an interdisciplinary background on names and naming. We then survey the issues inherent to associating names with sociodemographic attributes, covering problems of validity (e.g., systematic error, construct validity), as well as ethical concerns (e.g., harms, differential impact, cultural insensitivity). Finally, we provide guiding questions along with normative recommendations to avoid validity and ethical pitfalls when dealing with names and sociodemographic characteristics in natural language processing.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Personal Names in Modern Turkey
We analyzed the most common 5000 male and 5000 female Turkish names based on their etymological, morphological, and semantic attributes. The name statistics are based on all Turkish citizens who were alive in 2014 and th…
DiversityPresumed Cultural Identity: How Names Shape LLM Responses
Names are deeply tied to human identity. They can serve as markers of individuality, cultural heritage, and personal history. However, using names as a core indicator of identity can lead to over-simplification of comple…
ChatbotHANA: A HAndwritten NAme Database for Offline Handwritten Text Recognition
Methods for linking individuals across historical data sets, typically in combination with AI based transcription models, are developing rapidly. Probably the single most important identifier for linking is personal name…
Handwritten Text RecognitionTransfer LearningPersonalization of End-to-end Speech Recognition On Mobile Devices For Named Entities
We study the effectiveness of several techniques to personalize end-to-end speech models and improve the recognition of proper names relevant to the user. These techniques differ in the amounts of user effort required to…
speech-recognitionSpeech RecognitionCollaborative Filtering Ensemble for Personalized Name Recommendation
Out of thousands of names to choose from, picking the right one for your child is a daunting task. In this work, our objective is to help parents making an informed decision while choosing a name for their baby. We follo…
Collaborative FilteringRecommendation Systems