自然语言处理与大型语言模型的公平性认证
Fairness Certification for Natural Language Processing and Large Language Models
AI总结：
本文针对自然语言处理和大型语言模型中的公平性问题，采用定性研究方法，通过文献回顾和专家访谈，系统提出了六项可细分为18个子类别的公平性标准，为公平性认证流程的操作化和测试奠定了基础。
摘要：
自然语言处理（NLP）在我们的日常生活中扮演着重要角色，尤其是由于大型语言模型（LLM）的巨大进步。然而，NLP 有许多公平性至关重要的用例，例如作为招聘中的专家系统或教育中基于 LLM 的导师。由于 NLP 基于人类语言，潜在的有害偏见可能会渗透到 NLP 系统中，并产生不公平的结果、歧视少数群体或引发法律问题。因此，为 NLP 方法开发公平性认证非常重要。我们采用定性研究方法来探索 NLP 的公平性认证。具体而言，我们回顾了大量关于算法公平性的文献，并对该领域的广泛专家进行了半结构化专家访谈。我们系统地设计了六项 NLP 公平性标准，这些标准可进一步细分为 18 个子类别。我们的标准为操作化和测试公平性认证流程提供了基础，无论从审计师还是被审计组织的角度来看均是如此。
英文摘要：
Natural Language Processing (NLP) plays an important role in our daily lives, particularly due to the enormous progress of Large Language Models (LLM). However, NLP has many fairness-critical use cases, e.g., as an expert system in recruitment or as an LLM-based tutor in education. Since NLP is based on human language, potentially harmful biases can diffuse into NLP systems and produce unfair results, discriminate against minorities or generate legal issues. Hence, it is important to develop a fairness certification for NLP approaches. We follow a qualitative research approach towards a fairness certification for NLP. In particular, we have reviewed a large body of literature on algorithmic fairness, and we have conducted semi-structured expert interviews with a wide range of experts from that area. We have systematically devised six fairness criteria for NLP, which can be further refined into 18 sub-categories. Our criteria offer a foundation for operationalizing and testing processes to certify fairness, both from the perspective of the auditor and the audited organization.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
