Fairness Certification for Natural Language Processing and Large Language Models
Vincent Freiberger, Erik Buchmann
TL;DR
Fairness certification for NLP and LLMs is needed to prevent biased outcomes in high-stakes and everyday NLP applications. The authors use a qualitative method based on literature review and 14 semi-structured expert interviews to derive a hierarchical framework consisting of six main criteria and 18 sub-criteria for auditing NLP fairness. The framework covers governance, process design, data handling, project planning, modeling and evaluation, and operational practices, emphasizing data quality and rigorous testing. They discuss practical implications, limitations, and future research directions, including use-case dependent fairness definitions and the potential for mandatory certification to spur industry-wide adoption.
Abstract
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.