A review of fairness challenges in natural language processing

Natural Language Processing (NLP) systems are increasingly deployed in high-stakes systems including healthcare, education, recruitment, and law enforcement, yet they have frequently coded and magnified biases that undercut their system’s fairness and trust. This review synthesizes and critically analyzes 121 studies that were published in the year 2014 and up to date that address bias in NLP. We present a novel taxonomy of 18 bias types, such as previously underexplored categories like geographic, disability, and annotation bias, and project them onto the NLP lifecycle, taking data as the starting point to deployment. Four key detection paradigms are examined (statistical, model-probing, benchmark-based, and human-centric), alongside mitigation strategies at the data, model, and post-processing levels. Unlike prior surveys, this study offers a lifecycle-aware framework that connects bias origins, detection methods, and mitigation practices, while focusing on persistent challenges such as intersectionality, generalization, and fairness–performance trade-offs in large language models (LLMs). We argue that achieving fairness in NLP requires not only technical interventions but also socio-technical approaches that integrate community participation, transparency, and governance. Offering a structured, critical, and forward-looking synthesis, this work contributes a roadmap for building transparent, equitable, and socially responsible NLP systems.