Bias Detection and Mitigation in Natural Language Processing Prompting

Abstract

Natural Language Processing (NLP) systems have become integral to numerous applications, ranging from virtual assistants to sentiment analysis tools. However, biases inherent in language data can perpetuate societal inequalities when these systems are deployed without proper scrutiny. Bias detection and mitigation in NLP prompting are critical for ensuring fairness and equity. This paper explores various techniques and methodologies for identifying and addressing biases in NLP prompts, highlighting the importance of mitigating biases to foster inclusive and unbiased communication.