Ensuring Transparency and Fairness in AI DecisionMaking Processes Influenced by large language Models

This paper tackles the essential problem of assuring openness and justice in AI decision-making processes driven by huge language models. While large language models like GPT-3 have shown impressive capabilities, they have also prompted questions of fairness, transparency, and ethics. The "FairTransLing" approach contains features to improve interpretability, bias reduction, fairness evaluation, transparency, regulatory compliance, and real-time monitoring, and is proposed as a solution to these problems. The state-of-the-art and time-tested approaches "Interpret ML," "Fair ML," "BERT Viz," "FairGAN," "LIME," and "AIF360" were tested against FairTransLing in great detail. We compared these strategies based on six criteria: improved interpretability, reduced bias, increased fairness, improved transparency and fairness, more regulatory compliance, and continuous monitoring. The suggested strategy performed better than the conventional approaches in every respect. Our results demonstrate that FairTransLing provides a complete approach to increase interpretability, eliminate biases, and measure fairness in AI decision-making. Ethical problems and possible biases are reduced because of its emphasis on openness, regulatory compliance, and real-time monitoring. This study is a major advance toward the transparent and equitable implementation of massive language models used in AI systems.