Fairframe: a fairness framework for bias detection and mitigation in news

In the realm of digital information, ensuring the fairness and neutrality of textual content, especially news, is paramount. This paper introduces FairFrame, a novel framework engineered to both detect and mitigate bias in textual data. By harnessing the capabilities of state-of-the-art transformer models, FairFrame excels in identifying bias, surpassing the performance of current benchmarks. Additionally, the framework incorporates an explainable artificial intelligence (XAI) module based on Local Interpretable Model-agnostic Explanations (LIME), which aids in interpreting the rationale behind bias detection, thus fostering greater transparency. Uniquely, FairFrame employs large language models (LLMs) to mitigate detected biases through sophisticated few-shot prompting, marking a pioneering approach in the use of LLMs for bias mitigation. We validate the effectiveness of FairFrame through extensive experimental comparisons with leading fairness methods and an in-depth analysis of its components in diverse settings. The results demonstrate that FairFrame not only improves the detection of bias but also effectively mitigates it, offering a significant advancement in the development of fair artificial intelligence (AI) systems.