Mitigating Bias in Generative AI: The Role of Explainable AI for Ethical Deployment

Abstract

The rapid development of generative AI has greatly affected different industries such as journalism, healthcare, and finance, among many others. However, this has also created ethical concerns due to biased outputs that result from training data, algorithmic design, and human oversight. Explainable AI indeed helps mitigate these biases by increasing the transparency, interpretability, and accountability of AI decision-making. Techniques like SHAP, LIME, and counterfactual explanations facilitate the detection and correction of bias, ensuring that AI is utilized ethically. A comparison of the precision and accuracy across three studies showed varying results: Alikhademi Kiana et al. (2021) achieved 75% precision and 85% accuracy, Nagisetty Vineel et al. (2020) had 70% precision and 77% accuracy, while Brandt Rafael et al. (2023) recorded 60% precision and 75% accuracy. These discrepancies highlight the ethical challenges that biases in AI present and drive the imperative for better algorithm development, high- quality data, and monitoring at all times. XAI fosters confidence since it enhances trust, facilitates regulatory compliance, and enables the just use of AI in sensitive areas like healthcare and criminal justice by its emphasis on transparency and explainability. Keywords- Explainable AI (XAI), Generative AI, Ethical Deployment, Transparency, SHAP, LIME, Regulatory Compliance, Bias, Precision, Accuracy.