Ensuring Fairness and Equity in AI Systems Using Algorithms for Model Interpretability and Transparency

Abstract

This research paper critically examines SHAP and LIME as advance methodologies to enhance interpretability, precision & fairness of Artificial Intelligent structures. With AI systems being ubiquitous, more and more fundamental life decisions in areas such as healthcare, finance and criminal justice falls under its influential radar, thus intensifies concerns around bias and inequality. The paper delves into how algorithmic frameworks like SHAP and LIME facilitates bias detection and rectify fairness disparities across above pivotal domains. Central to the analysis, is the use of feature importance maps by Machine Learning algorithms, providing clear, data-driven explanation of AI predictions, reinforcing trust and informed decision making. Furthermore, the paper provides an insight by evaluating equitable deployment practices, emphasizing their role in mitigating systematic bias and ensuring just outcomes. The findings highlight the need for interpretability tools to bolster stakeholders trust, guarantees AI accountability and reducing discriminatory effects.