Explainable Artificial Intelligence (XAI) Models for Transparent Decision-Making Systems

Abstract

This paper reflects on the role of explainable artificial intelligence (XAI) towards constructing interpretable models that achieve ethically-sound decisions in areas such as health, finance, and public sector decision-making. Interpretable models, such as logistic regression or shallow decision trees, are transparent, accountable, and trustworthy - contrasting with a black-box opaque algorithm. We created interpretable classifiers using an Adult-like synthetic dataset with socio-economic decision-making. We examined the classifiers-key predictions with XAI tools. The results showed that interpretable models correctly sieved between accuracy versus interpretability, while identifying decision drivers, and potential biases. More, fairness metrics indicated evidence of systematic disparities, emphasizing the need to combine XAI with ethical auditing frameworks.