Artificial intelligence (AI) systems are increasingly embedded in critical decision-making processes across healthcare, finance, and education. While these technologies offer unprecedented potential for efficiency, prediction, and personalization, they also pose significant risks when designed or deployed without attention to fairness, transparency, and inclusivity. This manuscript presents a framework for advancing equitable and explainable AI (XAI) in public-impact sectors to ensure that algorithmic systems reinforce, rather than undermine, social equity and institutional trust. Equitable AI refers to models that actively mitigate disparities across demographic groups and ensure just outcomes, while explainable AI emphasizes interpretability and human comprehension of algorithmic logic. We explore the technical foundations and development pipelines required to build such systems—ranging from bias-aware data curation and model transparency methods to post-hoc interpretability tools and human-in-the-loop mechanisms. Through domain-specific applications, we demonstrate how AI can be designed to improve patient triage and care coordination, increase fairness in credit scoring and financial access, and reduce bias in academic assessments and resource distribution. We also assess outcome metrics such as disparity reduction, user comprehension, decision traceability, and stakeholder satisfaction. The findings highlight the need for interdisciplinary collaboration in the development and oversight of AI systems intended for broad societal impact. Equitable and explainable AI is not simply a technological imperative, but a moral and practical necessity for institutions seeking to deliver just, inclusive, and trustworthy services at scale. This manuscript offers actionable guidance for researchers, developers, and decision-makers committed to building algorithms that serve all communities fairly. Keywords: Artificial intelligence (AI), Explainable AI (XAI), Equitable AI.
Adewumi et al. (Tue,) studied this question.