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Machine Learning

Recent Emerging Techniques in Explainable Artificial Intelligence to Enhance the Interpretable and Understanding of AI Models for Human

Daniel J. Mathew(University of Nigeria), Deborah Ebem(University of Nigeria), Anayo Chukwu Ikegwu(Veritas University), Pamela Eberechukwu Ukeoma(University of Nigeria), Ngozi Fidelia Dibiaezue(University of Nigeria)
February 7, 2025Neural Processing Letters132 citations
PDFDOI

132

Citations

4

Influential Citations

Neural Processing Letters

Venue

2025

Year

Abstract

Recent advancements in Explainable Artificial Intelligence (XAI) aim to bridge the gap between complex artificial intelligence (AI) models and human understanding, fostering trust and usability in AI systems. However, challenges persist in comprehensively interpreting these models, hindering their widespread adoption. This study addresses these challenges by exploring recently emerging techniques in XAI. The primary problem addressed is the lack of transparency and interpretability in AI models to humanity for institution-wide use, which undermines user trust and inhibits their integration into critical decision-making processes. Through an in-depth review, this study identifies the objectives of enhancing the interpretability of AI models and improving human understanding of their decision-making processes. Various methodological approaches, including post-hoc explanations, model transparency methods, and interactive visualization techniques, are investigated to elucidate AI model behaviours. We further present techniques and methods to make AI models more interpretable and understandable to humans including their strengths and weaknesses to demonstrate promising advancements in model interpretability, facilitating better comprehension of complex AI systems by humans. In addition, we provide the application of XAI in local use cases. Challenges, solutions, and open research directions were highlighted to clarify these compelling XAI utilization challenges. The implications of this research are profound, as enhanced interpretability fosters trust in AI systems across diverse applications, from healthcare to finance. By empowering users to understand and scrutinize AI decisions, these techniques pave the way for more responsible and accountable AI deployment.

Analysis

Why This Paper Matters

As AI models grow in complexity, their opacity undermines trust and hinders adoption in high-stakes domains. This paper addresses a critical bottleneck: the gap between model performance and human understanding. By systematically reviewing recent XAI techniques, it provides a timely resource for practitioners seeking to make AI systems more transparent and accountable. The focus on institutional use cases (healthcare, finance) underscores the practical urgency of interpretability for regulatory compliance and user acceptance.

Technical Contributions

The paper categorizes XAI approaches into three main streams:

  • Post-hoc explanations: Methods like LIME, SHAP, and saliency maps that explain decisions after model training.
  • Model transparency methods: Inherently interpretable models (e.g., linear models, decision trees) and attention mechanisms.
  • Interactive visualization techniques: Tools that allow users to explore model behavior dynamically.

For each category, the authors detail strengths (e.g., flexibility of post-hoc methods) and weaknesses (e.g., instability of explanations). They also discuss local use case applications, bridging theory and practice.

Results

The review synthesizes findings from 132 cited works, concluding that emerging XAI techniques significantly improve human understanding of AI decisions. However, no quantitative metrics (e.g., fidelity, comprehensibility scores) are provided, as the paper is a qualitative survey. The main result is a structured taxonomy of methods and their trade-offs.

Significance

This paper serves as a comprehensive guide for AI practitioners aiming to deploy interpretable systems. By highlighting open challenges (e.g., evaluation metrics, user studies), it sets a research agenda for the field. The emphasis on trust and accountability aligns with global regulatory trends (e.g., EU AI Act), making the work highly relevant for both academia and industry.

explainable-aiinterpretabilitypost-hoc-explanationsmodel-transparencyinteractive-visualizationtrust-in-airesponsible-ai

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