Why explainability matters: XAI as the key to building trustworthy AI systems

The study reveals that XAI methodologies are essential for making black-box AI models more transparent. Traditional ML models are often divided into white-box models, which are interpretable but less accurate, and black-box models, which provide superior performance but lack transparency. XAI techniques aim to uncover the reasoning behind black-box model predictions, helping users understand why certain outcomes are generated.

Artificial Intelligence (AI) has become an integral part of industries ranging from healthcare to finance. However, as AI systems grow in complexity, the lack of transparency in decision-making processes has raised concerns over trust, accountability, and fairness. Explainable AI (XAI) aims to bridge this gap by making machine learning (ML) models more interpretable and comprehensible for users. Despite advancements in XAI, challenges remain in developing effective model explanations, evaluation metrics, and user-centered design approaches.

A recent study titled “A Systematic Literature Review of the Latest Advancements in XAI” by Zaid M. Altukhi, Sojen Pradhan, and Nasser Aljohani, published in Technologies (2025), provides a comprehensive analysis of XAI methodologies, frameworks, and evaluation techniques. The study reviews 30 research papers from IEEE Xplore, ACM, and ScienceDirect, categorizing XAI advancements into model developments, evaluation methods, and user-centered system design. This review sheds light on the current state of XAI and its future direction.

Bridging the gap between AI and explainability

The study reveals that XAI methodologies are essential for making black-box AI models more transparent. Traditional ML models are often divided into white-box models, which are interpretable but less accurate, and black-box models, which provide superior performance but lack transparency. XAI techniques aim to uncover the reasoning behind black-box model predictions, helping users understand why certain outcomes are generated.

Among the most widely used XAI methods are SHAP (Shapley Additive Explanations), LIME (Local Interpretable Model-Agnostic Explanations), and contrastive and counterfactual explanations. SHAP provides insight into how different features influence model predictions by quantifying their contributions. LIME explains individual predictions by perturbing input data and analyzing how output changes occur, offering a local interpretability approach. Meanwhile, contrastive and counterfactual explanations help users understand alternative decisions an AI model could have made under different circumstances.

Despite these advancements, XAI still struggles with several unresolved issues. Many of these methods improve AI transparency, but challenges remain in terms of scalability, interpretability consistency, and domain-specific applications. Researchers emphasize that as AI systems become increasingly complex, developing XAI solutions that can maintain accuracy while being understandable to users is critical for ethical AI deployment.

Advancements in XAI evaluation metrics and model development

One of the most pressing issues in XAI research is determining how to evaluate the quality of AI-generated explanations. The study categorizes XAI advancements into three key areas: model developments, evaluation metrics, and user-centered XAI system design.

In the first area, model developments, researchers have worked on improving AI model accuracy while ensuring transparency and interpretability. Some studies propose hybrid models that combine black-box and white-box approaches, attempting to balance interpretability with predictive performance. Others focus on refining deep learning explanations by introducing methods that simplify neural network outputs for easier human interpretation. These advancements seek to create AI systems that are both explainable and practical for real-world applications.

The second area focuses on evaluation metrics and methods, where researchers have developed new ways to measure the effectiveness of AI explanations. Some studies introduce the Mean Degree of Metrics Change (MDMC) to assess how AI models behave under different conditions, while others use SHAP-based evaluation metrics to ensure that AI explanations are internally consistent. However, the study highlights the lack of standardized benchmarks across different XAI methodologies, making it difficult to objectively compare models and establish universally accepted interpretability metrics.

The third area explores user-centered XAI system design, emphasizing the need to align AI explanations with end-user needs. Many XAI models are developed without considering how users - particularly non-technical stakeholders - will interpret them. This has led to the rise of interactive dashboards, visual tools, and user-friendly interfaces designed to bridge the gap between AI complexity and human understanding. Enhancing user experience is crucial in ensuring that AI systems are not only powerful but also accessible to a wider audience.

Challenges and ethical considerations in XAI adoption

Despite notable advancements, XAI continues to face major obstacles to widespread adoption. One of the primary concerns is the trade-off between accuracy and interpretability. White-box models, which prioritize explainability, often sacrifice predictive power, whereas black-box models deliver high accuracy but lack transparency. This challenge forces developers to find a middle ground where AI can maintain interpretability without significantly compromising performance.

Scalability is another pressing issue in XAI research. Many existing explainability techniques struggle to process large-scale AI applications efficiently, particularly in deep learning models that involve vast amounts of data. Ensuring that XAI methods remain computationally efficient while offering meaningful insights remains a key challenge in the field.

Bias and fairness also present significant ethical concerns. Since many AI models are trained on datasets that may not represent all demographic groups equally, their explanations can inadvertently reflect hidden biases. Ensuring fairness in AI decision-making is a core objective of XAI, but bias mitigation remains an ongoing struggle that requires better training data and improved fairness-aware algorithms.

Finally, user trust and transparency remain central to XAI adoption. Many AI users, particularly in high-stakes fields like healthcare and finance, struggle to interpret AI-generated explanations. If users do not fully understand why an AI model made a specific decision, they are less likely to trust its output. The study suggests that enhancing interpretability through clear, context-aware explanations and incorporating user feedback mechanisms can foster greater trust in AI-driven decision-making.

The future of Explainable AI: Where do we go from here?

The study concludes that the next phase of XAI research must focus on human-centered design, adaptive learning, and real-time interpretability. Developing adaptive XAI models that adjust explanations based on user expertise and domain requirements will be essential for creating AI systems that cater to a broader range of users. Additionally, integrating multimodal AI explanations - such as combining text-based, visual, and interactive tools - can enhance interpretability and user engagement.

Standardizing XAI evaluation metrics is another crucial step forward. The lack of universal benchmarks for explainability has made it difficult to assess which methods perform best in different contexts. By establishing industry-wide standards, researchers and developers can create more consistent and reliable XAI solutions.

Moreover, expanding XAI applications in critical industries such as healthcare, finance, and legal AI systems will play a significant role in shaping the future of AI adoption. In these fields, transparency and accountability are non-negotiable, making explainability an essential component of responsible AI deployment.

As AI continues to shape the digital landscape, ensuring that AI models are explainable, ethical, and user-friendly is paramount. With ongoing research and innovation, XAI has the potential to redefine AI usability and foster greater public trust in intelligent systems. The key to future success lies in balancing technical advancements with user needs, ensuring that AI remains both powerful and transparent for all stakeholders.