Transparent machine reasoning will define next phase of industrial AI

According to the review, global publication trends show a sharp rise in research on explainable AI beginning in 2020, driven by regulatory pressure, high-stakes safety concerns and scandals involving biased or unreliable AI models. A dramatic surge in 2024 reflects international efforts to embed transparency principles in emerging AI governance frameworks, particularly with the rollout of the European Union AI Act.

Global industry is entering a high-stakes phase where artificial intelligence systems must become fully transparent, auditable and aligned with human oversight if the shift from Industry 4.0 to Industry 5.0 is to succeed, according to a new scientific review.

The study, titled Illuminating Industry Evolution: Reframing Artificial Intelligence Through Transparent Machine Reasoning, published in Information, examines how explainable artificial intelligence is evolving from a technical add-on to a foundational requirement for trustworthy and human-centric industrial systems.

Based on 98 peer-reviewed publications indexed in Scopus, the authors find that the rapid adoption of machine learning and predictive automation in manufacturing, logistics, energy, healthcare, and cyber-physical systems has intensified pressure on companies and regulators to understand exactly how automated decisions are made. The review concludes that explainability, often referred to as XAI, has become an essential dimension of industrial governance, technological reliability, and societal legitimacy.

Growing pressure for transparent AI in industry

The researchers report that AI-driven automation across global industries has expanded faster than the mechanisms designed to oversee, interpret and justify those systems. As industrial systems become increasingly autonomous, from predictive maintenance and digital twins to smart robots and decision-support algorithms, their inner workings have become more complex and less interpretable. This opacity is a growing concern for governments, regulators, and industrial organisations that must ensure accuracy, fairness, safety, and compliance.

According to the review, global publication trends show a sharp rise in research on explainable AI beginning in 2020, driven by regulatory pressure, high-stakes safety concerns and scandals involving biased or unreliable AI models. A dramatic surge in 2024 reflects international efforts to embed transparency principles in emerging AI governance frameworks, particularly with the rollout of the European Union AI Act.

The analysis also highlights geographic patterns in research output, with India, the United States and several EU member states emerging as the biggest contributors. These countries have strong industrial digitisation strategies, national AI policies and well-developed research ecosystems. However, the authors caution that the concentration of research in Western and English-language outlets risks downplaying innovations from Latin America, Africa and parts of Asia, underscoring the need for more inclusive global representation.

The review confirms that XAI research is highly interdisciplinary, spanning computer science, engineering, mathematics, decision sciences and management. The field’s rapid expansion reflects industry’s urgent need to understand not only how AI models function, but how they behave under real-world operational constraints.

Rising demands for accountability, fairness and human-machine collaboration

The authors argue that explainability is now at the centre of industrial policymaking because opaque decision-making systems undermine accountability. When AI models determine outcomes in manufacturing quality checks, resource allocation, risk assessment, asset maintenance, safety operations or workforce processes, stakeholders must be able to justify and challenge decisions.

The review identifies a multi-dimensional set of motivations driving explainability. Trustworthiness is central: organisations must rely on decisions they can justify and audit. Intelligibility and comprehensibility are equally critical, ensuring users can grasp how models behave without technical deep dives. Transparency is framed as a cornerstone of accountability, but the study stresses that transparency must be meaningful, not just technical exposure. Providing model parameters or algorithmic summaries is insufficient if the information cannot be understood by operators, managers or regulators.

The review distinguishes several forms of transparency that influence industrial deployment. Simulatability concerns whether a human can mentally predict or reason through model outputs. Decomposability addresses the interpretability of model components such as features and parameters. Algorithmic transparency relates to training processes, optimisation criteria and reproducibility. Each dimension supports auditability and accountability, but each carries limitations. Technical disclosures often benefit experts but exclude front-line users or decision makers.

Another major theme is fairness. The study notes that explainability can help identify biased outcomes across demographic groups or operational contexts, but visibility alone does not fix discrimination. Effective fairness requires organisational mechanisms to act on the evidence. This includes monitoring systems, escalation protocols, retraining procedures and governance oversight. The review categorises fairness into general, formal and perceived dimensions, each influencing how stakeholders judge the ethical acceptability of AI-driven decisions.

Causal reasoning emerges as a critical requirement in industrial environments. Decision makers often need to understand not only correlations but underlying mechanisms, especially for interventions, system optimisation and safety-critical operations. The study notes that counterfactual reasoning is increasingly used to help operators understand what alternative conditions might have altered an outcome. However, challenges such as implausible recommendations, excessive complexity and misleading feature modifications remain obstacles.

The authors emphasise accessibility as a core requirement for future explainability. Industrial environments involve workers with varied expertise, languages and digital literacy. Explanations that are too technical or simplistic can cause confusion, erode trust or misrepresent risks. Well-designed explainability must offer layered and adaptive communication that accommodates diverse users.

How transparent machine reasoning reshapes industry 4.0 and industry 5.0

The study introduces a conceptual model called Transparent Machine Reasoning. This framework links interpretability, ethical accountability and regulatory compliance through a structured, measurable approach. It positions explainability as a strategic capability rather than a technical feature.

The authors argue that Transparent Machine Reasoning is essential for Industry 5.0, which places human-centricity, resilience and sustainability at the core of industrial transformation. Under this paradigm, AI systems must not only perform well but also communicate their reasoning, support human oversight and operate within clear ethical and regulatory boundaries.

The study identifies several technical categories shaping this shift:

Counterfactual reasoning: This technique allows users to explore how different inputs would have changed an outcome. In industrial settings, this supports predictive maintenance, fault diagnosis and process optimisation. However, the authors note recurring problems including unnecessary complexity, unrealistic suggestions and sparse explanations that may overlook key causal variables.

Causal modelling: Causal models produce mechanistic explanations of industrial processes, allowing operators to identify root causes, simulate interventions and evaluate system reliability. The approach is increasingly important for regulatory audits and traceability.

Hybrid neuro-symbolic frameworks: These systems combine the accuracy of neural networks with the clear reasoning of symbolic rule-based systems. They are especially useful in robotics, automated inspection, and distributed manufacturing systems. By blending pattern recognition with explainable logic, they enable safer human-machine collaboration.

Natural-language generation: This technique creates textual explanations tailored to different audiences. In large-scale factories or multinational operations, such explanations help operators understand alarms, quality issues or system advice without technical training. They also align with regulatory expectations for user-facing transparency.

Visual analytics: Dashboards, saliency maps and interactive tools help engineers interpret model behaviour by highlighting patterns or critical features. These tools improve decision accuracy, communication across teams, and audit readiness.

White-box models: Decision trees and additive models remain vital in highly regulated sectors because they provide direct interpretability. Although they may have lower accuracy than deep neural networks, they offer unmatched transparency for compliance and safety-critical operations.

Interpretable learning architectures: Recent AI models integrate explanation directly into their design through attention mechanisms, prototype networks or modular layers. These architectures support real-time interpretability in decentralised or high-precision environments such as assembly lines and industrial robotics.

Across all methods, the review finds that no single technique solves explainability for every industrial context. The best results come from combining approaches while tailoring them to organisational needs, regulatory demands and the level of human-AI interaction involved.

The paper also outlines limits within the review process, including reliance on a single database and exclusion of emerging grey literature, but stresses the need for continued interdisciplinary research. The authors call for future studies focusing on cross-cultural perspectives, empirical validation of Transparent Machine Reasoning, longitudinal analyses and policy-oriented research that can inform international AI governance.