Explainable Artificial Intelligence

Explainable artificial intelligence encompasses methods and design approaches aimed at making the decisions, decision-making processes, and behavioral patterns produced by artificial intelligence and machine learning systems understandable and justifiable to humans. The emergence of this field has accelerated significantly due to the widespread adoption of high-accuracy models such as deep learning, whose internal workings remain opaque. The need for explainability arises from the recognition that achieving accurate outcomes alone is insufficient in contexts where decisions carry critical implications for safety, ethics, law, cost, and societal impact.

Scope

The scope of explainable artificial intelligence is not limited to a single type of explanation and is shaped by the specific question the explanation seeks to answer. One group of approaches makes visible which input features a given output is sensitive to and which elements supported the decision. Another group aims to provide deeper insight into how the system works by making its internal representations, computational steps, or learning dynamics comprehensible. These two orientations are often combined in practice, as users require both an understanding of the rationale behind a specific decision and an awareness of the model’s general behavioral boundaries.

Core Concepts

The concepts of interpretability and explainability are closely related but are addressed with different emphases in the literature. Interpretability provides a framework that strengthens human-causal reasoning about the reasons behind a decision and highlights the intuitive justification underlying the output. Explainability, by contrast, seeks to explicitly reveal the internal logic and operational mechanisms that the model uses to generate decisions, going beyond intuitive justifications. The concept of transparency is also considered at two levels in this context. First, the model’s structure is directly understandable. Second, even if the model is opaque, meaningful indicators of the decision process can be generated through external explanation methods.

Explainability serves to build trust and enhance accountability of decisions. When individuals or institutions affected by a decision cannot understand how it was reached, their ability to use the system appropriately, define its limits, and challenge its outcomes is weakened. In regulatory and compliance processes, explainability contributes to verifying whether decisions align with requirements concerning discrimination, privacy, and security. During model development, explanations assist in diagnosing erroneous patterns, data biases, or overfitting issues, thereby accelerating error correction and improvement cycles.

Methodological Approaches

Explainable artificial intelligence methods can be distinguished based on whether explainability is designed into the model from the outset or added post-hoc. Approaches that prioritize explainability during design integrate explanations directly into the model architecture or decision structure, enabling the decision process to be inherently interpretable. Post-hoc approaches, by contrast, generate explanations by observing the behavior of a trained model and typically aim to make the reasons for decisions visible without altering the model itself.

These methods are also classified according to the scope of the explanation. Local explanations answer the question “Why was this specific outcome produced?” for a single instance. Global explanations summarize the model’s overall behavioral patterns, identifying under what conditions similar decisions are made and which input factors the system is systematically sensitive to. In practice, local and global explanations serve different purposes: local explanations provide justification at the moment of decision, while global explanations support evaluation of the model in terms of policy, process, and risk management.

Common Explanation Generation Mechanisms

One group of methods analyzes how outputs change in response to controlled modifications of inputs, thereby inferring which features the decision relies on. The quality of such explanations depends on what changes are considered “meaningful” and how well they represent the actual data distribution. Another group of methods, particularly in neural networks, expresses the sensitivity of the output to the input through gradient-based signals, generating visual or textual importance maps. Architecture-specific methods develop explanation mechanisms tailored to the internal structure of particular model families, and in some cases, the representations used to generate explanations are directly linked to the model’s intermediate layers.

A common form of local explanation involves approximating a complex model’s behavior around a specific example using a simpler, interpretable model. The goal in this framework is to balance interpretability with fidelity to the original model’s behavior. When this balance is not achieved, the explanation may appear convincing but fail to reflect the model’s true decision logic.

Explainability in Transformer-Based Models

The proliferation of transformer-based architectures and large language models has introduced new dimensions to explainability debates. In these systems, explanation extends beyond identifying which inputs were influential to addressing how information is encoded in representations and how decisions are generated through internal computational pathways. Issues such as consistency over long contexts, stability of explanations across varying input formats, and fidelity of explanations to model behavior have become more apparent. Moreover, in text-generation systems, verifying the alignment between the rationale presented to users and the actual signals the model relied on to produce outputs further increases evaluation demands.

Evaluation and Validation

Measuring the quality of explanations is a fundamental challenge in the field. Evaluation approaches include application-based assessments that examine the real-world impact of explanations, human-centered assessments that measure explainability and decision support from the user’s perspective, and formal methods that assess explanation quality without requiring user interaction. A common issue in practice is the claim that explanations are “plausible” merely based on illustrative examples. This approach risks conflating the persuasiveness of an explanation with its fidelity to the model’s actual behavior. Therefore, explanations must be evaluated simultaneously across multiple dimensions, including consistency, stability, fidelity, and scope.

Application Areas and Stakeholder Requirements

Explainable artificial intelligence is adopted in different domains for distinct reasons. In healthcare, during clinical decision support processes, the need for auditability and trust is paramount. In finance and insurance, justifying automated assessments is driven by appeal processes and regulatory requirements. In security and cybersecurity applications, explanations support the reduction of false positives and enable effective incident analysis by guiding analyst intervention. In industrial processes, explanations help link model outputs to operational decisions and determine under which conditions the model operates reliably. This diversity demonstrates that explanations must not be uniform but rather tailored to the goals and risk profiles of stakeholders.

Limitations and Open Problems

Explanation methods often provide only a limited window into the model rather than fully revealing its inner workings. Local explanations, valid only in the vicinity of the specific example, can lead to overgeneralizations about the model’s overall behavior. In input perturbation-based methods, the choice of perturbation space critically determines the explanation. In sensitivity-based methods, regions that appear visually meaningful are not always the true causal factors behind decisions. Moreover, ensuring that explanations maintain consistent reliability across different times and data conditions remains a significant challenge, particularly in environments affected by distributional shift. These issues necessitate viewing explainability not merely as a production step but as a system property requiring continuous validation and monitoring.

Explainable artificial intelligence consists of methods and evaluation frameworks developed to enable high-performance models to be used safely, accountably, and audibly. At its core lies the understanding that explainability is a context-dependent requirement and that explanations must be both comprehensible to humans and verifiable in their representation of model behavior. Therefore, explainability is regarded as an engineering and evaluation domain spanning from model design to application monitoring.