Decision Interpretability and Transparency in AI Systems

Modern artificial intelligence systems, particularly those based on deep learning, have achieved high levels of accuracy across many domains. However, this success comes with significant engineering and ethical challenges, as the decision-making processes of these systems remain largely opaque. Knowing what output a system produces is no longer sufficient; understanding how and why a particular decision is made has become increasingly critical.

This situation calls for a reconsideration of artificial intelligence systems not only in terms of technical performance, but also in terms of their societal implications.

Aim and Scope of the Study

This study aims to place the interpretability of decision-making processes at the center of artificial intelligence research, addressing the problem from both technical and normative perspectives. The goal is not merely to improve model performance, but to explore how AI systems can be designed to be understandable, auditable, and accountable to humans.

Black Box Models and the Interpretability Challenge

The first part of the study examines machine learning and deep learning models commonly described as black boxes. Their internal representations, decision boundaries, and feature contributions are analyzed in order to understand why direct interpretation is inherently difficult. This section emphasizes that the interpretability problem is not simply a matter of presentation, but a consequence of model architecture and design choices.

Explainable Artificial Intelligence Approaches

The study then reviews contemporary approaches in Explainable Artificial Intelligence. Model-agnostic and model-specific methods are examined through techniques such as LIME, SHAP, attention mechanisms, and visualization-based explanations.