The Observability Paradox in Artificial Intelligence Ethics: Philosophical Foundations of Explainability and Epistemic Limits in Autonomous Decision Systems
DOI:
https://doi.org/10.64504/big.d.v3i3.773Keywords:
AI ethics; explainable artificial intelligence; epistemic opacity; observability; autonomous decision systems; algorithmic accountability; uncertainty governanceAbstract
The increasing deployment of autonomous artificial intelligence (AI) systems in high-stakes domains has intensified concerns about transparency, accountability, and ethical assurance. Although explainable AI (XAI) methods such as LIME, SHAP, saliency mapping, and counterfactual explanation have improved the interpretability of model outputs, they often provide local or post-hoc explanations rather than verifiable access to the internal epistemic state of complex AI systems. This study introduces the concept of the observability paradox, defined as the structural tension between the normative demand for transparent AI decision-making and the theoretical and practical limits of observing complex computational processes. To address this problem, we propose the Observation–Inference–Validation (OIV) framework, an epistemic governance model that integrates concepts from AI ethics, philosophy of science, and systems theory. We further develop an Observability Index (OI) to assess the extent to which internal system states can be instrumented, accessed, and statistically associated with system outputs. Using a mixed-methods design, the study evaluates the relationship between system complexity, observability, and user-level outcomes, including perceived understanding, trust, and confidence in error detection. The findings indicate that higher system complexity is associated with lower observability, while OIV-informed explanations improve users’ perceived understanding and trust compared with standard post-hoc explanations. However, improved subjective understanding does not necessarily translate into superior error detection, suggesting a distinction between interpretive confidence and actual epistemic reliability. The study contributes to AI ethics by shifting the focus from the pursuit of complete transparency to the structured management of epistemic limits. The proposed framework provides a theoretical and methodological basis for evaluating, communicating, and governing uncertainty in autonomous decision systems.
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