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Adaptive data-centric artificial intelligence for robust decision-making in dynamic environments
Author(s)
Date Issued
2026
Publisher
INTI International University
Journal
ISSN
28055160
Citation
Journal of Data Science, 2026, vol. 1, pp. 1-14.
Description
Open access
Type
Peer Reviewed Journal Article
Abstract
Artificial intelligence systems deployed in real-world environments often experience performance degradation due to dynamic and non-stationary data distributions. Existing approaches predominantly adopt model-centric optimization strategies, assuming static data conditions and relying on frequent retraining to address performance decay. However, such strategies are computationally expensive and operationally impractical in continuous deployment settings. This study addresses the research gap in adaptive data-centric artificial intelligence by proposing an automated framework that prioritizes continuous data adaptation rather than repeated model modification. The proposed framework integrates automated data profiling, concept drift detection, and adaptive data refinement mechanisms to maintain decision-making robustness under evolving data conditions. The methodology evaluates the framework across multiple real-world datasets characterized by temporal variation, noise, and class imbalance, simulating realistic deployment scenarios. Performance is compared against conventional static data pipelines using identical model architectures to isolate the impact of data-centric adaptation. Experimental results demonstrate that the adaptive data-centric framework consistently outperforms static pipelines in terms of predictive accuracy, decision stability, and generalization consistency. In particular, the framework achieves sustained accuracy improvements following detected drift events and significantly reduces performance volatility over time. Moreover, these gains are obtained with substantially lower computational overhead compared to retraining-based strategies. The goal of this research is to establish adaptive data-centric optimization as a scalable and practical paradigm for long-term AI system reliability. The findings provide empirical evidence that intelligent data adaptation can effectively mitigate concept drift and enhance operational resilience in dynamic decision-making environments.
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