Citation :

Naresh Dulam, 2025. "Enhancing Real-World Robustness in AI: Challenges and Solutions" ESP International Journal of Advancements in Computational Technology (ESP-IJACT)  Volume 3, Issue 1: 154-162.

Abstract :

Artificial Intelligence (AI) is transforming industries, driving innovations in healthcare, finance, transportation, and beyond. Yet, as AI systems transition from controlled environments to real-world applications, their performance often falters. The unpredictable nature of real-world data introduces noise, inconsistencies, & adversarial threats that can undermine AI's reliability. This discrepancy between lab success and real-world deployment highlights the critical need for enhancing AI robustness. One major challenge lies in data quality—models trained on clean, curated datasets struggle when faced with incomplete, biased, or shifting data in production. Additionally, adversarial attacks expose AI's vulnerabilities, where small input data manipulations lead to incorrect outputs. Environmental factors such as lighting changes, sensor errors, or unforeseen scenarios further complicate AI's performance. Addressing these issues requires a multi-faceted approach. Improving data quality through rigorous preprocessing, augmentation, & diverse datasets is essential to build more generalized models. Enhancing model interpretability allows developers to understand how AI makes decisions, identifying potential weaknesses and ensuring accountability. Continuous learning mechanisms, where models adapt and evolve with new data, help maintain relevance and accuracy over time. Furthermore, robust AI architectures and defensive techniques like adversarial training strengthen resilience against attacks. Collaboration between AI researchers & industry practitioners is pivotal in bridging the gap between theoretical advancements and practical implementation. By fostering transparency, ethical AI practices, and iterative improvements, the field can develop systems capable of thriving in the complexities of the real world. Ultimately, the goal is to create AI that excels in ideal conditions and withstands the unpredictable challenges posed by real-world environments, ensuring safer, more reliable, and practical solutions across sectors.

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Keywords :

AI Robustness, Adversarial Resilience, Model Generalization, AI Security, Machine Learning Stability, Algorithm Interpretability, Neural Network Defense, Data Augmentation, Overfitting Prevention, Bias Mitigation, Model Drift, Scalable AI Systems, Continuous Model Improvement, Anomaly Detection, AI Safety, Trustworthy AI, Explainable AI, Uncertainty Quantification, Transfer Learning, Synthetic Data, Model Validation, Edge Cases, Failure Analysis, Domain Adaptation, Robustness Benchmarking, Adversarial Defense Strategies, Noise Tolerance, Gradient Masking.