Zapisane w:
| Główni autorzy: | , |
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| Format: | Recurso digital |
| Język: | |
| Wydane: |
Zenodo
2025
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| Dostęp online: | https://doi.org/10.5281/zenodo.17824885 |
| Etykiety: |
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Spis treści:
- This paper examines the critical role of data validation in ensuring the robustness and trustworthiness of artificial intelligence (AI) systems. As AI becomes increasingly integrated into various aspects of modern life, the reliability of these systems hinges on the quality of the data they are trained and operated on. Data validation, the process of ensuring data accuracy, completeness, consistency, and adherence to predefined rules, emerges as a fundamental component in mitigating risks associated with flawed or biased data. The paper explores the various techniques and methodologies for data validation, including statistical methods, rule-based systems, and machine learning-based approaches. It further discusses the impact of inadequate data validation on AI system performance, leading to issues such as reduced accuracy, unfairness, and vulnerability to adversarial attacks. Through case studies and examples, the paper highlights the practical implications of data validation in different AI applications, ranging from healthcare and finance to autonomous driving and natural language processing. Finally, the paper proposes a comprehensive framework for incorporating data validation into the AI development lifecycle, emphasizing the need for continuous monitoring and adaptation to evolving data landscapes.