Real-Time Privacy Auditing for AI Systems: Monitoring Bias, Consent, and Data Flows
Keywords:
Real-Time Auditing, AI Privacy, Algorithmic Bias Monitoring, Consent Compliance, Data Provenance, GDPR, AI Ethics, Continuous Compliance, Privacy by Design, Audit Logging.Abstract
The pervasive deployment of AI systems necessitates robust mechanisms to ensurecompliance with ethical principles and regulatory mandates concerning privacy, fairness, anddata governance. Traditional static or post-hoc audits are insufficient for dynamic, continuouslylearning systems operating on real-time data streams
References
Asad, M., Shaukat, S., Javanmardi, E., Nakazato, J., & Tsukada, M. (2023). A comprehensive survey on privacy-preserving techniques in federated recommendation systems. Applied Sciences, 13(10), 6201. https://doi.org/10.3390/app13106201
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Published
2023-07-10
How to Cite
Saumya Dixit, Rahul Rishi Sharma, Jagrati Bhardwaj, Deepankar Dixit. (2023). Real-Time Privacy Auditing for AI Systems: Monitoring Bias, Consent, and Data Flows . Journal of Computational Analysis and Applications (JoCAAA), 31(4), 2625–2640. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/4986
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