Improving data quality and privacy in AI systems used for employee data processing

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Analysis highlights fairness and accountability challenges in AI systems for employee data, suggesting improved privacy measures.

Data quality significantly influences the ethical application of artificial intelligence, particularly in employment contexts.
Global frameworks demand fairness, accountability, and accurate data usage, reinforcing privacy concerns in AI systems.
The proposed subjective anonymisation aims to balance data utility with individual privacy protection in AI practices.
Risk-based governance strategies may enable compliance while fostering ethical AI innovation in dynamic regulatory landscapes.

Weinbaum et al. (2025) studied this question.

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