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

Author

Listed:
  • Weinbaum, Noemie

    (PS Expertise, Czech Republic)

  • Kamp, Roy

    (UKG, The Capitol Building, Oldbury, Bracknell, Berkshire RG12 8FZ, UK)

Abstract

High-quality data is crucial for the ethical and effective use of artificial intelligence (AI), particularly in the employment context. This paper critically examines global legal frameworks demanding accuracy, fairness and accountability in AI data usage. Analysing key legislation including the European Union (EU) Artificial Intelligence AI Act (AI Act), US federal actions, Japan’s Act on Protection of Personal Information (APPI) and China’s Personal Information Protection Law (PIPL), it highlights international consensus and geopolitical divergences. Recognising limitations in privacy-enhancing technologies (PETs), the authors propose ‘subjective anonymisation’ — a novel, context-sensitive method protecting individual privacy while preserving data utility. The paper argues for dynamic, risk-based data governance as both a compliance strategy and foundation for ethical AI innovation.

Suggested Citation

  • Weinbaum, Noemie & Kamp, Roy, 2025. "Improving data quality and privacy in AI systems used for employee data processing," Journal of Data Protection & Privacy, Henry Stewart Publications, vol. 8(2), pages 165-182, November.
    • Handle: RePEc:aza:jdpp00:y:2025:v:8:i:2:p:165-182
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    JEL classification:

    • K2 - Law and Economics - - Regulation and Business Law

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