Ethical and Legal Considerations of AI in HRM

Ethical and Legal Considerations of AI in HRM

Introduction

Artificial Intelligence (AI) in Human Resource Management (HRM) has revolutionised recruitment, employee monitoring, and performance evaluation. However, its rapid adoption raises critical ethical and legal issues. As AI gains more autonomy in decision-making, HR professionals must ensure transparency, data protection, and compliance with existing labour laws (Sachan et al., 2024). In this blog we will explore challenges associated with AI driven HRM systems focusing on data privacy, algorithmic bias, and the legal risks.

Understanding the Ethical Landscape of AI in HRM

Ethical considerations for generative AI

Data Privacy and Security Concerns

AI-powered HR systems are essentially dependent on data majorly obtained from CVs and engagement metrics to keystroke data and communication logs. However, collecting, storing, and analysing such data exposes organisations and businesses to privacy risks.

Europe being one of the regions having most advanced data protection policies, under their General Data Protection Regulation (GDPR), employers must:

  • Obtain clear consent for data collection.
  • Limit data use to specific, lawful purposes.
  • Provide individuals with access of control of their data and explain its usage (European Parliament, 2020).

Therefore, automated profiling, especially in recruitment and performance tracking, must include transparency measures. AI systems should incorporate "privacy by design"
(proactively embedding privacy into the design of systems, processes rather than addressing privacy issues as an afterthought) and "data minimisation" strategies to reduce risk exposure (Du, 2024).

An example of consequences of not following GDPR for AI driven HRM systems is that in 2023 a European firm was fined €250,000 for failing to inform job applicants about AI-based resume screening tools (EPRS, 2020).

Algorithmic Bias and Discrimination in Hiring

AI tools guarantee objectivity, but they often maintain historical bias embedded in training data leading to instances such as discriminatory hiring algorithms which led to draw public scrutiny. The infamous Amazon case is a reminder of a such instance where their AI recruitment system disadvantaged women due to biased historical data inputs (JCEIM, 2024).

Therefore, it is important to understand root causes that produce such biases in AI. Generally, bias in AI arises when:

  • Datasets underrepresent certain demographics.
  • Algorithms prioritise majority-group patterns.
  • Developers lack diversity, failing to anticipate discriminatory patterns (Sachan et al., 2024).

A real-world case study of a biased AI system is where a recruitment firm was investigated for rejecting candidates of ethnic minority backgrounds due to flawed AI logic (Lewis Silkin, 2023).

In order to avoid such occurrences, HR professionals must:

  • Conduct algorithm audits regularly with experts.
  • Involve diverse teams in AI development.
  • Enable appeal mechanisms for rejected applicants (a formal process for individuals who have had their application denied seeking reconsideration or review of that decision).

Legal Implications: Surveillance and Monitoring

AI tools are increasingly used for productivity tracking through analysing emails, webcam usage, or even break times. While productivity monitoring isn't new, the level of access enabled by AI invites questions about workplace surveillance and employee rights.

Legal frameworks such as the GDPR and Article 8 of the European Convention on Human Rights demand:

  • Proportionality in surveillance.
  • Clear communication of monitoring practices.
  • Safeguards against misuse of sensitive data (European Parliament, 2020).

Also, KPMG Report (2024) suggests that employers must justify surveillance with a legitimate interest and should not infringe on employees' dignity and autonomy.

Example: In the UK, several gig economy firms faced legal backlash for implementing AI-driven facial recognition to log worker hours without informing staff adequately (Prettys, 2024).

Ensuring Transparency and Accountability

To build trust, organisations must implement "explainable AI” which involves:

  • Informing recruitment candidates and employees about automated decisions.
  • Providing human interventions in decision-making.
  • Using clear, understandable language to describe AI processes (Du, 2024).

The GDPR requires that individuals impacted by automated decisions have the right to:

  • Obtain meaningful information about the logic involved.
  • Contest decisions and request human review (European Parliament, 2020).

Video Resource: Why Explainable AI Matters in HR

Policy Recommendations and Best Practices

  1. Bias Audits: Periodic reviews of AI systems for fairness.
  2. Ethics Committees: Establish cross-functional boards to oversee AI implementation.
  3. Human Oversight: Retain final decision-making with trained HR professionals.
  4. Employee Consent & Awareness: Conduct training on AI usage in HR systems.
  5. Avoid Excessive Monitoring: Limit to retaining only relevant information
  6. Educated Vendor Selection: Select AI/IT Venders who prioritise data privacy and offer transparency.

Conclusion

While AI holds great potential to revolutionise HRM, its ethical and legal deployment requires deliberate, human-centric governance. By integrating fairness, transparency, and regulatory compliance into AI systems, organisations can use AI responsibly without compromising employee rights.

References

  1. Du, J. (2024) 'Ethical and Legal Challenges of AI in Human Resource Management', Journal of Computing and Electronic Information Management, 13(2), pp. 71-77.
  2. European Parliament (2020) The impact of the General Data Protection Regulation (GDPR) on artificial intelligence. [online] Available at: https://www.europarl.europa.eu/stoa/en/document/EPRS_STU(2020)641530 [Accessed 18 Apr. 2025].
  3. Forbes Human Resources Council (2024) 'Ethics And Compliance Are Vital When Applying AI Tools In HR'. [online] Available at: https://www.forbes.com/sites/forbeshumanresourcescouncil/2024/11/06/ethics-and-compliance-are-vital-when-applying-ai-tools-in-hr/ [Accessed 18 Apr. 2025].
  4. JCEIM (2024) Ethical and Legal Challenges of AI in HRM, Journal of Computing and Electronic Information Management, 13(2), pp. 71-77.
  5. KPMG UK (2024) 'AI and Employment Law: 6 Key Considerations'. [online] Available at: https://kpmg.com/uk/en/insights/ai/ai-and-employment-law-6-key-considerations.html [Accessed 18 Apr. 2025].
  6. Lewis Silkin LLP (2023) 'Discrimination and Bias in AI Recruitment: A Case Study'. [online] Available at: https://www.lewissilkin.com/insights/2023/10/31/discrimination-and-bias-in-ai-recruitment-a-case-study [Accessed 18 Apr. 2025].
  7. Prettys (2024) 'Understanding AI Governance in HR'. [online] Available at: https://prettys.co.uk/articles/understanding-ai-governance-in-hr/ [Accessed 18 Apr. 2025].
  8. Sachan, V.S. et al. (2024) 'The Role Of Artificial Intelligence In HRM: Opportunities, Challenges, And Ethical Considerations', Educational Administration: Theory and Practice, 30(4), pp. 7427-7435.

Comments

  1. Sujith H. Rajapaksha 22 April 2025 at 18:24

    This is a very helpful blog about how AI is used in HR and the risks we need to think about. I liked the real examples, like Amazon’s case, which made it easy to understand. But maybe you can add one example from an Asian or Sri Lankan company too. That would help local readers connect better and see how these issues affect them too.

    ReplyDelete
    Replies
    1. Sajana Vithanawasam26 April 2025 at 23:21

      Thank you for your feedback, and I’m glad you found the examples helpful. You’re right, adding a local example would make the post more relatable for readers in Sri Lanka and Asia. I’ll definitely consider including one to highlight how these issues are affecting companies in the region.

      Delete
  2. Rumesh Dissanayaka29 April 2025 at 07:18

    This article provides a comprehensive overview of the ethical and legal considerations surrounding AI in HR. The emphasis on transparency and fairness is particularly pertinent, especially in light of recent discussions on algorithmic bias and discrimination. It's crucial for organizations to ensure that AI tools are designed and implemented with these principles in mind to foster trust and equity in the workplace. Thank you for shedding light on this important topic.

    ReplyDelete
    Replies
    1. Sajana Vithanawasam30 April 2025 at 08:00

      Thank you for your thoughtful comment! You’ve highlighted a crucial point, transparency and fairness must be at the heart of any AI implementation in HR. As the use of algorithm driven decision-making grows, so does the responsibility to ensure these systems do not reinforce bias or undermine employee trust.

      Delete
  3. Dulanjali Yapa1 May 2025 at 08:59

    Your blog post title describes HRM AI's challenges and responsibilities. You emphasize honesty, fairness, and GDPR compliance when discussing data privacy, algorithmic bias, and workplace surveillance. Companies can use "explainable AI" to ensure workers understand and agree with computer decisions. That will improve your discussion. How can HR professionals use AI while following Sri Lankan and international laws and ethics when laws change frequently?

    ReplyDelete
    Replies
    1. Sajana Vithanawasam3 May 2025 at 08:26

      Thank you for your comment and for highlighting the importance of balancing technological advancement with ethical and legal responsibility. You made an excellent point about the value of explainable AI in building transparency and trust within the workplace. Your question about how HR professionals can stay compliant with evolving local and international laws is timely and relevant, I believe the wayforward should be creating to system to have continuous legal updates, cross-functional collaboration with compliance teams, and integrating adaptive AI governance frameworks can support this.

      Delete
  4. SHAKIR BLOGS3 May 2025 at 17:45

    “Your post clearly articulates the ethical and legal challenges of integrating AI in HRM, emphasizing the need for transparency, fairness, and regulatory alignment in Sri Lanka .

    “How can HR professionals balance innovation with accountability when adopting AI-driven decision-making tools?”

    ReplyDelete
    Replies
    1. Sajana Vithanawasam3 May 2025 at 22:46

      Thank you for your insightful comment. Balancing innovation with accountability starts with embedding ethical guidelines into every stage of AI adoption, from selecting vendors to deploying tools. HR professionals can ensure fairness by regularly auditing AI outcomes for bias, maintaining transparency in how decisions are made, and involving diverse stakeholders in the implementation process. In Sri Lanka’s evolving regulatory context, staying updated on local and international compliance standards is also crutial. All in all, having an approacj with human-in-the-loop can help ensure AI-driven decisions are aligned with organizational values while maintaining employees' trust.

      Delete

Post a Comment