Assignment task:
I. Introduction
Hook: Surge in AI adoption across industries, especially HR.
AI offers productivity gains but raises ethical red flags.
Thesis: While AI enhances efficiency in HR, its use must be guided by ethical standards to prevent bias, preserve privacy, and maintain accountability. Need Assignment Help?
II. Background: What is AI in HR?
Tools: resume screening (e.g., Pymetrics, HireVue), video assessments, predictive analytics (Workday).
Use cases in recruitment, performance evaluation, scheduling.
Source: Workday's head of product on the growing integration of AI (Kazmaier, 2025).
III. Benefits of AI in HR
Increases speed and scalability of hiring (Florentine, 2022).
Data-driven objectivity-AI can reduce human bias (in theory).
Improves candidate experience (chatbots, automation).
Reduces admin workload for HR professionals.
IV. Ethical Concerns in Practice
Bias & Discrimination:
Amazon's AI rejected women candidates (Dastin, 2018).
UW study: names associated with Black males were ranked lower (University of Washington, 2024).
AI tools showed poor results for non-native English speakers (University of Melbourne, 2025).
Lack of Transparency:
Black-box systems: Candidates don't know why they're rejected.
Legal risk: Glitch example from AI job interview (New York Post, 2025).
Privacy & Surveillance:
Algorithmic monitoring in the workplace; EEOC concerns (EEOC, 2023).
Overreach into employee behavior and emotion tracking (Tursunbayeva et al., 2018).
V. Legal & Regulatory Landscape
EEOC's guidelines on algorithmic fairness (2023).
EU AI Act and GDPR protections.
U.S. State AGs stepping in to fill regulatory gaps (Reuters, 2025).
Employers urged to follow mitigation steps to reduce risk (The Employer Report, 2024).
VI. Case Studies
Amazon: Discontinued AI recruiting tool due to gender bias.
HireVue: Faced scrutiny over video-based emotion and tone analysis.
Real-life AI interview gone wrong-glitch causes unfair outcome (New York Post, 2025).
Workday: Promoting explainable AI to improve trust (The Verge, 2025).
VII. Best Practices for Ethical AI Use in HR
Human-in-the-loop for final decisions.
Regular algorithm audits (Raghavan et al., 2020).
Clear communication to candidates and employees.
Build ethical AI teams and review boards.
Promote DEI by actively testing tools for bias.
VIII. Conclusion
AI in HR is not inherently unethical but must be managed with foresight.
Legal frameworks are catching up, but proactive HR leadership is key.
The future of HR must remain both innovative and human-centric.
Resources:
1. Binns, R. (2018). Fairness in machine learning: Lessons from political philosophy. Proceedings of the 2018 Conference on Fairness, Accountability and Transparency.
2. Dastin, J. (2018, October 10). Amazon scrapped 'biased' AI recruiting tool. Reuters.
3. Equal Employment Opportunity Commission (EEOC). (2023). Artificial intelligence and algorithmic fairness in employment decisions.
4. Florentine, S. (2022, June 15). AI in HR: Where automation helps-and where it doesn't. CIO.
5. Kazmaier, G. (2025, May). Workday's AI vision: Trust, transparency, and compliance. The Verge.
6. Raghavan, M., Barocas, S., Kleinberg, J., & Levy, K. (2020). Mitigating bias in algorithmic hiring: Evaluating claims and practices. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency.
7. Tursunbayeva, A., Pagliari, C., & Bunduchi, R. (2018). The ethics of people analytics: Risks, opportunities, and recommendations. Personnel Review, 47(6), 1381-1391.
8. University of Melbourne. (2025, May 14). People interviewed by AI for jobs face discrimination risks. The Guardian.
9. University of Washington. (2024, October 31). AI tools show biases in ranking job applicants' names.
10. New York Post. (2025, May 13). AI job interview spirals into 'dystopian, disturbing' glitch.
11. The Employer Report. (2024, November). The legal playbook for AI in HR: Five practical steps to help mitigate your risk.
12. Reuters. (2025, May 19). State AGs fill the AI regulatory void.
Thesis Statement:
While artificial intelligence promises greater efficiency and objectivity in human resource management, its growing use raises serious ethical and legal concerns-including algorithmic bias, lack of transparency, and data privacy risks-requiring HR professionals to adopt thoughtful, responsible integration practices.