Safety and ethics of AI decisions in personnel management

The introduction of artificial intelligence (AI) into human resource (HR) management in recent years has led to dramatic changes in recruiting, performance evaluation, safety, and talent management.

Prior to the advent of AI, bias and subjectivity were common in HR processes, which negatively impacted the objectivity and fairness of decisions. These problems included discrimination based on gender, age, race, and other characteristics, which reduced the efficiency and fairness of HR processes.

The purpose of this article is to explore how AI can eliminate bias and improve HR functions, and to examine the ethical and safety aspects of using AI solutions.

Current research shows that AI can significantly improve HR processes by making them more efficient and objective. The main issues raised in the literature include algorithm bias, transparency, and data protection.

AI in recruiting

Platforms such as HireVue use AI to evaluate candidates based on their responses and facial expressions, helping to reduce hiring time and improve selection objectivity.

Case Study 1: Unilever deployed the HireVue platform to automate initial candidate screening. Using AI, the system analyzes video interviews by evaluating candidates’ facial expressions, intonation, and keywords. This has reduced hiring time by 70% and improved the objectivity of the selection process.

AI helps eliminate human bias as algorithms can evaluate candidates based on objective data rather than subjective impressions.

Example 2: Pymetrics uses neuropsychological games to assess candidates’ cognitive and emotional qualities. AI algorithms analyze the results of the games and create a candidate profile, which helps avoid resume-related bias.

AI in performance appraisal

Using AI to monitor and analyze employee performance allows for more objective performance reviews and supports career development.

Case Study 3: IBM uses AI to analyze employee performance, providing managers with recommendations for competency development and career growth. Based on these, an objective assessment of each employee’s contribution is made and individualized development plans are created.

AI in learning and development

Personalized training programs based on data analytics can better tailor training to each employee’s needs. AI can recommend courses and materials based on an analysis of employee skills and needs, making educational programs more effective and adaptable.

Case Study 4: Cornerstone OnDemand platform uses AI to personalize training courses, suggesting materials and trainings to employees that match their skill level and professional goals.

AI and employee retention

Applying predictive analytics to predict employee turnover helps HR managers take measures to retain employees. AI can analyze employee engagement and satisfaction data, identify factors affecting turnover, and suggest retention measures.

Case Study 5: Workday uses AI to analyze employee engagement data and predict their likely termination.

Data security and privacy

Measures to protect employee data when using AI include data encryption, regular security audits and strict data access policies. A review of regulatory requirements and standards emphasizes the need for legal compliance and ethical standards to ensure data security.

Case Study 6: SAP SuccessFactors has implemented a multi-layered data security system that includes encryption, access controls, and regular audits. This helps protect sensitive employee data and comply with international standards.

Conclusion

AI solutions in HR offer significant benefits such as reduced bias and increased efficiency. However, it is important to consider the ethical and safety aspects of their use. Examples of the use of AI in recruiting, performance appraisal, employee training and retention demonstrate the potential of these technologies in improving HR processes.

Practical tips for HR managers to implement AI

– Tool selection: Use tools that meet your functional requirements and provide algorithm transparency. For example, HireVue for recruiting and Cornerstone OnDemand for training.

– Data Security: Ensure data security and employee privacy through measures such as encryption, access controls, and regular audits.

– Employee training: Regularly train and inform employees about the use of AI to increase their trust and understanding. This will also help minimize resistance to change and increase engagement.

The balance between automation and preserving the human aspect in HR is critical. I urge HR managers to consciously and responsibly use AI solutions to improve the efficiency and fairness of HR processes. AI adoption should not only help improve business results, but also create a fair and inclusive work environment.

List of references

1. Bankins, S. (2021). Ethical use of artificial intelligence in human resource management: a framework for decision making.

2. Fritts, M. and Cabrera, F. (2021). Artificial intelligence recruitment algorithms and the problem of dehumanization.

3. Yam, J. & Skorburg, J. A. (2021). From human resources to human rights: impact assessments of hiring algorithms.

4. Bogen, M. and Rieke, A. (2018). Help wanted: a study of hiring algorithms, fairness and bias. Rise.

5. Candiotto L. (2023). Ethical use of artificial intelligence in human resource management: a framework for decision making.

6. Fritts M. and Cabrera F. (2021). Artificial intelligence recruitment algorithms and the problem of dehumanization.

7. Yam, J. and Skorburg J. A. (2021). From human resources to human rights: impact assessments of hiring algorithms.

Alena Lipina has 20 years of experience in leading global companies: TEZ TOUR, Coffee Terra, ViaCon Technologies, Deloitte. In each of these companies she has achieved significant success in the field of HR, which is confirmed by numerous cases and examples of successful projects.

The author holds two diplomas of higher legal education (Bachelor and Specialist) from the leading University of the Republic of Belarus and a Cambridge Certificate (FCE).

Alena Lipina is a member of prestigious professional associations and organizations: American Economic Association (USA), Society for Human Resource Management (SHRM, USA), National Human Resources Association, e-Commerce & Digital Marketing Association (EDMA, USA).

In August 2024 Alena Lipina’s scientific article “Unique methodology in HR management as a key to record results” was published in the international scientific journal “Current Research”.