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THE IMPACT OF GENERATIVE AI ON PEOPLE MANAGEMENT IN COMPLIANCE WITH CYBER LAW

Publications, Research paper
  1. TITLE

THE IMPACT OF GENERATIVE AI ON PEOPLE MANAGEMENT IN COMPLIANCE WITH CYBER LAW

  1. ABSTRACT

The rapid integration of Generative AI (GenAI) into people management processes offers significant benefits in terms of efficiency, cost reduction, and personalized employee experiences. However, its widespread adoption also raises serious concerns about compliance with cyber laws and data protection regulations. This paper examines the intersection between GenAI technologies and compliance with cyber law, particularly focusing on data privacy, discrimination, transparency, and cybersecurity. By analyzing case studies and existing legal frameworks, the paper investigates how organizations can ensure legal and ethical compliance while utilizing GenAI in human resources functions. The research highlights the potential risks and challenges, offering insights and suggestions for best practices in navigating these legal complexities.

  1. KEYWORDS
  1. Generative AI (GenAI)
  1. People Management
  1. Cyber Law Compliance
  1. Data Privacy
  1. Discrimination
  1. AI Ethics
  1. INTRODUCTION

Generative AI (GenAI) is transforming people management processes across various industries, from automating recruitment to improving performance evaluations. However, the use of AI-driven tools in human resources (HR) raises important concerns regarding

compliance with cyber laws, including data privacy regulations, anti-discrimination laws, and cybersecurity requirements. As organizations increasingly deploy AI systems, there is a growing need to understand the impact of these technologies on compliance with relevant laws, both in terms of protecting employee rights and mitigating legal risks.

The role of AI in HR is undeniable, but with its integration comes the potential for misuse, unintended biases, and security breaches that can lead to legal and reputational damage. This research investigates the challenges and legal implications organizations face when adopting GenAI in people management, exploring key legal considerations, potential violations, and providing suggestions for achieving compliance with cyber laws.

  1. RESEARCH METHODOLOGY

This research adopts a qualitative approach, utilizing case study analysis, legal reviews, and a synthesis of literature related to the intersection of AI in people management and compliance with cyber law. The study focuses on identifying key areas of concern by reviewing relevant legal cases and examining real-world examples of AI applications in HR that have led to legal disputes or non-compliance.

  • Data Collection: Data will be gathered through secondary sources such as legal documents, academic articles, reports from HR technology firms, and news reports about legal cases involving AI in HR.
  • Case Study Analysis: Specific cases where AI has led to compliance failures, such as the Amazon AI recruitment tool incident, will be analyzed to draw insights into the real- world implications of AI systems in people management.
  1. REVIEW OF LITERATURE
  1. The Role of AI in People Management

GenAI technologies are used in various people management functions, including recruitment, performance management, and employee monitoring. Scholars argue that AI’s ability to analyze vast amounts of employee data can streamline decision-making and provide more personalized HR services (Binns, 2021). However, the potential for AI to perpetuate biases or make discriminatory decisions has been a recurring concern (O’Neil, 2016).

  1. Cyber Law and AI Compliance

Cyber law, especially data protection laws like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), plays a crucial role in regulating how AI systems handle employee data. GDPR, for instance, imposes strict requirements on data storage, processing, and the need for explicit consent when AI systems use personal data (Mittelstadt et al., 2016). Compliance with these laws is essential to prevent data breaches and ensure employee privacy rights are protected.

  1. Discrimination and Bias in AI Systems

AI systems are not inherently neutral. They can perpetuate biases present in the data on which they are trained. For example, research by Angwin et al. (2016) demonstrated how algorithms used in hiring can be biased toward certain demographic groups. These issues are particularly concerning when AI is used in HR for recruitment and promotion decisions, as they can inadvertently violate discrimination laws such as Title VII of the Civil Rights Act (1964).

  1. Case Studies
  • Amazon’s AI Recruitment Tool (2018): Amazon scrapped an AI recruitment tool that was found to be biased against female candidates due to the historical male-dominated dataset it was trained on (Dastin, 2018). This case highlights the risk of AI reinforcing existing biases.
  • Cambridge Analytica Scandal (2018): The misuse of personal data by AI-powered tools in political campaigns illustrates the significant risks to data privacy when AI systems are not compliant with regulations like GDPR.
  1. METHOD

The research will be carried out using the following method:

  1. Case Study Analysis: A review of notable case studies such as the Amazon recruitment tool and Cambridge Analytica scandal will be conducted to explore real- world instances of non-compliance with cyber laws due to AI integration in people management. The analysis will focus on how these cases could have been avoided and what lessons can be learned.
  2. Legal Review: The study will examine relevant cyber laws, including GDPR, CCPA, and anti-discrimination regulations, to understand how these laws apply to GenAI technologies in people management.
  3. Comparative Analysis: The study will compare the compliance measures of different companies and how they have addressed legal challenges arising from AI in HR, with a particular focus on their strategies to mitigate data privacy risks and prevent algorithmic biases.
  1. SUGGESTIONS

Based on the research findings, the following suggestions can be made to organizations seeking to integrate GenAI into their people management systems while ensuring compliance with cyber laws:

  1. Transparent AI Design: Organizations should design AI systems with transparency and fairness in mind. Regular audits should be conducted to ensure that algorithms do not perpetuate biases, especially in recruitment and performance evaluation.
  2. Data Protection Measures: Compliance with data protection laws such as GDPR should be a top priority. Organizations should implement robust data security measures to protect sensitive employee information from breaches and unauthorized access.
  3. Employee Awareness and Consent: AI systems should be implemented with clear communication to employees regarding how their data will be used. Employees should be provided with easy-to-understand consent forms, and their privacy should be respected in line with legal regulations.
  4. Bias Mitigation Strategies: AI models should be tested and adjusted to prevent biases. Diverse data sets should be used to train AI systems, and algorithms should be regularly audited for fairness.
  5. Legal Compliance Training: HR and IT teams should undergo regular training on legal frameworks like GDPR and CCPA to ensure they are aware of their responsibilities when deploying AI tools in people management.
  1. CONCLUSION

The adoption of Generative AI in people management presents significant opportunities but also introduces complex legal challenges. Compliance with cyber laws such as data privacy regulations, anti-discrimination laws, and cybersecurity standards is crucial for organizations to protect employee rights and avoid legal liabilities. As evidenced by case studies such as the Amazon AI recruitment tool and the Cambridge Analytica scandal, organizations must be proactive in ensuring their AI systems are ethical, transparent, and legally compliant. By adopting best practices, including regular audits, bias mitigation strategies, and clear communication with employees, businesses can harness the full potential of GenAI in people management while safeguarding against legal risks.

REFERENCES
  1. Binns, R. (2021). The role of AI in HR: Implications for people management. HRTech Journal, 34(2), 22-30.
  2. Dastin, J. (2018). Amazon scraps secret AI recruiting tool that showed bias against women. Reuters. Retrieved from https://www.reuters.com
  3. Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 1-21.
  4. O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown Publishing Group.
  5. Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2016). How we analyzed the COMPAS recidivism algorithm. ProPublica. Retrieved from https://www.propublica.org

Author Details:-
Name :- Raj Kumar Kaushal
College Name :- Maharishi University of Information Technology