AI in the Workplace: Employment & Labor Law Risks, Opportunities, and Compliance

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About Course

This two-hour CLE course delivers a focused and practical examination of how Artificial Intelligence (AI) is reshaping employment and labor law. It addresses real-world legal challenges posed by AI in hiring, performance evaluation, workplace monitoring, and dispute resolution—alongside strategies for compliance and risk management. 

Attendees will explore key legal frameworks, including Title VII, ADA, ADEA, EEOC and OFCCP guidance, GDPR, CCPA, and state/local AI regulations such as NYC Local Law 144. The course will highlight landmark case studies, enforcement trends, and anticipated litigation patterns over the next five years. 

Interactive discussions and a case-based exercise will equip participants to recognize potential bias, privacy violations, and labor rights issues stemming from AI adoption—and to design policies that balance efficiency with fairness and legal compliance. 

Learning Objectives 
By the end of this course, participants will be able to: 

  • Identify major AI applications in workplace compliance, HR decision-making, and dispute resolution. 
  • Evaluate the legal implications of bias detection, performance monitoring, and automated decision-making. 
  • Apply relevant federal, state, and local regulations governing AI in employment. 
  • Implement bias audits, human oversight, and contractual safeguards for AI vendors. 
  • Anticipate and prepare for litigation trends involving AI in the workplace. 

Key Topics 

  • AI in compliance monitoring and workplace investigations 
  • Bias detection in hiring, promotion, and pay equity 
  • Risks in AI-driven termination and performance scoring 
  • Employee privacy, data protection, and consent requirements 
  • Union negotiations and collective bargaining over AI use 
  • Best practices for policy drafting and vendor selection 
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What Will You Learn?

  • How to align AI workplace practices with labor and employment laws
  • How to detect and mitigate algorithmic bias
  • How to manage privacy and data security obligations
  • How to prepare for regulatory inquiries and litigation

Course Content

Welcome and Course Overview
Introduction of presenter(s) and course objectives Outline of session topics and interactive elements

Section 1: AI in the Workplace – Context and Definitions
Evolution from automation to generative AI in HR and workplace management Key terms: AI, machine learning, algorithmic decision-making Why employment law is uniquely impacted by AI technologies

Section 2: Regulatory Landscape for AI in Employment
U.S. federal laws (Title VII, ADA, ADEA, EEOC guidance) State/local laws, including NYC Local Law 144 International considerations: EU AI Act, GDPR

Section 3: AI in Compliance Monitoring and Workplace Investigations
AI tools for policy compliance (safety, attendance, harassment prevention) Predictive analytics in investigations and dispute resolution Legal risks: privacy, employee monitoring, evidentiary challenges

Section 4: Bias Detection in Hiring and Promotion
Detecting systemic discrimination in hiring, promotions, and pay equity Bias audits, mitigation strategies, and statistical analysis Pitfalls of algorithmic bias and proxy discrimination

Section 5: AI in Termination and Performance Management
Productivity scoring and automated termination decisions Risks under ADA, Title VII, and collective bargaining agreements Relevant case law and agency positions

Section 6: Data Privacy, Security, and Employee Rights
GDPR/CCPA implications for employee data in AI systems Consent and transparency obligations Worker rights under labor laws and union contracts

Section 7: Emerging Litigation and Union Negotiations
Early AI-related employment litigation Anticipated claims: discrimination, wrongful termination, defamation Union bargaining over AI use and surveillance

Section 8: Best Practices for Risk Mitigation
Human-in-the-loop oversight models Independent audits and vendor due diligence Employee training and transparent policies

Section 9: Hypothetical Case Study and Group Discussion
Scenario on AI in hiring, monitoring, and bias claims Audience analysis of legal issues and mitigation strategies

Conclusion and Survey
Key takeaways and resources Instructions for completion of CLE survey and attendance requirements

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