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Graduate School USA

AI in Government Performance Management

via Graduate School USA

Overview

Explore how artificial intelligence can transform performance management systems within government organizations through this comprehensive course. Learn the fundamental concepts of performance management in the public sector workforce and discover how AI technologies including analytics, automation, and predictive modeling can enhance traditional evaluation processes. Examine practical applications such as AI-driven goal setting, real-time performance monitoring, and predictive analytics for strategic workforce planning. Navigate the critical ethical considerations surrounding AI implementation, including strategies to avoid bias in automated evaluations, ensure transparency, and maintain employee trust while adhering to government ethical standards. Understand the policy landscape through examination of OMB, OPM, and agency-specific guidance on AI and data-driven HR practices, with emphasis on privacy, accountability, and compliance requirements. Gain hands-on experience with practical tools and frameworks including dashboards, visualization tools, AI-driven KPIs and metrics, and data governance protocols. Address potential risks and limitations by identifying challenges and implementing appropriate safeguards for AI-enabled performance management systems. Apply learned concepts to real government work environments by developing strategies to integrate AI practices into existing performance management processes and create actionable implementation plans for AI-enhanced performance management systems.

Syllabus

What is Performance Management?

  • Definition and purpose in the government workforce

AI Fundamentals for Performance Management

  • Types of AI relevant to performance management (analytics, automation, predictive modeling)
  • Key terms and concepts

Opportunities and Use Cases

  • AI for goal setting and alignment
  • Real-time performance monitoring
  • Predictive analytics for workforce planning

Fairness, Transparency, and Ethics in AI-Driven Evaluation

  • Avoiding bias in automated evaluations
  • Ensuring transparency and employee trust
  • Government ethical standards and requirements

Government Policy Context

  • OMB, OPM, and agency-specific guidance on AI and data-driven HR practices
  • Privacy, accountability, and compliance considerations

Practical Tools and Frameworks

  • Dashboards and visualization tools
  • Setting up AI-driven KPIs and metrics
  • Data governance and documentation

Managing Risks and Limitations

  • Identifying challenges and safeguards for AI-enabled performance management

Application to Government Work

  • Integrating AI practices into existing performance management processes

Action Planning

  • Steps to implement or improve AI-enabled performance management

Taught by

Bruce Gay, Steve Pesklo, and Brian Simms

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