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Course Outline

Introduction to Explainable Artificial Intelligence and Ethical Principles

  • The imperative for transparency in artificial intelligence systems
  • Obstacles related to ethics and equitable outcomes in AI deployment
  • Summary of applicable regulatory and ethical standards

Techniques for Explainable AI (XAI) Supporting Ethical Practices

  • Model-agnostic approaches: Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP)
  • Methodologies for identifying bias within AI models
  • Strategies for managing interpretability in complex AI architectures

Transparency and Accountability in Artificial Intelligence

  • Frameworks for designing transparent AI systems for government use
  • Mechanisms to ensure accountability in automated decision-making processes
  • Procedures for auditing AI systems to verify fairness and compliance

Fairness and Bias Mitigation Strategies

  • Identifying and mitigating bias in machine learning models
  • Maintaining equitable outcomes across diverse demographic populations
  • Integrating ethical guidelines into the AI development lifecycle

Regulatory and Ethical Frameworks for Public Sector AI

  • Overview of established AI ethics standards relevant to government operations
  • Analysis of industry-specific AI regulations
  • Alignment of AI systems with the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and other applicable frameworks

Practical Applications of XAI in Ethical AI Deployment

  • Enhancing explainability in healthcare artificial intelligence applications
  • Developing transparent AI solutions within the financial sector
  • Implementing ethical AI practices in law enforcement operations for government agencies

Future Trends in XAI and Ethical AI

  • Emerging directions in explainability research
  • Innovative methods for detecting and addressing bias
  • Prospective opportunities for advancing ethical AI development

Summary and Next Steps

Requirements

  • Foundational understanding of machine learning algorithms
  • Proficiency with artificial intelligence development tools and frameworks
  • Commitment to principles of AI ethics and operational transparency for government initiatives

Audience

  • Professionals specializing in AI ethics
  • Practitioners in artificial intelligence development
  • Data science analysts
 14 Hours

Number of participants


Price per participant

Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.

Upcoming Courses

Explainable AI (XAI) for Ethical AI Development

2026-10-29 09:30
14 hours
Pierre, South Dakota
$ 2703 (Online)
$ 3903 (Classroom)

Explainable AI (XAI) for Ethical AI Development

2026-11-12 09:30
14 hours
Pittsburgh, PA – Regus at Keystone Building
$ 2703 (Online)
$ 3903 (Classroom)

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