Responsible and Ethical AI

  • Responsible and Ethical AI
  • Turkey - Istanbul

Responsible and Ethical AI

Course Category : Information Technology

A practice-oriented programme focused on establishing governance and ethical frameworks to ensure responsible, fair, and trustworthy AI development and deployment across organisations.
Duration: 5 Days | Level: Advanced.

Starts On

20 - September - 2026

Ends On

24 - September - 2026

Location

Turkey - Istanbul

Language

English

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View Course Introductory PDF

Targeted Audience

  • Executives and Decision-Makers
  • AI and Digital Transformation Leaders
  • Governance and Risk Managers
  • IT Managers
  • Middle Management Leaders
  • Data and AI Teams
  • Compliance and Privacy Officers
  • Legal and Technology Advisors

Targeted Skills

  • Responsible AI Principles
  • AI Governance Framework Design
  • Bias and Fairness Risk Management
  • Transparency and Explainability
  • Regulatory Compliance and Data Protection
  • Ethical AI Lifecycle Integration

Expected Outcomes

  • Develop a clear understanding of responsible and ethical AI.
  • Design effective AI governance frameworks.
  • Identify and mitigate bias and fairness risks.
  • Ensure transparency and explainability of AI systems.
  • Support regulatory compliance and data protection.
  • Build organisational trust in AI solutions.

Training Topics Index

  • Evolution of responsible technology
  • AI vs automation ethics
  • Risks of uncontrolled AI
  • Trust as a strategic factor
  • Organisational ethics

  • Algorithmic bias sources
  • Fairness and non-discrimination
  • Data-driven bias
  • Ethical impact assessments
  • Bias mitigation strategies

  • AI governance frameworks
  • Model transparency
  • Explainability and accountability
  • Governance roles and structures
  • Auditing and documentation

  • Personal data protection
  • Access and security controls
  • Regulatory compliance
  • Legal risk management
  • Incident response

  • Ethics-by-design
  • Responsible for testing and deployment
  • Continuous monitoring
  • Model improvement
  • Sustainable AI practices