Duration: 75 Minutes

Description

Examines how bias enters AI systems and the Board’s responsibility to prevent discriminatory outcomes.

Topics Covered

  • Algorithmic Bias
  • Data Bias
  • Human Bias
  • Fairness Metrics
  • Discrimination
  • Protected Characteristics
  • Inclusive AI
  • Bias Mitigation

What Will I Learn?

You will learn to:

  • Recognise AI bias.
  • Identify discriminatory outcomes.
  • Evaluate fairness.
  • Recommend mitigation strategies.
  • Promote inclusive AI.

Learning Outcomes

Participants will:

  • Assess bias risks.
  • Apply fairness principles.
  • Reduce discriminatory outcomes.
  • Strengthen governance oversight.

Board Discussion

Should the Board approve an AI model with high commercial value but identified bias?

SkillSim Simulation

Bias Investigation

Investigate complaints that an AI recruitment system unfairly rejects candidates.

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