Welcome to the workshop!
Workshop
In ProgressWelcome to Auditing Fairness and Bias in ML Models
Today's icebreaker:
Fair or Flawed?
Which do you think is harder to detect — performance issues or fairness issues in a model?
What makes one trickier to uncover than the other?
Type your answer in the chat!
Today's agenda:
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Review:
Recap key concepts.
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Practical exercise:
Bias under the microscope.
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Closing:
Wrap up and reflection.
Today's learning objectives:
- Detect algorithmic bias in model outputs using fairness metrics and explainable AI tools.
- Interpret subgroup disparities using fairness visualisations.
- Recommend mitigation strategies and documentation practices for ethical AI deployment.
Workshop slides
Preview or download a copy of the Workshop 2 slides:
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In progress