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Apply new skills to your role

Instruction and application
In Progress

Applying key takeaways to your role​

  • How does your organisation ensure fairness and transparency in ML model development and deployment?

  • Where do bias risks or fairness gaps most often appear in your workflows or decision processes?

  • What actions could your team take to make fairness auditing and documentation more consistent, collaborative, and accountable across projects?

Action item: Share how you will apply new skills to your role.​

Directions: In this workshop, we covered auditing fairness and bias in ML models using fairness metrics, explainability tools, and mitigation strategies. Create a discussion topic and share how you will apply skills from this workshop in your role. Engage in a discussion by adding commentary to at least one post from a peer.Don't know where to start? Consider the following to guide your response.- Who is responsible for reviewing fairness and documentation practices, and how are those findings communicated to stakeholders?

  • At what stage—data collection, model training, evaluation, or deployment—do fairness issues tend to go unnoticed or unaddressed?
  • What small, practical change (like shared templates, fairness checkpoints, or cross-team reviews) could strengthen your current workflow?