AI and Compliance- Bias and Fairness

Bias Mitigation

What steps are being taken to identify and mitigate any biases in the AI model’s training data and outputs?

To identify and mitigate biases, the following steps will be taken:

• Data Diversity: Ensuring that the training data includes a diverse range of tenders from various sectors and regions to prevent skewed outputs.

• Regular Updates:Regularly updating the training data to include new and varied tenders, reducing the risk of outdated or biassed information.

• Human Oversight: Incorporating human oversight in the review process to catch and address any biases that automated tools might miss.