AI and Machine Learning Algorithms have seamlessly integrated into our modern lives. They control our access to information and the way that we allocate resources. While these technologies hold the capacity to do tremendous good for humans, they can also exacerbate societal inequities and existing biases. Vivek will be dive into the ML Lifecycle and discuss how biases can seep into the development process at each step. Along the way, he will also share ways to mitigate these risks, with concrete examples of how your organisation can do this in practice when building your data products.

이 과정은 참가자가 다음 내용을 익히도록 설계되었습니다:

• Identify bias risks across the ML lifecycle—from data collection to deployment.
• Apply practical techniques to reduce bias, including better sampling, fairness checks, and post‑deployment monitoring.
• Select and interpret key fairness metrics to balance accuracy and equity.
• Implement simple governance measures such as checklists, model cards, and review gates.
• Create an action plan to manage bias in an upcoming data product.