Understand Responsible AI

As a data scientist, you may train a machine learning model to predict whether someone is able to pay back a loan, or whether a candidate is suitable for a job vacancy. As models are often used when making decisions, it's important that the models are unbiased and transparent.

Whatever you use a model for, you should consider the Responsible Artificial Intelligence (Responsible AI) principles. Depending on the use case, you may focus on specific principles. Nevertheless, it's a best practice to consider all principles to ensure you're addressing any issues the model may have.

Microsoft has listed six Responsible AI principles:

• Fairness: Ensure your model provides equitable outcomes by testing for and mitigating harmful bias across groups.
• Reliability & Safety: Build, test, and monitor your model so it performs consistently, handles edge cases, and prevents unsafe behavior.
• Privacy & Security: Protect user data through minimal collection, strong safeguards, and responsible data-handling practices.
• Inclusiveness: Design and evaluate systems so people of diverse abilities, backgrounds, and contexts can use them effectively.
• Transparency: Communicate clearly how your model works, what data it uses, and how its outputs should be interpreted.
• Accountability: Assign human oversight and responsibility so decisions influenced by AI remain traceable and governed.