Bias Mitigation — Microsoft Azure AI Fundamentals (AI-900) Practice Questions
Bias mitigation involves identifying and reducing the influence of systematic errors or prejudices in training data or model behavior that lead to unfair outcomes for certain groups. On the AI-900 exam, bias mitigation is discussed within the responsible AI and fairness framework, and candidates should understand that bias can originate from imbalanced training datasets, historical inequities reflected in data, or flawed feature selection. Microsoft's Fairlearn toolkit, accessible through Azure Machine Learning, is the primary tool the exam associates with measuring and mitigating model bias.
Free questions on bias mitigation
How should organizations approach fairness and bias mitigation in AI systems?
Free question · medium · full answer + explanation
More bias mitigation questions in the full bank
- What is the purpose of fairness assessment in Azure ML? Unlock answer & explanation →
- You are designing a loan approval system using machine learning. The model has high accuracy overall but unfairly denies loans to applicants from certain demographics. How should you address this? Unlock answer & explanation →
- Which responsible AI practice helps identify and mitigate bias in machine learning models? Unlock answer & explanation →