Practice - Bias and Fairness in Machine Learning: Origins, Detection, and Remediation
Practice Questions
Test your understanding with targeted questions
Define bias in the context of machine learning.
💡 Hint: Consider how decisions might unfairly favor one group over another.
What is demographic parity?
💡 Hint: Think about fairness in terms of distribution.
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Interactive Quizzes
Quick quizzes to reinforce your learning
What is bias in machine learning?
💡 Hint: Recall how bias can influence decisions.
True or False: Representation bias occurs when training data does not accurately reflect the broader population.
💡 Hint: Consider examples of underrepresented demographics.
1 more question available
Challenge Problems
Push your limits with advanced challenges
Given a dataset with a significant number of gender attributes, devise a strategy that mitigates potential biases in predicting job suitability for candidates.
💡 Hint: Consider where bias may permeate the process, from data collection to model evaluation.
Analyze how a healthcare prediction model could reinforce racial biases if trained on historical patients' data. Propose comprehensive bias detection and remediation strategies.
💡 Hint: Link the remediation strategies directly to sources of bias identified in your analysis.
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Reference links
Supplementary resources to enhance your learning experience.