Ethics and Bias Overview

This section addresses the crucial considerations of fairness, accountability, transparency, and privacy in data science and machine learning.

Key Topics Covered:

• Placeholder Fairness Metrics: Replace this with a link to notes on different ways to measure fairness.
• Placeholder Bias Detection & Mitigation: Replace this with links to notes on identifying and addressing bias in data and models.
• Placeholder Privacy Concerns: Add links to notes on data privacy, GDPR, differential privacy, etc.
• Placeholder Explainability (XAI): Add links to notes on model interpretability techniques.

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Browse the sidebar under the Ethics and Bias heading for a full list of notes within this topic.