Bias Detection & Mitigation
Learn to identify and address algorithmic bias in AI models to ensure fair and equitable outcomes.
Understanding Algorithmic Bias
Welcome to our lesson on Bias Detection & Mitigation! AI models are powerful, but they can sometimes make unfair or discriminatory decisions. This is known as algorithmic bias.
Bias creeps into AI when the data used to train models doesn't accurately represent the real world, or when the model design itself inadvertently amplifies existing societal biases.
Where Does Bias Come From?
Algorithmic bias isn't usually intentional. It typically originates from several sources:
- Historical Bias: Reflects existing societal inequalities in the data.
- Representation Bias: When training data doesn't include diverse enough examples.
- Measurement Bias: Flaws in how data is collected or labeled.
- Algorithmic Bias: Choices made in model design or optimization that favor certain groups.
All lessons in this course
- Bias Detection & Mitigation
- Explainable AI (XAI) Techniques
- Fairness & Accountability
- Human-in-the-Loop Oversight for AI Systems