Reading 1 • Think of a Machine Learning algorithm like a recipe, where the ingredients are data, and the dish is a model that can make predictions or inferences about the future.

Reading 2 • Any algorithms that perpetuate the status quo and do not improve social equity for people of color, women, the poor and working class, those with disabilities, and other marginalized groups are biased.

Reading 3 • 1. Historical bias 2. Representation bias 3. Measurement bias 4. Aggregation bias 5. Evaluation bias 6. Deployment bias

Reading 4 • Six points in the development process at which biases are introduced.