Recipe
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Algorithmic Bias and Fairness

Collaborators
Reviewers
Total time needed: ~4 hours
Learning Objectives
This list will get you started with concepts and practices in algorithmic bias and fairness.
Potential Use Cases
Assist in understanding some specific causes of algorithmic biases and potentially avert harmful impacts.
Target Audience
BEGINNERData Scientist new to AI ethics
Go through the following annotated items in order:
VIDEO 1. Getting Specific About Algorithmic Bias
  • What are specific algorithmic biases?
30 minutes
ARTICLE 2. Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries
  • What are the biases in data?
20 minutes
VIDEO 3. 21 Fairness Definition and Their Politics
  • What are definition of fairness?
60 minutes
ARTICLE 4. Dealing with Bias and Fairness in Building Data Science/ML/AI Systems
  • How to implement fairness definition?
90 minutes

Concepts Covered