Covers: theory of Algorithmic Bias
Estimated time needed to finish: 20 minutes
Questions this item addresses:
  • What are the biases in data?
How to use this item?

Read section 3 to learn about types of biases in data (optional: you can read other sections of the paper as well as the resources recommended on section 10.3)

Author(s) / creator(s) / reference(s)
Alexandra Olteanu, Carlos Castillo, Fernando Diaz, Emre Kiciman
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Recipe
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Algorithmic Bias and Fairness

Collaborators
Total time needed: ~4 hours
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.
Who is this for ?
BEGINNERData Scientist new to AI ethics
Click on each of the following annotated items to see details.
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

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