Covers: theory of Algorithmic Bias and Fairness
Estimated time needed to finish: 30 minutes
Questions this item addresses:
- What are specific algorithmic biases?
How to use this item?
Watch the whole video (optional: there are other related resources from Rachel Thomas that you can read)
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Author(s) / creator(s) / reference(s)
Rachel Thomas
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Algorithmic Bias and Fairness
Collaborators
- 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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