ARTICLESocial Data: Biases, Methodological Pitfalls, and Ethical Boundaries

Covers: theory of Algorithmic Bias
Estimated time needed: 20 minutes
Questions this item adddesses:
  • 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

Algorithmic Bias and Fairness

Somaieh NikpoorTotal 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 Convered