Fairness Measures

Datasets and software for detecting algorithmic discrimination



Ranking Algorithms

Classification Algorithms



Code (on GitHub) 🔗



Fairness Definitions in Machine Learning

What does it actually mean for an algorithm to be fair? Different researchers have used different notions of algorithmic fairness. We provide here three different ways of classifying fairness.

Group versus Individual Fairness

Group Fairness

It is also refered to as statistical parity. It is a requirement that the protected groups should be treated similarly to the advantaged group or the populations as a whole.

Individual Fairness

It is a requirement that individuals should be treated consistently.

Comparison between Group & Individual Fairness

Group fairness does not consider the individual merits and may result in choosing the less qualified members of a group, whereas individual fairness assumes a similarity metric of the individuals for the classification task at hand that is generally hard to find.

User versus Content Biases

User Bias

This appears when different users receive different content based on user attributes that should be protected, such as gender, race, ethnicity, or religion.

Content Bias

It refers to biases in the information received by any user. Take for example, when some aspect is disproportionately represented in a query result or in news feeds.

Direct versus Indirect Discrimination

Direct Discrimination

This consists of rules or procedures that explicitly mention minority or disadvantaged groups based on sensitive discriminatory attributes related to group membership.

Indirect Discrimination

This consists of rules or procedures that, while not explicitly mentioning discriminatory attributes, intentionally or unintentionally could generate discriminatory decisions. It exists due to the correlation of the non-discriminatory items with the discriminatory ones.


These definitions of fairness can be found in following literature:

Zehlike, Meike, et al. “Fa* ir: A fair top-k ranking algorithm” Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. doi:10.1145/3132847.3132938. bibtex

Pitoura, Evaggelia et al. “On measuring Bias in Online Information.” doi:10.1145/3186549.3186553 . bibtex

Hajian, Sara et al. “Algorithmic Bias: From Discrimination Discovery to Fairness-aware Data Mining.” doi:10.1145/2939672.2945386 .bibtex