Fairness Constraints: Mechanisms for Fair Classification

  title={Fairness Constraints: Mechanisms for Fair Classification},
  author={Muhammad Bilal Zafar and Isabel Valera and Manuel Gomez-Rodriguez and Krishna P. Gummadi},
Algorithmic decision making systems are ubiquitous across a wide variety of online as well as offline services. These systems rely on complex learning methods and vast amounts of data to optimize the service functionality, satisfaction of the end user and profitability. However, there is a growing concern that these automated decisions can lead, even in the absence of intent, to a lack of fairness, i.e., their outcomes can disproportionately hurt (or, benefit) particular groups of people… CONTINUE READING
Highly Cited
This paper has 97 citations. REVIEW CITATIONS
Related Discussions
This paper has been referenced on Twitter 23 times. VIEW TWEETS

From This Paper

Figures, tables, and topics from this paper.


Publications citing this paper.
Showing 1-10 of 72 extracted citations

97 Citations

Citations per Year
Semantic Scholar estimates that this publication has 97 citations based on the available data.

See our FAQ for additional information.


Publications referenced by this paper.
Showing 1-10 of 20 references

Big Data’s Disparate Impact

S. Barocas, A. D. Selbst
California Law Review, • 2016
View 4 Excerpts
Highly Influenced

Three naive Bayes approaches for discrimination-free classification

Data Mining and Knowledge Discovery • 2010
View 7 Excerpts
Highly Influenced

Adverse Impact and Test Validation: A Practitioner’s Guide to Valid and Defensible Employment Testing

D. Biddle
View 5 Excerpts
Highly Influenced

Classifying without discriminating

2009 2nd International Conference on Computer, Control and Communication • 2009
View 2 Excerpts
Highly Influenced

Similar Papers

Loading similar papers…