Statistical Equity: A Fairness Classification Objective

by   Ninareh Mehrabi, et al.

Machine learning systems have been shown to propagate the societal errors of the past. In light of this, a wealth of research focuses on designing solutions that are "fair." Even with this abundance of work, there is no singular definition of fairness, mainly because fairness is subjective and context dependent. We propose a new fairness definition, motivated by the principle of equity, that considers existing biases in the data and attempts to make equitable decisions that account for these previous historical biases. We formalize our definition of fairness, and motivate it with its appropriate contexts. Next, we operationalize it for equitable classification. We perform multiple automatic and human evaluations to show the effectiveness of our definition and demonstrate its utility for aspects of fairness, such as the feedback loop.



There are no comments yet.


page 1

page 2

page 3

page 4


Subjective fairness: Fairness is in the eye of the beholder

We analyze different notions of fairness in decision making when the und...

Fair and Unbiased Algorithmic Decision Making: Current State and Future Challenges

Machine learning algorithms are now frequently used in sensitive context...

Fairness for Whom? Critically reframing fairness with Nash Welfare Product

Recent studies on disparate impact in machine learning applications have...

Fairness by Explicability and Adversarial SHAP Learning

The ability to understand and trust the fairness of model predictions, p...

Soliciting Stakeholders' Fairness Notions in Child Maltreatment Predictive Systems

Recent work in fair machine learning has proposed dozens of technical de...

Fairness and underspecification in acoustic scene classification: The case for disaggregated evaluations

Underspecification and fairness in machine learning (ML) applications ha...

Statistical inference for individual fairness

As we rely on machine learning (ML) models to make more consequential de...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.