On fairness and randomness

On fairness and randomness

We investigate the relation between the behavior of non-deterministic systems under fairness constraints, and the behavior of probabilistic systems. To this end, first a framework based on computable stopping strategies is developed that provides a common foundation for describing both fair and probabilistic behavior. On the basis of stopping strategies it is then shown that fair behavior corre...

Fairness, Computable Fairness, and Randomness

On Fairness, Diversity and Randomness in Algorithmic Decision Making

Consider a binary decision making process where a single machine learning classifier replaces a multitude of humans. We raise questions about the resulting loss of diversity in the decision making process. We study the potential benefits of using random classifier ensembles instead of a single classifier in the context of fairness-aware learning and demonstrate various attractive properties: (i...

the relationship between eq, iq and test format: a study on test fairness

the major aim of this study was to investigate the relationship between iq, eq and test format in the light of test fairness considerations. this study took this relationship into account to see if people with different eq and iq performed differently on different test formats. to this end, 90 advanced learners of english form college of ferdowsi university of mashhad were chosen. they were ask...

On Fairness and Calibration

The machine learning community has become increasingly concerned with thepotential for bias and discrimination in predictive models. This has motivated agrowing line of work on what it means for a classification procedure to be “fair.”In this paper, we investigate the tension between minimizing error disparity acrossdifferent population groups while maintaining calibrated probab...