Corpus ID: 13633339

The Case for Process Fairness in Learning: Feature Selection for Fair Decision Making

@inproceedings{GrgicHlaca2016TheCF,
  title={The Case for Process Fairness in Learning: Feature Selection for Fair Decision Making},
  author={Nina Grgic-Hlaca and M. B. Zafar and K. Gummadi and Adrian Weller},
  year={2016}
}
Machine learning methods are increasingly being used to inform, or sometimes even directly to make, important decisions about humans. A number of recent works have focussed on the fairness of the outcomes of such decisions, particularly on avoiding decisions that affect users of different sensitive groups (e.g., race, gender) disparately. In this paper, we propose to consider the fairness of the process of decision making. Process fairness can be measured by estimating the degree to which… Expand
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