Changes

Harms are typically considered in the context of error rates and/or rates of representation. Differences in error rates across different groups of people induce disproportionate performance of the system, leading to unjust consequences or reduced benefits for certain groups. Differences in rates of representation, even in the absence of differences in rates of error, can lead to unfair distributions of benefits or penalties across groups of people. The types of harm applicable to a particular system are highly dependent upon the intended usage of the system. A flow chart to assist in identification of the metric of fairness most relevant to a system is provided in Figure 3.

Harms are typically considered in the context of error rates and/or rates of representation. Differences in error rates across different groups of people induce disproportionate performance of the system, leading to unjust consequences or reduced benefits for certain groups. Differences in rates of representation, even in the absence of differences in rates of error, can lead to unfair distributions of benefits or penalties across groups of people. The types of harm applicable to a particular system are highly dependent upon the intended usage of the system. A flow chart to assist in identification of the metric of fairness most relevant to a system is provided in Figure 3.

[[File:FairnessTree.png|center|thumb|679x679px|'''Figure 3: Fairness Metric Selection Flow Chart.''' <ref>Aequitas - The Bias Report (dssg.io)</ref>]]

[[File:FairnessTree.png|center|thumb|679x679px|'''Figure 3: Fairness Metric Selection Flow Chart.''' <ref>http://www.datasciencepublicpolicy.org/our-work/tools-guides/aequitas/</ref>]]

Having laid out the potentials for harm in the system, the next step is to identify protected attributes. In the context of bias detection and mitigation, a protected attribute refers to a categorical attribute for which there are concerns of bias across the attribute categories. Common examples of protected attributes are race and gender. Relevant protected attributes for the application of the system must be identified in order to investigate disproportionate impacts across the attribute categories.

Having laid out the potentials for harm in the system, the next step is to identify protected attributes. In the context of bias detection and mitigation, a protected attribute refers to a categorical attribute for which there are concerns of bias across the attribute categories. Common examples of protected attributes are race and gender. Relevant protected attributes for the application of the system must be identified in order to investigate disproportionate impacts across the attribute categories.