Disparate impact occurs when a decision process produces disproportionately harmful or beneficial outcomes for different population subgroups, even without explicitly using protected attributes. In algorithmic systems, it is assessed by examining outcome disparities and their relationship to sensitive or legally protected characteristics.
Making decisions about people that impact different population subgroups disproportionately. This usually refers to situations where an algorithmic decision-making process harms or benefits some subgroups more than others. For example, suppose an algorithm that determines a Lilliputian's eligibility for a miniature-home loan is more likely to classify them as "ineligible" if their mailing address contains a certain postal code. If Big-Endian Lilliputians are more likely to have mailing addresses with this postal code than Little-Endian Lilliputians, then this algorithm may result in disparate impact. Contrast with disparate treatment, which focuses on disparities that result when subgroup characteristics are explicit inputs to an algorithmic decision-making process.