An Anti-Subordination Approach to Fair Classification

Race-neutral vs race-conscious: Using algorithmic methods to evaluate the reparative potential of housing programs

“…Scholars have outlined how algorithms may accelerate, exacerbate, and extend existing forces of oppression (Benjamin, 2019;D'Ignazio and Klein, 2020;Eubanks, 2018;Umoja Noble, 2018). However, there has been a concerted debate about whether such metrics are reflective of addressing systemic inequalities because they fail to address the root causes of the same (Green, 2022;Keswani and Celis, 2022). Thus, the algorithmic fairness discourse may be limited because in many cases, machine learning algorithms utilize the data at the point of creating the algorithm without considering the historical context in which the input data were generated (So et al, 2022).…”

“…In effect, these methods leverage reparative algorithms to redress algorithmic harms. The use of these methods is grounded in a theoretical shift – instead of conceiving of fairness as the absence of, or expunging of, racial classification from computational systems (race-neutral) we move toward an antisubordination approach, which contends that equal citizenship is not possible under the current social structure and requires the dismantling of racial stratification precisely by examining and attending to its racist effects (race-conscious) (Keswani and Celis, 2022).…”