Possible Trade-Offs with Overall Accuracy: Balancing error rates may reduce the system’s overall predictive accuracy. Critics might argue that prioritising fairness could make the algorithm less reliable overall, potentially compromising public safety if certain high-risk individuals are incorrectly flagged as low-risk. This trade-off could be seen as an ethical tension between individual fairness and collective security.
Challenges in Implementation: Adjusting for error rate balance can be technically complex, requiring sophisticated adjustments to the algorithm. Additionally, the process of balancing error rates may inadvertently introduce other forms of bias, complicating efforts to achieve a truly fair system. This raises the question of whether it is feasible to achieve a perfectly balanced system in a way that doesn’t introduce unintended consequences.
Tension with Efficiency Goals: A Rawlsian framework’s commitment to fairness could be seen as inefficient by those who prioritise maximised accuracy. Balancing error rates may require that the system occasionally over- or under-flag certain groups to maintain equality, a choice that could be perceived as suboptimal for purely operational or security-focused goals.