Fine-Grained Consequentialist Assessment

Understanding this consequentialist approach requires taking into account the following aspects:

Aggregated Benefits: By maximising accuracy, the system is intended to provide reliable guidance to judges and other decision-makers, leading to fewer incorrect assessments and, in theory, a safer society. Accurate predictions mean that individuals who are genuinely high-risk are more likely to be flagged, potentially preventing harm and contributing to social stability.

Consideration of Error Rates: It is noteworthy that a consequentialist approach can still take into account the kinds of mistakes being made and the severity of potential errors (as we discussed in the context of error balance). For instance, a false positive (incorrectly labeling someone as high risk and thus potentially keeping them in prison) is a significant harm—especially if that person’s freedom and reputation are compromised without fault. Thus, while the priority remains on maximising overall accuracy, this approach does not completely disregard error rates. Rather, it seeks to limit severe mistakes where possible, acknowledging that certain types of errors (like unwarranted imprisonment) carry especially high moral and social costs. But the consequentialist approach would consider error rates only insofar as they are relevant to an assessment of the overall aggregation of benefits and harms.