Question

Passage A
The integration of algorithmic risk assessment tools into criminal sentencing marks a crucial step toward fairer judicial outcomes. For centuries, human judges have operated as legal "black boxes," issuing decisions without providing a truly transparent account of their internal reasoning. A judge's sentencing rationale is inherently subjective, vulnerable to implicit biases, cognitive fatigue, and arbitrary personal heuristics that can lead to wildly disparate sentences for similar crimes. While critics correctly point out that current algorithmic models often reproduce historical biases embedded in their training data, this flaw is a temporary engineering challenge rather than an insurmountable fundamental defect. Unlike the opaque cognitive processes of a human judge, an algorithm's underlying code and data inputs can be mathematically audited, explicitly identified, and eventually corrected by developers. By transitioning to machine-assisted sentencing, the justice system can establish a baseline of quantifiable consistency. Furthermore, algorithms possess the capability to process vast, complex datasets regarding recidivism factors that no human mind could meaningfully synthesize during a trial. As these predictive tools are continually refined, tested, and stripped of their legacy biases, they will inevitably provide a more objective, uniform standard for determining sentences. Doing so will mitigate the capricious nature of purely discretionary human judgment and move the legal system closer to the ideal of blind, impartial justice.

Passage B
The contemporary rush to adopt algorithmic risk assessments in criminal sentencing represents a fundamental misunderstanding of what constitutes justice. Proponents of these systems often argue that computational tools will successfully eliminate the well-documented subjective biases and cognitive opacity that frequently plague human judicial decision-making. Indeed, no serious legal scholar can claim that human judges are perfectly objective; their vulnerabilities to prejudice and inconsistency are a matter of public record. However, replacing the human element with statistical algorithms merely trades the pursuit of individual justice for the cold mechanics of group probability. Algorithmic tools evaluate a defendant not as a unique, autonomous individual, but as a composite of demographic and socioeconomic data points, effectively punishing people based on the statistical behavior of others who happen to share their traits. Moreover, because these machine-learning tools are trained on historically biased arrest and conviction records, they inevitably launder deeply rooted systemic prejudices into a false veneer of mathematical objectivity. Even if these data biases could be perfectly scrubbed by programmers—a highly dubious proposition given the structural realities of the legal system—the core philosophical problem remains unresolved. A human judge, despite their inherent flaws and lack of absolute transparency, uniquely possesses the capacity for moral reasoning. Only a human has the ability to weigh the qualitative, highly specific, and often tragic circumstances of a defendant's life. Algorithms optimize entirely for systemic efficiency and risk management, but true justice requires an individualized moral assessment that no machine can provide.

Both authors would be most likely to agree with which one of the following statements regarding algorithmic risk assessments in criminal sentencing?