Question

Passage A
In recent years, jurisdictions have increasingly integrated algorithmic risk assessment instruments into pretrial bail and sentencing determinations. Proponents laud these actuarial tools as objective instruments capable of neutralizing human bias. However, the proprietary nature of commercial risk algorithms presents a grave challenge to procedural due process. When private developers invoke trade secret protections to shield their source code, training datasets, and feature weightings from public scrutiny, criminal defendants are effectively deprived of the ability to contest the statistical validity of the scores assigned to them.

Meaningful cross-examination and adversarial testing require that a defendant understand why a specific risk score was produced. A probabilistic score generated by an opaque algorithm cannot be meaningfully challenged if the defense cannot determine whether the tool relies on racially correlated proxies or flawed statistical regressions. While standardized risk prediction remains a legitimate institutional ambition, constitutional protections cannot be subordinated to commercial proprietary claims. Unless judicial systems mandate complete open-source transparency or strictly restrict the use of proprietary tools in liberty-depriving decisions, algorithmic justice will undermine the foundational legitimacy of the courts.

Passage B
Critiques of algorithmic risk assessment in criminal sentencing often rest on an idealized benchmark of human decision-making that does not exist in practice. Unassisted judicial determinations are notoriously erratic, subject to cognitive fatigue, subconscious prejudices, and arbitrary disparities between courtrooms. Actuarial tools, by contrast, offer a systematic framework that applies consistent predictive criteria across cases, significantly curbing the vast variance observed in purely intuitive sentencing.

Demands for complete source-code disclosure and open-source mandates represent an impractical response that ignores the operational realities of software development and would stifle private innovation in actuarial modeling. Transparency does not require opening the software's internal proprietary mechanisms to adversarial litigation. Instead, institutional accountability is best achieved through robust external validation and continuous third-party auditing. By measuring an algorithm's empirical performance—specifically assessing calibration, false-positive parity, and predictive accuracy across demographic subgroups—auditors can detect and correct discriminatory outcomes far more reliably than judges can self-correct their own unstated biases. Algorithmic governance should focus on verifiable outputs rather than inspecting internal programming.

Based on the passages, which of the following assertions are supported by BOTH passages? (Select all that apply.)