Read the following two passages regarding algorithmic risk-assessment tools in the criminal justice system.
Passage A
While it is undeniably true that algorithmic risk-assessment tools used in criminal sentencing reflect the biases of their historical training data, they nonetheless offer a more transparent alternative to human intuition. Implicit bias in human judges is famously difficult to detect and correct. Algorithms, however flawed, rely on explicit variables that can be audited, debated, and eventually refined. Thus, while they are not a perfect cure for systemic prejudice, these computational models provide a necessary foundation for progressively reducing disparity in judicial outcomes.
Passage B
The deployment of algorithmic risk-assessment tools in sentencing operates under a dangerous guise of mathematical objectivity. In reality, these proprietary models are black boxes that digest decades of structurally biased arrest records and output risk scores that judges rarely understand. Far from providing clarity, the complexity and opacity of these algorithms effectively launder historical prejudice, shielding it from legal scrutiny. By outsourcing sentencing to machines, the justice system does not reduce bias; it merely entrenches it deeper out of sight.
Statement: The authors of the passages disagree over whether algorithmic risk-assessment tools are entirely free from the biases found in their historical training data.
Answer: Answer