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English Language Arts: Text Analysis (TEKS.ELA.9-12.6.B) Practice Test

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Q1

Excerpt 1 (Academic Study): Drawing on a multi-jurisdiction dataset of pretrial decisions, this study evaluates algorithmic risk assessment via three fairness criteria: calibration parity (estimated risk matches observed outcomes across groups), error-rate parity (equal false positive/negative rates), and balance for the positive class (equal precision among those who reoffend). Constrained models were trained to satisfy each criterion in turn. Findings indicate that satisfying calibration parity raises cross-group gaps in false positives when base rates differ, whereas imposing error-rate parity distorts probability estimates and degrades ranking fidelity. Cost-sensitive simulations show that policy goals—minimizing incapacitation days, limiting disparate detention, or preserving rank order—map onto different fairness constraints. The paper argues that measurement clarity can reveal conflicts among desiderata but cannot adjudicate the normative priority of incompatible constraints. Excerpt 2 (Philosophical Reflection): To be translated into a score is to stand before an unseen tribunal that speaks in numbers. Even if its arithmetic is sound, a person might justly ask whether procedures that sort lives by proxy truly honor the dignity they claim to serve. The essay probes two tensions: between outcome equity and the right to be judged as an individual; and between institutional efficiency and the moral intelligibility of reasons offered to those constrained by them. Drawing on deontological and phenomenological frames, it maintains that the ethical heart of the matter is not whether estimates are well-calibrated, but whether the terms of being estimated are acceptable. Metrics, on this view, can clarify trade-offs but cannot supply the warrant for whom—and to what ends—we make trade-offs at all.

Which response best compares how the two excerpts treat the question of algorithmic fairness?

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