English Language Arts: Questioning Text (TEKS.ELA.9-12.5.B) Practice Test
•17 QuestionsCities increasingly outsource high-stakes decisions—loan approvals, hiring screens, policing predictions—to algorithms praised for neutrality yet trained on historical data soaked in bias. The passage contrasts two rival notions of fairness: procedural fairness, which values consistent rules and transparency about inputs, and outcome fairness, which evaluates whether results reproduce inequities even when procedures look clean. A hiring algorithm that ignores names and addresses may appear procedurally fair; yet if past performance reviews reflect biased mentorship patterns, the model could perpetuate disparities in promotions. The author suggests that debates often smuggle in unstated priorities: is fairness about equal treatment, equal opportunity, or equalized outcomes? Each choice redistributes error—who gets flagged as a risk, who is mistakenly excluded—and reconceives harm as a false positive, a false negative, or a muffled possibility never considered. The piece also questions whether explanations satisfy accountability if they merely reveal how bias traveled, not why it was permitted to travel. It proposes that auditability without a forum for remedy can launder injustice by making it legible but unchallenged. Ultimately, fairness, the author argues, is less a property of the tool than a governance decision about whose losses we are willing to normalize as the cost of efficiency.
Which during-reading question would most effectively deepen understanding of the passage's complex treatment of algorithmic fairness?
Which during-reading question would most effectively deepen understanding of the passage's complex treatment of algorithmic fairness?