14. The author challenges claims of technological neutrality by emphasizing: computational error (B) market incentives (C) embedded assumptions in design (D) user resistance

17. Which implied concept is central to the passage's argument? Discursive authority (B) Technological autonomy (C) Economic productivity (D) Linguistic efficiency Reading Passage B As artificial intelligence systems become increasingly embedded in education, research, and professional practice, concerns about their reliability have grown more urgent. One major concern is the phenomenon known as "hallucination," in which AI systems generate responses that appear fluent and authoritative but are factually incorrect. These errors arise from the probabilistic nature of language models, which predict plausible sequences of words rather than verify truth. Because such outputs often sound confident, hallucinations can be difficult to detect without careful scrutiny. Closely related is the issue of bias. Trained on large datasets that reflect existing social patterns, AI systems may reproduce or amplify historical inequalities. When AI-generated content is treated as neutral or objective, biased outputs risk being accepted uncritically, particularly in academic or institutional contexts. These risks have already been impacting real-life practices. In several documented cases, AI tools used for legal research have generated fictitious court cases that were subsequently cited by lawyers in formal filings. Although the text appeared professionally written and legally coherent, the sources did not exist. These incidents demonstrate how fluency can mask inaccuracy and how overreliance on AI can shift responsibility away from human users. Technical improvements—such as better training data or refined model architecture—may reduce error rates, but they cannot eliminate these limitations. AI systems lack understanding and epistemic judgment. Responsibility therefore remains with human users. In educational settings especially, responsible AI use requires cultivating critical literacy: the ability to evaluate AI-generated content with informed skepticism. Integrating AI successfully depends not on replacing judgment, but on strengthening it.

14. The author challenges claims of technological neutrality by emphasizing:
computational error

(B) market incentives

(C) embedded assumptions in design

(D) user resistance

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