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<p>This article introduces a systematic methodology, called <i>red reading</i>, for critical textual analysis that adapts red teaming techniques from computer science and artificial intelligence (AI) into a comprehensive diagnostic protocol, supported by Large Language Models (LLMs), to analyse written documents. As the methodological counterpart of <i>distant writing</i>, which transforms text production through AI-assisted design, <i>red reading</i> expands human analytical capabilities and critical reading by embedding adversarial analysis in a constructive framework powered by LLMs. The approach employs a rigorously tested, diagnostic protocol that stress-tests texts across structural, rhetorical, logical, and stylistic dimensions, while strengthening interpretive understanding. The methodology shows how LLM-supported, textual red teaming can provide a rigorous, critical <i>and</i> constructive approach to textual analysis with broad applications across disciplines. We exemplify the method by developing a fifteen-phase diagnostic prompt for red reading. In the Appendix, we apply this protocol to a case study analysing Turing’s seminal article, <i>Computing Machinery and Intelligence</i>. We finally discuss the challenges of using red reading and provide some suggestions for future research. In particular, we emphasise the importance of calibration methods for LLMs’ critical analysis, along with evidence-based strategies for seamlessly integrating red reading into real-world workflows.</p>

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Red Reading The Text: On How To Red Team Texts Using LLMs

  • 期刊论文

  • 发表日期：

    2025年9月19日

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  • Luciano Floridi,
  • Jessica Morley,
  • Claudio Novelli

摘要

This article introduces a systematic methodology, called red reading, for critical textual analysis that adapts red teaming techniques from computer science and artificial intelligence (AI) into a comprehensive diagnostic protocol, supported by Large Language Models (LLMs), to analyse written documents. As the methodological counterpart of distant writing, which transforms text production through AI-assisted design, red reading expands human analytical capabilities and critical reading by embedding adversarial analysis in a constructive framework powered by LLMs. The approach employs a rigorously tested, diagnostic protocol that stress-tests texts across structural, rhetorical, logical, and stylistic dimensions, while strengthening interpretive understanding. The methodology shows how LLM-supported, textual red teaming can provide a rigorous, critical and constructive approach to textual analysis with broad applications across disciplines. We exemplify the method by developing a fifteen-phase diagnostic prompt for red reading. In the Appendix, we apply this protocol to a case study analysing Turing’s seminal article, Computing Machinery and Intelligence. We finally discuss the challenges of using red reading and provide some suggestions for future research. In particular, we emphasise the importance of calibration methods for LLMs’ critical analysis, along with evidence-based strategies for seamlessly integrating red reading into real-world workflows.