Tag: Transparency

The Case Against AI Disclosure Statements

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A large tablet displaying an "AI Disclosure Statement" document with a prominent red "X" over it sits on a wooden desk in a courtroom setting. A gavel lies next to the tablet, and a judge's bench with scales of justice is visible in the background. Image (and typos) generated by Nano Banana.
Challenging transparency: A visual argument against mandatory AI disclosure statements, set against the backdrop of legal scrutiny. Image (and typos) generated by Nano Banana.

Source

Inside Higher Ed

Summary

Julie McCown, an associate professor of English at Southern Utah University, argues that mandatory AI disclosure statements in higher education are counterproductive. Initially designed to promote transparency and responsible use, these statements have instead reinforced a culture of guilt, distrust, and surveillance. McCown contends that disclosure requirements stigmatise ethical AI use and inhibit open dialogue between students and educators. Rather than policing AI use, she advocates normalising it within learning environments, rethinking assessment design, and fostering trust. Transparency, she suggests, emerges from safety and shared experimentation, not coercion.

Key Points

  • Mandatory AI disclosure creates a culture of confession and distrust.
  • Research shows disclosure reduces perceived trustworthiness regardless of context.
  • Anti-AI bias drives use underground and suppresses AI literacy.
  • Assignments should focus on quality and integrity of writing, not AI detection.
  • Normalising AI through reflective practice and open discussion builds genuine transparency.

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URL

https://www.insidehighered.com/opinion/views/2025/10/28/case-against-ai-disclosure-statements-opinion

Summary generated by ChatGPT 5

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Why Students Shouldn’t Use AI, Even Though It’s OK for Teachers

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A split image showing a frustrated male student on the left, with text "AI USE FOR STUDENTS: PROHIBITED," and a smiling female teacher on the right, with text "AI USE FOR TEACHERS: ACCEPTED." Both are working on laptops in a contrasting light. Image (and typos) generated by Nano Banana.
The double standard: Exploring why AI use might be acceptable for educators yet detrimental for students’ learning and development. Image (and typos) generated by Nano Banana.

Source

Edutopia

Summary

History and journalism teacher David Cutler argues that while generative AI can meaningfully enhance teachers’ feedback and efficiency, students should not use it unsupervised. Teachers possess the critical judgment to evaluate AI outputs, but students risk bypassing essential cognitive processes and genuine understanding. Cutler likens premature AI use to handing a calculator to someone who hasn’t learned basic arithmetic. He instead promotes structured, transparent use—AI for non-assessed learning or teacher moderation—while continuing to teach critical thinking and writing through in-class work. His stance reflects both ethical caution and pragmatic optimism about AI’s potential to support, not supplant, human learning.

Key Points

  • Teachers can use AI to improve feedback, fairness, and grading efficiency.
  • Students lack the maturity and foundational skills for unsupervised AI use.
  • In-class writing fosters integrity, ownership, and authentic reasoning.
  • Transparent teacher use models responsible AI practice.
  • Slow, deliberate adoption best protects student learning and trust.

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URL

https://www.edutopia.org/article/why-students-should-not-use-ai/

Summary generated by ChatGPT 5

GenAI News

Not Even Generative AI’s Developers Fully Understand How Their Models Work

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In a futuristic lab or control room, a diverse group of frustrated scientists and developers in lab coats are gathered around a table with laptops, gesturing in confusion. Behind them, a large holographic screen prominently displays "GENERATIVE AI MODEL: UNKNOWABLE COMPLEXITY, INTERNAL LOGIC: BLACK BOX" overlaid on a glowing neural network. Numerous red question marks and "ACCESS DENIED" messages highlight their inability to fully comprehend the AI's workings. Image (and typos) generated by Nano Banana.
Groundbreaking research has unveiled a startling truth: even the developers of generative AI models do not fully comprehend the intricate inner workings of their own creations. This image vividly portrays a team of scientists grappling with the “black box” phenomenon of advanced AI, highlighting the profound challenge of understanding systems whose complexity surpasses human intuition and complete analysis. Image (and typos) generated by Nano Banana.

Source

The Irish Times

Summary

John Thornhill examines the paradox at the heart of the artificial intelligence boom: even the developers of generative AI systems cannot fully explain how their models function. Despite hundreds of billions being invested in the race toward artificial general intelligence (AGI), experts remain divided on what AGI means or whether it is achievable. While industry leaders such as OpenAI and Google DeepMind pursue it with near-religious zeal, critics warn of existential risks and call for restraint. At a Royal Society conference, scholars argued for redirecting research toward tangible, transparent goals and prioritising safety over hype in AI’s relentless expansion.

Key Points

  • Massive investment continues despite no shared understanding of AGI’s meaning or feasibility.
  • Industry figures frame AGI as imminent, while most academics consider it unlikely.
  • Experts highlight safety, transparency, and regulation as neglected priorities.
  • Alan Kay and Shannon Vallor urge shifting focus from “intelligence” to demonstrable utility.
  • Thornhill concludes that humanity’s true “superhuman intelligence” remains science itself.

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URL

https://www.irishtimes.com/business/2025/10/10/not-even-generative-ais-developers-fully-understand-how-their-models-work/

Summary generated by ChatGPT 5

GenAI News

We must set the rules for AI use in scientific writing and peer review

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A group of scientists and academics in lab coats are seated around a conference table in a modern meeting room with a city skyline visible through a large window. Above them, a glowing holographic screen displays "GOVERNING AI IN SCIENTIFIC PUBLICATION," with two main columns: "Scientific Writing" and "Peer Review," each listing specific regulations and ethical considerations for AI use, such as authorship, plagiarism checks, and bias detection. Image (and typos) generated by Nano Banana.
As AI’s role in academic research rapidly expands, establishing clear guidelines for its use in scientific writing and peer review has become an urgent imperative. This image depicts a panel of experts discussing these crucial regulations, emphasizing the need to set ethical frameworks to maintain integrity, transparency, and fairness in the scientific publication process. Image (and typos) generated by Nano Banana.

Source

Times Higher Education

Summary

George Chalhoub argues that as AI becomes more entrenched in research and publication, the academic community urgently needs clear, enforceable guidelines for its use in scientific writing and peer review. He cites evidence of undeclared AI involvement in manuscripts and reviews, hidden prompts, and inflated submission volume. To maintain credibility, journals must require authors and reviewers to disclose AI use, forbid AI as a co-author, and ensure human oversight. Chalhoub frames AI as a tool—not a decision-maker—and insists that accountability, transparency, and common standards must guard against erosion of trust in the scientific record.

Key Points

  • Significant prevalence of AI content: e.g. 13.5 % of 2024 abstracts bore signs of LLM use, with some fields reaching 40 %.
  • Up to ~17 % of peer review sentences may already be generated by AI, per studies of review corpora.
  • Some authors embed hidden prompts (e.g. white-text instructions) to influence AI-powered reviewing tools.
  • Core requirements: disclosure of AI use (tools, versions, roles), human responsibility for verification, no listing of AI as author.
  • Journals should adopt policies involving audits, sanctions for misuse, and shared frameworks via organisations like COPE and STM.

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URL

https://www.timeshighereducation.com/opinion/we-must-set-rules-ai-use-scientific-writing-and-peer-review

Summary generated by ChatGPT 5

GenAI News

Explainable AI in education: Fostering human oversight and shared responsibility

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Source

The European Digital Education Hub

Summary

This European Digital Education Hub report explores how explainable artificial intelligence (XAI) can support trustworthy, ethical, and effective AI use in education. XAI is positioned as central to ensuring transparency, fairness, accountability, and human oversight in educational AI systems. The document frames XAI within EU regulations (AI Act, GDPR, Digital Services Act, etc.), highlighting its role in protecting rights while fostering innovation. It stresses that explanations of AI decisions must be understandable, context-sensitive, and actionable for learners, educators, policy-makers, and developers alike.

The report emphasises both the technical and human dimensions of XAI, defining four key concepts: transparency, interpretability, explainability, and understandability. Practical applications include intelligent tutoring systems and AI-driven lesson planning, with case studies showing how different stakeholders perceive risks and benefits. A major theme is capacity-building: educators need new competences to critically assess AI, integrate it responsibly, and communicate its role to students. Ultimately, XAI is not only a technical safeguard but a pedagogical tool that fosters agency, metacognition, and trust.

Key Points

  • XAI enables trust in AI by making systems transparent, interpretable, explainable, and understandable.
  • EU frameworks (AI Act, GDPR) require AI systems in education to meet legal standards of fairness, accountability, and transparency.
  • Education use cases include intelligent tutoring systems and lesson-plan generators, where human oversight remains critical.
  • Stakeholders (educators, learners, developers, policymakers) require tailored explanations at different levels of depth.
  • Teachers need competences in AI literacy, critical thinking, and the ethical use of XAI tools.
  • Explanations should align with pedagogical goals, fostering self-regulated learning and student agency.
  • Risks include bias, opacity of data-driven models, and threats to academic integrity if explanations are weak.
  • Opportunities lie in supporting inclusivity, accessibility, and personalised learning.
  • Collaboration between developers, educators, and authorities is essential to balance innovation with safeguards.
  • XAI in education is about shared responsibility—designing systems where humans remain accountable and learners remain empowered.

Conclusion

The report concludes that explainable AI is a cornerstone for trustworthy AI in education. It bridges technical transparency with human understanding, ensuring compliance with EU laws while empowering educators and learners. By embedding explainability into both AI design and classroom practice, education systems can harness AI’s benefits responsibly, maintaining fairness, accountability, and human agency.

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URL

https://knowledgeinnovation.eu/kic-publication/explainable-ai-in-education-fostering-human-oversight-and-shared-responsibility/

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