Tag: trust

Where Does Human Thinking End and AI Begin? An AI Authorship Protocol Aims to Show the Difference

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A split image contrasting human and AI cognitive processes. On the left, a woman writes, surrounded by concepts like "HUMAN INTUITION" and "ORIGINAL THOUGHT." On the right, a man works at a computer, with "AI GENERATION" and "COMPUTATIONAL LOGIC" displayed. A central vertical bar indicates an "AUTHORSHIP PROTOCOL: 60% HUMAN / 40% AI." Image (and typos) generated by Nano Banana.
Decoding authorship: A visual representation of the intricate boundary between human creativity and AI generation, highlighting the need for protocols to delineate their contributions. Image (and typos) generated by Nano Banana.

Source

The Conversation

Summary

Eli Alshanetsky, a philosophy professor at Temple University, warns that as AI-generated writing grows increasingly polished, the link between human reasoning and authorship is at risk of dissolving. To preserve academic and professional integrity, his team is piloting an “AI authorship protocol” that verifies human engagement during the creative process without resorting to surveillance or detection. The system embeds real-time reflective prompts and produces a secure “authorship tag” confirming that work aligns with specified AI-use rules. Alshanetsky argues this approach could serve as a model for ensuring accountability and trust across education, publishing, and professional fields increasingly shaped by AI.

Key Points

  • Advanced AI threatens transparency around human thought in writing and decision-making.
  • A new authorship protocol links student output to authentic reasoning.
  • The system uses adaptive AI prompts and verification tags to confirm engagement.
  • It avoids intrusive monitoring by building AI-use terms into the submission process.
  • The model could strengthen trust in professions dependent on human judgment.

Keywords

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URL

https://theconversation.com/where-does-human-thinking-end-and-ai-begin-an-ai-authorship-protocol-aims-to-show-the-difference-266132

Summary generated by ChatGPT 5

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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.

Keywords

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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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