Dr. Amasiya

In my first post, I described the moment I realized we had replaced ethics with regulations. That we have reduced the AI debate to “is it allowed or not?” In the second, I shared the results of a classroom experiment that challenged the assumption that humans are more accurate than AI in literature reviews. The data showed humans are simply more trusted, not necessarily more accurate.

But both posts raised a question I kept getting in my inbox: What are the actual rules? What do institutions, journals, and workplaces actually say about AI use — and do they even agree with each other?

Fair question. Before we can test whether specific AI tools meet the standards for responsible use (which is what this blog exists to do) we need to know what those standards are. We need a baseline. And to build that baseline, I spent the past several weeks doing something I wish someone had done a long time ago: reading the actual policies.

Not summaries. Not opinion pieces about the policies. The policies themselves from universities, journals, publishers, professional bodies, funding agencies, consulting firms, law firms, newsrooms, creative industries, and governments. Over a hundred of them, spanning from late 2022 to today.

What I found was both reassuring and deeply frustrating.

The one thing everyone agrees on

Let me start with the good news. Across every sector I examined (academia, publishing, industry, law, government), there is exactly one principle that enjoys universal, uncontested agreement and is it that AI cannot be an author.

Not a single major university, journal, publisher, professional association, or ethics body disagrees. The Committee on Publication Ethics (COPE) was among the first to formalize this, stating that AI tools “cannot meet the requirements for authorship as they cannot take responsibility for the submitted work.” The International Committee of Medical Journal Editors (ICMJE) expanded on this in their January 2024 update, noting that chatbots “should not be listed as authors because they cannot be responsible for the accuracy, integrity, and originality of the work.” Elsevier, Springer Nature, Wiley, Taylor and Francis, SAGE, IEEE, ACM — every major publisher echoes this position.

The logic is straightforward: authorship implies accountability. If an AI fabricates a citation (as ChatGPT did in 12% of cases in my own experiment) who gets sanctioned? The model cannot be hauled before an ethics committee, cannot have a paper retracted from its CV, cannot lose tenure. The human can. So the human must be the author.

This is one of those rare moments in academic governance where there is no meaningful dissent. Enjoy it, because it is the last one.

From panic to pragmatism in three years

To understand why everything beyond that single principle is contested, it helps to trace how we got here. The trajectory from ChatGPT’s launch in November 2022 to today follows a remarkably clear arc — and it is faster than almost anything I have seen in institutional policymaking.

The panic phase (late 2022 to early 2023). ChatGPT reached 100 million users in two months. New York City’s Department of Education banned it on all school devices. Sciences Po in Paris threatened expulsion. Multiple Australian states blocked it on school networks. Italy became the first Western country to ban ChatGPT outright (though only for about two weeks). Science magazine banned all AI-generated text entirely. The editor-in-chief, Holden Thorp, explained the reasoning with admirable honesty: “It’s a lot easier to loosen our criteria than it is to tighten them.”

I remember this phase well. It was the period I described in my first post — jumping from “that should definitely be treated as academic dishonesty” to “there is actually no generally accepted way to determine if someone has or has not used AI.”

The cautious exploration phase (mid-2023). NYC reversed its ban in May. The UK’s Russell Group, representing 24 leading universities, published five guiding principles explicitly rejecting blanket bans. Their CEO stated: “The transformative opportunity provided by AI is huge and our universities are determined to grasp it.” This was the phase when most institutions stopped asking “should we allow it?” and started asking “how do we manage it?”

It was also the phase of the Mata v. Avianca ruling — the case that changed everything in the legal profession. Attorneys Steven Schwartz and Peter LoDuca used ChatGPT to draft legal briefs that contained entirely fabricated cases with fake citations. They were fined $5,000. The judge noted there is “nothing inherently improper about using a reliable artificial intelligence tool for assistance,” but the attorneys had failed to verify anything. By 2025, researchers documented over 300 cases of AI hallucinations in court filings — escalating from roughly two per week to two or three per day.

The framework development phase (late 2023 to 2024). Biden’s AI Executive Order (October 2023), the EU AI Act political agreement, the Bletchley Declaration — this is when institutions moved from ad hoc responses to structured governance. The American Bar Association issued its first formal ethics opinion on generative AI. NIST published its generative AI risk profile. Universities moved from instructor-level discretion to institutional frameworks.

The nuanced integration phase (2025 to present). Trump revoked Biden’s executive order on his first day in office. The EU began enforcing AI Act prohibitions. NIH dramatically tightened restrictions on AI in grant applications. Meanwhile, the University of Michigan rolled out a full suite of AI tools for all students and staff, the University of Sydney declared that AI cannot be prohibited in open assessments, and 31 US states enacted AI legislation. The landscape became simultaneously more sophisticated and more fragmented.

In barely three years, we went from “ban everything” to a global governance architecture involving every major institution. The speed is remarkable. The coherence is not.

What universities actually say (and where they contradict each other)

But the picture gets messy — and, frankly, this is where the “replaced ethics with regulations” problem I described in my first post becomes starkly visible.

I examined AI policies from universities across the US, UK, Canada, and Australia. The variation is extraordinary — not just between countries, but between universities in the same city, and sometimes between departments in the same university.

What happens when a syllabus says nothing about AI? This is one of the most common scenarios a student faces, and the guidance is flatly contradictory. At MIT, if a syllabus is silent, students should assume AI is not allowed. At Stanford, the Board on Conduct Affairs says AI use should be “treated analogously to assistance from another person” — which, depending on your reading, either permits or restricts it. At UCL in London, their three-category framework implies that assistive AI use (brainstorming, background research) is acceptable even without explicit permission. For a student transferring between institutions — or even switching courses within one — the rules change with the hallway they walk down.

Can instructors ban AI entirely? At Stanford’s Graduate School of Business, instructors actually may not ban AI on take-home coursework, though they can restrict in-class exams. At Columbia, using generative AI without instructor permission is outright prohibited and constitutes unauthorized assistance. At the University of Sydney, AI cannot be prohibited in unsupervised assessments as of mid-2025. Their Deputy Vice-Chancellor explained: “Generative AI has already had a profound impact on workplaces and our graduates are expected to demonstrate skilled use of the relevant tools in job interviews.”

Think about this from the perspective of students and early career researchers trying to figure out what is acceptable. Stanford says your business school professor cannot stop you from using AI at home. Columbia says using it without asking is a violation. Sydney says banning it in open assessments is the violation. These are all elite institutions. They are all acting in good faith. And they are giving fundamentally incompatible guidance.

Do AI detection tools work? This one matters because it determines whether any of these policies are enforceable. Princeton explicitly recommends against AI detection tools, calling them “unreliable at best and biased at worst.” Berkeley discourages them, citing bias and inaccuracy. Australia’s national university (ANU) trialled Turnitin’s AI detection tool in 2023 and elected not to continue, reporting “significant issues regarding its efficacy (including false positives), potential bias, and lack of transparency.” Bath, in the UK, ruled them out as “fundamentally flawed.”

Meanwhile, the University of Melbourne has used Turnitin’s AI detector since April 2023. And a 2023 evaluation of 14 AI-detection tools found all scored below 80% accuracy, with only five above 70%. Simple prompt engineering reduced Turnitin’s detection rate from 100% to 0%. Most concerning, AI detectors show bias against non-native English speakers — a finding that should give every international university pause.

As I noted in my first post: we need to know the mistakes before we can test. Detection is a mistake we need to talk about honestly.

The integration pioneers are worth watching. The University of Michigan describes itself as the “first university in the world to provide a custom suite of generative AI tools to its community” — including custom GPT-4o, DALL-E, Llama, and Claude implementations that meet FERPA data security standards, all free for students and staff. They are not just allowing AI; they are building institutional infrastructure around it. Whether this becomes the model or an outlier will be one of the most important questions in higher education over the next two years.

What journals and publishers say (and the enforcement gap)

If universities are a patchwork, academic publishers present a more coherent picture — at least on the surface. The universal agreement that AI cannot be an author is accompanied by widespread agreement that AI use must be disclosed, that humans bear full responsibility for all content, and that AI-generated images are generally prohibited.

But the specifics diverge considerably, and the enforcement gap is enormous.

Disclosure requirements vary widely. Elsevier requires a mandatory “Declaration of Generative AI” section using a provided template. Taylor and Francis wants “the full name of the tool used (with version number), how it was used, and the reason for use.” SAGE makes a distinctive split: what they call “Assistive AI” (i.e., refining your own work) does not require disclosure at all, while “Generative AI” (i.e., producing new content) must be disclosed, cited in-text, and included in the reference list. SAGE is actually the only major publisher that treats AI as a citable source. Meanwhile, Springer Nature exempts basic copy editing from disclosure entirely.

So if you use Claude to polish the grammar in your methods section, SAGE says that is assistive and needs no disclosure. Taylor and Francis says disclose the tool name, version, how, and why. Springer Nature says copy editing is exempt. Elsevier says use the template. For the same action, four different publishers give four different answers.

The peer review line is clearer and revealing. Elsevier, NIH, and virtually every major funder prohibit AI in peer review. The logic is confidentiality: uploading a manuscript to an AI tool potentially exposes unpublished data to a third-party system. This is the one area where I see genuine consensus beyond the authorship question. Yet a study across 49 BMJ Group journals found that self-reported AI use among reviewers was “significantly lower” than actual usage, and only 0.7% of JAMA Network reviewers self-reported using AI over a two-year period. Up to 22% of computer science papers now show signs of LLM-generated text, and roughly 17% of peer review reports at a top CS conference showed ChatGPT-generated characteristics.

In other words: the policies are clear, but enforcement relies almost entirely on self-disclosure, and the data suggests people are not disclosing.

This is exactly the gap I warned about in my first post. Regulations without ethics. Rules without internalization. Policies without the transparent conversations required to make them meaningful.

Beyond academia, industry is moving faster (and with less scrutiny)

One of the things that struck me during this research is how different the AI conversation is inside corporations compared to academia. In universities, we are still debating whether students can use ChatGPT on a take-home essay. In industry, the question is how to scale AI across every function while managing liability.

Consulting firms have gone all-in. McKinsey’s proprietary AI chatbot “Lilli” is used by over 70% of its 45,000 consultants, approximately 17 times per week, reportedly reducing research and synthesis time by around 30%. Deloitte committed $3 billion to AI capabilities. PwC invested $1 billion over three years. Accenture plans to double its AI workforce from 40,000 to 80,000 specialists.

But here is the detail that caught my methodologist’s eye: at McKinsey, nearly 1 in 4 AI-drafted deliverables requires substantial rewriting. That 25% figure should sound familiar. In my experiment, AI-only output scored 6.1/10 overall — competent enough to impress on first glance, unreliable enough to need significant human intervention. The corporate data mirrors the classroom data.

The legal profession learned the hard way. The Mata v. Avianca case was not an isolated incident — it was the first in a cascade. Over 300 documented cases of AI-generated fabricated citations have appeared in court filings since mid-2023. The American Bar Association’s Formal Opinion 512 (July 2024) now maps AI obligations to six existing Model Rules: competence (you must understand AI capabilities and limitations), confidentiality (evaluate data risks before inputting anything), communication (inform clients of AI use), candor toward tribunal (verify all citations), supervisory responsibilities (establish firm-wide policies), and fees (you cannot charge clients for your general AI learning curve).

Journalism drew bright lines. The Associated Press released guidelines stating that “AI cannot be used to create publishable content and images for the news service” and that “any output from a generative AI tool should be treated as unvetted source material.” The Guardian committed to using AI “only with clear evidence of a specific benefit, human oversight, and the explicit permission of a senior editor.” These are among the clearest, most operational policies I have seen in any sector.

Creative industries fought for protections. The 2023 WGA and SAG-AFTRA strikes — lasting 148 and 118 days respectively — were substantially about AI. The resulting agreements established that AI cannot be considered a “writer,” AI-produced material cannot be “literary material” under guild contracts, and companies must disclose if material given to a writer was AI-generated. Getty Images banned AI-generated image submissions in September 2022 and sued Stability AI.

The government regulatory landscape is a geopolitical fault line

If the university policies are a patchwork and the publisher policies are coherent-but-unenforced, government regulation is a full-blown geopolitical divergence.

The EU AI Act is the world’s first comprehensive AI regulation, using a four-tier risk framework from “unacceptable” (banned) to “minimal” (unregulated). Education is classified as “high-risk.” Penalties reach 35 million euros or 7% of worldwide annual turnover. The first enforcement provisions took effect in February 2025.

The United States went in the opposite direction. Biden’s 2023 Executive Order (the longest in history at 110 pages) directed over 50 federal entities to take action on AI safety, equity, and innovation. Trump revoked it on his first day in office and signed a new order prioritizing “removing barriers” to AI development. The NIST AI Risk Management Framework remains a widely used voluntary standard, but the federal regulatory direction shifted decisively from precaution to promotion. Meanwhile, 31 states have enacted their own AI laws, and in December 2025, Trump issued an executive order seeking to preempt state-level regulation — creating a regulatory tug-of-war.

The UK chose a “pro-innovation” approach: five principles (safety, transparency, fairness, accountability, contestability) implemented by existing sector regulators rather than a single AI act. Canada’s proposed AI legislation died when Parliament was prorogued. Australia relies on voluntary principles and guidelines.

For researchers and professionals who work across borders — which, in 2026, is most of us — this means the regulatory framework governing your AI use depends not just on your institution or your journal, but on your jurisdiction. The same action might be unregulated in Texas, governed by a voluntary code in London, and subject to a high-risk compliance regime in Brussels.

The ethical issues nobody has solved

Beneath the policy specifics, several fundamental ethical challenges run through every sector, and none of them have been resolved.

The copyright question is fracturing in real time. The US Copyright Office confirmed that purely AI-generated material is not copyrightable and that human authorship is required. But the question of whether AI companies can train on copyrighted material is producing contradictory court decisions. One federal ruling rejected fair use for training on legal headnotes. Another called training on copyrighted books “transformative — spectacularly so.” Over 50 federal lawsuits between IP holders and AI developers were pending as of 2025. The Supreme Court may eventually weigh in, but for now, there is no settled law.

Bias is documented and persistent. Amazon scrapped a recruitment AI that favored male candidates. Facial recognition error rates remain significantly higher for darker-skinned individuals. A July 2025 study found chatbots recommended lower salaries to women and minorities for equally qualified candidates. Google suspended its Gemini image generator over historical inaccuracies. These are not theoretical concerns — they are measured, quantified failures with real consequences.

And this connects directly to my experiment’s finding that both ChatGPT and Claude amplified dataset biases, prioritizing Global North studies on mental health. When we talk about AI bias in research contexts, we are not just talking about fairness in hiring algorithms. We are talking about whose knowledge counts, whose voices are amplified, and whose are erased — the fundamental questions of social science methodology.

The environmental cost is growing and poorly governed. Global data center electricity consumption reached approximately 415 TWh in 2024, projected to nearly double by 2030. Training GPT-3 alone generated an estimated 552 tons of CO2. Most tech companies do not distinguish AI from non-AI workloads in their environmental reports. No binding framework requires AI-specific environmental mitigation. This is an ethical dimension that gets almost no attention in the “should students use ChatGPT” debate — but it should.

So where does this leave us?

Let me be direct about what this landscape review tells us.

The consensus is thinner than it appears. Yes, everyone agrees AI cannot be an author. Everyone agrees on disclosure. Everyone agrees humans bear responsibility. But the moment you move from principles to practice — how much AI use is acceptable, what must be disclosed, how to enforce compliance, whether detection tools work — the agreement evaporates.

The “ethics vs. regulations” gap I identified in my first post is everywhere. Universities have policies but contradictory implementation. Publishers have disclosure requirements but minimal enforcement. Governments have frameworks but jurisdictional fragmentation. The rules exist. The shared ethical understanding of why those rules matter, and how to apply them in practice, largely does not.

This is exactly why testing matters. Going back to the tagline of this blog: we need to know the mistakes before we can test. This post documents the mistakes — the contradictions, the enforcement gaps, the unresolved tensions. With this baseline established, we can now ask the operational questions that matter: When a journal says “disclose AI use,” what does competent disclosure actually look like? When a university says “AI-assisted brainstorming is permitted,” where does brainstorming end and drafting begin? When a consulting firm says AI output requires “substantial rewriting” 25% of the time, what are the failure patterns and how do you catch them?

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In the next post, I will share the results of a dedicated comparison of specialized AI literature review tools — SciSpace, Consensus AI, and Elicit AI — tested against the same 5-point framework used in my original experiment. If you want to be notified when it goes live, subscribe to the newsletter or follow on social media.