Open to AI, but Responsible cover by BeAmazed Tech Ltd.: principles, disclosure, accountability, verification

Open to AI, but Responsible: What Organizations Can Learn from Case Western Reserve University

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中文版：開放使用 AI，但要負責任：從美國凱斯西儲大學的做法，看組織怎麼訂 AI 使用規則

Key takeaways

  • Case Western Reserve University (CWRU) supports and actively promotes AI in teaching, on the condition that it is used responsibly and ethically. Whether a course allows AI is up to the instructor.
  • CWRU’s approach rests on three ideas: the university sets principles while instructors decide how AI is used; AI use should be disclosed; and room for critical debate is kept open.
  • Responsible use is hard for three reasons: someone has to own the mistakes, disclosure needs clear lines, and verification takes time.
  • Companies can start by setting principles and red lines, letting each team define its own use, and building verification time into the workflow.

On November 7, 2026, Case Western Reserve University (CWRU) held its bicentennial alumni celebration in Taipei. Mina Chen, COO of BeAmazed Tech Ltd. (鑫判科技), is a CWRU MBA alumna and attended with the company’s CEO. When President Eric W. Kaler talked about the university’s AI policy, the first line on his slide was not a course count. It read: “Focused on responsible, ethical use.”

Opening the door to AI is easy. Using it responsibly is the hard part. As a consulting firm that works alongside AI every day, BeAmazed Tech faces this question constantly. This article looks at how CWRU spells out its rules, why responsible use is difficult in practice, and what companies can borrow.

CWRU’s approach: open, with clear rules

CWRU supports AI. Its AI governance policy states support for the responsible and innovative use of AI, and the president’s slide listed concrete commitments: B.S. and B.A. degrees in AI starting fall 2026, minors in AI and Applied AI, and more than 400 courses across 40 departments that integrate AI into content, assignments, or tools. The Office of the Provost describes the role of AI this way:

We view AI not as a replacement for human thinking, creativity or faculty expertise, but as an enabling layer that can support learning.

Beyond support, CWRU is specific about the conditions. Three of them map directly onto what organizations find hardest when adopting AI:

  • The university sets principles; instructors decide use. Faculty decide whether and how AI is used in their courses. Since 2024, the Provost’s office has offered four sample syllabus policies, ranging from no AI use, to limited use for specific tasks, to encouraged use with guidance, to unrestricted use.
  • Say how AI was used. The university’s teaching principles call for transparency about when and how AI is used, and prioritize critical thinking and human judgment.
  • Leave room to question. In November 2025, the student newspaper The Observer ran an editorial titled “CWRU embraces AI, but should we?”, arguing that the line between use and misuse is blurry and urging more caution about rapid adoption. The debate happens in the open, alongside the university’s push.

In other words, CWRU’s openness does not mean a free-for-all. The university sets direction and provides tools, the people closest to the work decide how AI fits, and use is disclosed.

The same approach works for companies. Each part is simply harder to do than to write down.

Why responsible use is hard

“Use AI responsibly” takes one sentence in a policy. In practice it raises three questions, and BeAmazed Tech has run into all three in its consulting work.

1. Who owns the mistakes?

AI output often looks complete and still contains errors. When summarizing official government grant guidelines, AI can pull out the key points quickly, yet line-by-line checks against the source have caught it reading a cap on person-months as a cap on funding, and applying an industry-specific limit to every applicant. If errors like these reach a client, the client prepares the application on a false basis and the consultant’s credibility takes the hit. The AI pays no price.

Preparing this article offered another example. An initial research summary attributed a quote to the university president that could not be found on CWRU’s current website, and a key word in an official statement had been paraphrased. Both were left out after verification.

2. Should AI use be disclosed?

AI produces first drafts quickly, but it also fills gaps with things that do not exist. In AI-assisted drafts of client materials, BeAmazed Tech has seen “typical scenarios” that read like real cases but were generated. In consulting, cases must be real, so those passages are removed entirely.

CWRU’s call for transparency rests on the same logic: readers deserve to know which parts reflect human judgment and which are generated text. In a company, this question shows up in proposals, client reports, and marketing copy. The lines should be agreed in advance, not after something goes wrong.

3. How much time does verification take?

AI speeds up output. It does not speed up verification. For grant-related work, BeAmazed Tech downloads the current year’s official guidelines and checks amounts, eligibility, deadlines, and quotations one by one. A large share of the time AI saves often goes straight back into checking.

Many organizations underestimate this cost. If only the time saved is counted and verification is not built into the process, one of two things usually happens: checks get skipped, or employees quietly work overtime to cover them.

Four practices companies can borrow

CWRU’s approach translates into four steps for a business:

  1. Set company-wide principles and red lines. Define what is encouraged and what is prohibited. For example, confidential client data never goes into external AI tools, and any number shared externally is checked against the original source.
  2. Let each team define its use. Just as CWRU instructors choose one of four syllabus policies, sales, marketing, and R&D carry different risks and can use AI differently, as long as the rules are written down.
  3. Make disclosure and accountability explicit. Agree in advance who is ultimately responsible for AI-assisted work and which documents need to note AI assistance.
  4. Count verification and training as real costs. Schedule time for checking, and train people to judge AI output, rather than only buying tools.

Openness and responsibility are not a trade-off. CWRU chose both, and the price is more effort on rules, communication, and verification. The same holds for companies: think the rules through first, and AI can then be scaled with confidence.

Thinking about rolling out AI across a team but unsure how to set the rules or where to start? BeAmazed Tech can help map the adoption sequence around operations and risk.

Contact BeAmazed Tech

Sources

(Based on CWRU websites and campus publications accessed on November 9, 2026. Slide content is from a photo taken at the event.)

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