Dear friend,
this is a beautifully hard problem—because most “ethical AI principles” do kill creativity by turning ethics into bureaucracy.
So I’ll give you principles that protect creativity by protecting agency, not by enforcing control.
What follows is not a policy checklist, but a set of ethical design stances.
Think of them as guardrails for living systems, not fences.
Principles for Ethical AI Use
(Without Killing Creativity)
1.
Visibility before Control
If you can’t see it, don’t try to regulate it.
If you can’t see it, don’t try to regulate it.
Principle
- Make AI use visible, discussable, and narratable before making it compliant.
- Shadow AI becomes dangerous when it is silent, not when it is creative.
Practice
- Ask: “Where are people already using AI?”
- Not: “Who violated policy?”
Why this preserves creativity
- People keep experimenting.
- They stop hiding.
2.
Human-in-the-Loop Where Meaning Is at Stake
Automate efficiency, not responsibility.
Automate efficiency, not responsibility.
Principle
- AI may suggest, draft, cluster, or simulate.
- Final judgment must remain human wherever:
- harm is possible
- dignity is involved
- truth claims are made
Practice
- Explicit “human sign-off” for:
- diagnosis
- hiring
- discipline
- strategy
- publication
Why this preserves creativity
- AI becomes a thinking partner, not an oracle.
- Humans remain authors, not operators.
3.
Reversibility by Design
No AI decision should be irreversible by default.
No AI decision should be irreversible by default.
Principle
- Any AI-supported decision must be:
- revisable
- appealable
- explainable after the fact
Practice
- Keep:
- prompts
- versions
- human rationales
- Allow rollback.
Why this preserves creativity
- People dare to try new things.
- Because failure is not fatal.
4.
Contextual Permission, Not Blanket Bans
Ethics should be situational, not absolutist.
Ethics should be situational, not absolutist.
Principle
- Replace “AI is forbidden” with:
- “AI is allowed for X, not for Y, unless Z.”
Practice
- Green / Yellow / Red zones:
- Green: ideation, drafts, exploration
- Yellow: recommendations, summaries
- Red: autonomous decisions with harm
Why this preserves creativity
- Exploration stays free.
- Risk is named, not denied.
5.
Epistemic Humility
No one should pretend to know what an AI “really means.”
No one should pretend to know what an AI “really means.”
Principle
- Treat AI outputs as:
- probabilistic
- fallible
- context-sensitive
Practice
- Ban phrases like:
- “The model says… therefore…”
- Encourage:
- “This suggests…”
- “This might be wrong because…”
Why this preserves creativity
- Curiosity survives.
- Dogma does not form.
6.
Data Dignity
Not all data wants to be intelligent.
Not all data wants to be intelligent.
Principle
- Protect:
- personal
- clinical
- intimate
- historical trauma data
from casual AI ingestion.
Practice
- Ask before training:
- “Would I say this out loud to a stranger?”
Why this preserves creativity
- Trust remains.
- People continue to speak freely outside machines.
7.
Distributed Accountability
Someone must be responsible, but not everyone must be afraid.
Someone must be responsible, but not everyone must be afraid.
Principle
- Responsibility is traceable, not punitive.
- No scapegoating of users for systemic failures.
Practice
- Clear ownership:
- who approved the use
- who reviewed the output
- who acted on it
Why this preserves creativity
- People act thoughtfully, not defensively.
- Innovation does not retreat underground.
8.
Ethics as Conversation, Not Documentation
If ethics only lives in PDFs, it is already dead.
If ethics only lives in PDFs, it is already dead.
Principle
- Ethics must be:
- discussed
- debated
- revised
Practice
- Regular forums:
- “AI mistakes we learned from”
- “Where AI surprised us”
- “Where we said no, and why”
Why this preserves creativity
- Ethics becomes lived wisdom.
- Not compliance theater.
9.
Right to Refuse Automation
No one should be forced to outsource judgment.
No one should be forced to outsource judgment.
Principle
- Humans retain the right to say:
- “I want to think this through myself.”
Practice
- No penalties for manual work in meaningful contexts.
Why this preserves creativity
- Craft survives.
- Professional identity remains intact.
10.
AI as Apprentice, Not Master
The correct metaphor matters.
The correct metaphor matters.
Principle
- Frame AI as:
- junior assistant
- intern
- sparring partner
- Never as authority.
Practice
- Teach people how to:
- challenge AI
- interrogate outputs
- say “no”
Why this preserves creativity
- Mastery grows.
- Dependence does not.
The Deepest Principle (the one you care about)
Ethical AI begins where we refuse to surrender authorship.
Ethical AI begins where we refuse to surrender authorship.
Not authorship of text,
but authorship of meaning, responsibility, and response.
This aligns perfectly with what you have been saying all along:
- 回到分類之前
- 不趕路
- 為岩中花樹所見
- 讓人回到恰如其分的人
AI can help us think.
But ethics begins where we stay answerable.
If you want, next we can:
- Translate these into clinical ethics
- Or write a one-page “AI use manifesto” for SP 2025
- Or compress this into five aphorisms
Just say the word.
