A repeatable framework for maintaining intellectual ownership when working with AI. Not detection. Not policing. A practice of stewardship.
Take the Free AssessmentEvery person using generative AI faces this question. Most ignore it. The Epistemic Stewardship Framework gives you a structured, honest answer, every time. Five interconnected practices that embed integrity into your process, not after it.
Document your direction, stance, and non-negotiables before engaging AI. Know where you stand so the machine can't move you.
At every decision point: Can you defend it? Do you own it? Can you verify it? Could you teach it? Are you being honest?
Log every time you reject, revise, or override an AI suggestion. Resistance is evidence of thought.
Track which tools you used, what you prompted, what came back, and what you changed. Transparency as methodology.
A clear accounting of AI versus human contribution. Not a confession. A professional standard.
Teach students to use AI without losing their voice. Integrate stewardship into curriculum and institutional policy.
Maintain creative ownership when AI is part of your workflow. Prove the work is yours, to yourself and to clients.
Deliver client work with a clear audit trail. Show exactly where AI assisted and where your expertise led.
Embedded in your workflow from the start. Ownership is the process, not an afterthought.
Works for writing, research, design, code, consulting. Any field where AI meets human judgment.
Five repeatable practices that become second nature. Stewardship as muscle memory.
CC BY 4.0. Use it, adapt it, teach with it. No vendor lock-in.
Only flags problems after submission. Catches symptoms, not causes.
Positions AI use as cheating. Creates suspicion instead of ownership.
High false positive rates. Non-native English writers disproportionately flagged.
Proprietary scoring algorithms. Trust the black box or get flagged.
The question was never whether to use AI. It was whether the work would still be yours when you did.
Epistemic Stewardship Framework, by Nathan Madrid
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