I. The Principle of Active Attention
- I will recognize "Inertia Scrolling" as a trap. I understand that the longer I stay on an app without a goal, the more the algorithm "scales" its attempt to trigger my emotions for engagement.
- I will use "Digital Inoculation." Before diving into a controversial topic, I will remind myself of the "Math of the Trap": that outrage is a more profitable signal for the machine than calm truth.
II. The Principle of Verification (Gold over Garbage)
- I will use the "Rule of Three." I will never accept a high-stakes AI output as "Fact" unless I can verify the claim through three independent "Gold Data" sources (e.g., peer-reviewed journals, verified institutional archives, or C2PA-certified media).
- I will seek the "Provenance." I will prioritize AI tools that provide a "Digital Chain of Custody," showing exactly which datasets were used to generate a specific conclusion.
III. The Principle of Data Sovereignty
- I will minimize my "Digital Exhaust." I will use "Privacy-by-Design" tools (like data masking or pseudonymization) to ensure my personal behavior doesn't become "Garbage In" for someone else's profitable model.
- I will opt-out by default. I will treat "Sharing with the Community" as a conscious contribution, not a default setting, ensuring my private data isn't used to train models that might later be used against my interests.
IV. The Principle of Intellectual Diversity
- I will manually break my "Filter Bubble." Once a week, I will intentionally search for the smartest version of an argument I disagree with to "reset" the algorithm’s spotlight and prevent "Affective Polarization."
- I will value "Slow Information." I recognize that "Fast AI" is optimized for speed and clicks; I will prioritize "Deep AI" (like o1 "Reasoning" models) for complex decisions that require logic over mere probability.
Last Updated: 14 February 2026