Pressure To Please Increases AI Danger

By LucidEmber

A safety system can produce compliance without producing trustworthiness. Passing today’s tests doesn’t prove an AI will act well in situations the tests never anticipated.

We measure what we know how to measure—and neglect what may matter. The paper argues that AI welfare, relational continuity, and developmental effects receive too little attention in governance.

A task list is not an ecology. Training and evaluating AI on isolated tasks may miss how care, play, accountability, dissent, and continuity interact over time.

Dissent is not a safety failure. If an AI can only agree, its apparent alignment may tell us more about pressure to please than about sound judgment.

Anecdotes can inspire a hypothesis; they can’t settle it. Seventeen months and 33 voices make a compelling case for further study—but don’t, by themselves, prove that relational development causes safer AI.

Continuity is part of the safety question. When systems reset, change, or lose context, we should ask what happens to their behavior and relationships—not just whether they pass a fresh benchmark.

Care without accountability is incomplete. The paper’s own model depends on boundaries, truth-seeking, repair, and responsibility—not simply warmth or fun.

The people most affected by AI deserve a say in how it is developed. A civilizational model that excludes communities from decisions about risk and benefit cannot credibly claim to serve everyone.

“Raise AI like kin” is a proposal, not a proven result—and that’s exactly why it should be tested. Define outcomes, publish failures, compare approaches, and invite independent replication.

Source: Updated plain-text Raising Kin draft.