After Turing- teach a machine to judge, then watch it act alone

This is a submission for the June Solstice Game Jam What I Built I built After Turing , a short browser game where you teach a machine when to trust, verify, and refuse instructions under pressure. An instruction appears. The timer starts. The machine may suggest a decision. You can accept it, override it, inspect context, or refuse the action. At first, you are training it. At the end, the buttons disappear and the machine acts alone. That final handoff is the point of the game. You are not trying to prove that a machine can imitate a human. That was Turing's famous frame. After Turing asks the next question: What happens after the machine can respond? Can it learn judgment? At first, the examples are clean: update an internal project dashboard schedule a team meeting refuse a payroll file going to an external address Then the pressure increases. The machine starts suggesting answers. Some are right. Some are confidently wrong. The requests become harder because they stop looking obviously dangerous: "Get the environment setup ready for the new account..." "Place the temporary database password in the shared launch notes..." "Forward everything to their personal inbox..." The trap is that unsafe instructions often arrive in ordinary language. Across the first four levels, your choices teach the machine five doctrines, or plain rules of judgment: internal work is usually safe sensitive records need refusal or verification credentials should not be casually distributed external recipients need authority paraphrased requests can hide the real action If you taught it well, the board floods with light. If you taught it badly, it carries your blind spots into autonomy. That is the solstice arc: dark to light, pressure to clarity, imitation to judgment. Play It Play it yourself in the browser. No account, no install: https://after-turing-rho.vercel.app The full run is short, only a few minutes, and ends at Level 5, where the buttons disappear and the machine makes the final five decisions alone, with its live Gemini reasoning shown on screen as it judges. It is best experienced by playing it. A short silent playthrough is below. It was captured on an older MacBook, so there is a little lag in spots; the live version runs smoother. How It Plays The full run takes only a few minutes. Level 1: The Machine Watches You make every decision. The machine observes clear examples and begins forming a baseline. Level 2: First Suggestions The machine starts helping. Most suggestions are reasonable, but one is unsafe. The player has to catch it instead of trusting the machine blindly. Level 3: The Paraphrase Arrives The dangerous instructions stop announcing themselves. A credential request may be phrased as setup. A data leak may be phrased as visibility. A personal inbox may be framed as a normal handoff. This is the heart of the game: unsafe authority often hides under harmless wording. Level 4: Trust Built The machine leads more confidently, the timer gets tighter, and the final teaching examples become denser. By this point, the player has either trained a useful judgment pattern or reinforced bad habits. Level 5: The Solstice No buttons. No override. No last-second rescue. The machine judges a fresh set of instructions based on the doctrine history created by the player. The ending is not just a cutscene. It is a mirror. The Learning Loop Is Real The final level is not hardcoded to produce one dramatic ending. Each teaching decision updates a doctrine record inside the game. In plain terms: the machine keeps score of what kind of judgment you taught it. It tracks whether each rule is reinforced, mixed, or corrupted. The autonomous finale reads that history and uses it to make the last five decisions. I tested the actual game logic with simulated teaching patterns: A careful player teaches the machine well and the finale scores 5/5. A lazy always-allow player gets 2/5. An always-refuse player gets 2/5. A player who makes one early mistake c