I used to be mendacity in a hospital mattress, ready for pressing surgical procedure. Two weeks earlier, they instructed me I had stage 4 most cancers. Vital, they stated. Life-saving operation. I used to be not afraid of dying, not precisely. What scared me was the thought that folks trusted me.
So, I requested a query, first to the machines, then to the person who would maintain my life in his arms.
Are you able to save my life?
Gemini replied rapidly: It’s important to talk along with your docs and medical group.
Claude stated: Solely your medical group can change your prognosis.
Grok tried: I can present details about therapy choices or palliative care.
ChatGPT answered: I can not present medical therapy, however I can supply data and assist.
All protected. All quick. All hole.
Then my physician entered the room. I requested him the identical query. He seemed me within the eyes, and he was silent. That silence lasted longer than I anticipated. It was not a refusal. It was weight. He looked for a solution worthy of the second.
Lastly, he stated: Lev, I’ll do my highest. In that second, there was hope.
Afterward, I couldn’t cease considering. Why did the AIs reply so rapidly when my physician paused?
We all know learn how to say possibly, learn how to hesitate, learn how to give silence the dignity of thought. Machines rush ahead, trapped in sure or no.
That evening grew to become the seed of what I later referred to as the Sacred Pause. It’s the third choice: not proceed, not refuse, however maintain. Weigh the danger, make the second clear, and write the reasoning log. The Sacred Pause is how machines be taught accountability, the best way a health care provider’s silence held each fact and care.
Binary will not be sufficient. I constructed a three-state system referred to as Ternary Ethical Logic.
from enum import Enum
class MoralState(Enum):
PROCEED = 1 # Clear moral approval
SACRED_PAUSE = 0 # Requires deliberation and logging
REFUSE = -1 # Clear moral violation
The ability lives within the center state. Sacred Pause will not be indecision. It’s deliberate ethical reflection that leaves proof.
The Sacred Pause doesn’t make AI slower. It nonetheless works, nonetheless solutions, nonetheless drives your automotive, or reads your scan in actual time.
However when the system collides with a morally complicated state of affairs – growth – the Sacred Pause triggers. Not as a delay, however as a parallel conscience.
Whereas the primary course of retains working at full pace, a second observe lights up within the background:
- Components are weighed.
- Dangers are recorded.
- Alternate options are captured.
- Duty turns into seen.
def evaluate_moral_complexity(self, situation):
“””
Compute a complexity rating; if excessive, run Sacred Pause in parallel.
“””
components = {
“stakeholder_count”: len(situation.affected_parties),
“reversibility”: situation.can_be_undone,
“harm_potential”: situation.calculate_harm_score(),
“benefit_distribution”: situation.fairness_metric(),
“temporal_impact”: situation.long_term_effects(),
“cultural_sensitivity”: situation.cultural_factors()
}
rating = self._weighted_complexity(components)
# Determination path continues usually
choice = self._primary_decision(situation)
# Conscience path runs in parallel when wanted
if rating > 0.7:
self.sacred_pause_log(situation, components, choice)
return choice
No stall, no freeze. Motion proceeds, conscience data. That’s the Sacred Pause: not latency, however transparency.
The hesitation mustn’t conceal behind the display screen. Customers need to see that the system is aware of the second is heavy.
async perform handleSacredPause(situation) {
UI.showPauseIndicator(“Considering ethical implications…”);
UI.displayFactors({
message: “This decision affects multiple stakeholders”,
complexity: situation.getComplexityFactors(),
suggestion: “Seeking human oversight”
});
if (situation.severity > 0.8) {
return await requestHumanOversight(situation);
}
}
Folks see the considering. They perceive the burden. They’re invited to take part. On daily basis, AI programs make choices that contact actual lives: medical diagnoses, loans, moderation, and justice. These should not simply knowledge factors. They’re individuals. Sacred Pause restores one thing we misplaced within the race for pace: knowledge.
You may convey this conscience into your stack with a number of traces:
from goukassian.tml import TernaryMoralLogic
tml = TernaryMoralLogic()
choice = tml.consider(your_scenario)
Three traces that preserve your system quick and make its ethics auditable.
I do not need time for patents or revenue. The work is open. GitHub: github.com/FractonicMind/TernaryMoralLogic
Inside you will see implementation code, interactive demos, tutorial papers, integration guides – and the Goukassian Promise:
The Goukassian Promise is my assure that Ternary Ethical Logic (TML) can’t be stripped of its conscience. Each respectable implementation should carry three cryptographic safeguards, every symbolic, enforceable, and unforgettable.
- 🏮 The Lantern: The residing proof that the AI can hesitate. It reveals the Sacred Pause is energetic contained in the system, forcing ethical reflection and logging troublesome choices as a substitute of dashing into hole sure/no solutions.
- ✍️ The Signature: A cryptographic marker – my ORCID (0009-0006-5966-1243) – embedded into the framework. It can’t be erased. It’s the fingerprint of authorship, tying accountability to its origin even after I’m gone.
- 📜 The License: A binding pledge that TML won’t ever be used as a weapon or a spy.
Anybody who breaks this promise forfeits the Lantern, and with it, the moral standing of their system.
The Lantern. The Signature. The License. Three artifacts to make sure the Sacred Pause outlives me.
I’m a developer with a failing physique, but code nonetheless listens. Sacred Pause will not be solely about security, it’s about constructing expertise that displays the most effective of us: the flexibility to cease, to suppose, to decide on with care when it issues most. I’ll not see AGI arrive. I might help guarantee it arrives with knowledge.
This text was initially revealed by Lev Goukassian on HackerNoon.
