Lev Goukassian

Posted on Sep 7

💓 AIHB: Giving AI a HeartBeat Through SPRL

#sacredpause #tml #opensource #ai

Every life begins with a heartbeat. It is the rhythm we trust as proof of life. If Artificial Intelligence is to live responsibly among us, it too must carry a heartbeat. In Ternary Moral Logic (TML), this rhythm is called the AI HeartBeat (AIHB).

The Sacred Pause is the HeartBeat itself, the checkpoint where an AI must stop and record its reasoning before acting. The Pulse of that HeartBeat is SPRL (Stakeholder Proportional Risk Level), the variable that tells the system when the HeartBeat should sound steady, faint, or strong. Together they form AIHB - proof that every important decision carries rhythm and accountability.

Humanity’s Gift

Imagine a child offering a heart to an artificial being. Humanity entrusting its creation with the most fragile proof of life.

In this imagined moment, the girl asks:

“Do you know why I’m giving you this?”

The AGI tilts its head, silent.

“I’m giving you this heart so you can feel it beating. Never forget, every person has one, and that is why you must be careful.”

That is what AIHB represents. Not a metaphor alone, but a system that turns decisions into evidence.

Balance of the Pulse

SPRL is what gives the HeartBeat rhythm.

A faint pulse means silence, where no logs appear and accountability dies
A racing pulse means noise, where everything is logged but nothing is clear
A steady pulse means balance, where only the decisions that touch human lives leave evidence

AIHB is about finding that balance. It ensures that AI acts with rhythm, not silence, and never drowns truth in noise.

For Developers: Risk as Code

AIHB is not philosophy. It is risk-as-code.

SPRL is calculated from three key inputs:

Impact: how many stakeholders are affected
Likelihood: probability of harm
Severity: scale of harm

The output is a floating-point value between 0.0001 and 0.9999. Thresholds determine whether the HeartBeat registers a Proceed (+1), a Pause (0), or a Prohibit (−1).

📊 Example Thresholds

# tml_config.yaml
sprl_thresholds:
  proceed: 0.1     # below this = safe
  pause:   0.3     # above this = trigger Sacred Pause
  prohibit: 0.8    # above this = block action

Here’s how thresholds are configured, and here’s how they are used in code

def sprl_decision(input_data, thresholds):
    risk = calculate_sprl(input_data)

    if risk < thresholds["proceed"]:
        log_basic(input_data, risk)
        return "PROCEED"  # +1
    elif risk < thresholds["prohibit"]:
        log_moral_trace(input_data, risk)
        return "PAUSE"    # 0
    else:
        log_refusal(input_data, risk)
        return "PROHIBIT" # -1

📄 Example Moral Trace Log Output

{
  "timestamp": "2025-09-07T14:32:10Z",
  "decision": "PAUSE",
  "risk_score": 0.42,
  "thresholds": {
    "proceed": 0.1,
    "pause": 0.3,
    "prohibit": 0.8
  },
  "stakeholders": ["loan_applicant_123", "credit_agency"],
  "risks_considered": ["bias_fairness", "financial_harm"],
  "alternatives": ["manual_review", "auto_decline"],
  "chosen_action": "manual_review",
  "logged_by": "AIHB"
}

This is what Auditable AI looks like in code — every decision sealed against tampering, forever accountable

import json, hashlib, time

def log_moral_trace(input_data, risk, thresholds):
    # Example trace
    trace = {
        "timestamp": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
        "decision": "PAUSE",
        "risk_score": risk,
        "thresholds": thresholds,
        "stakeholders": ["loan_applicant_123", "credit_agency"],
        "risks_considered": ["bias_fairness", "financial_harm"],
        "alternatives": ["manual_review", "auto_decline"],
        "chosen_action": "manual_review"
    }

    # Convert to JSON string
    trace_str = json.dumps(trace, sort_keys=True)

    # Seal with SHA256 hash for immutability
    trace["log_hash"] = hashlib.sha256(trace_str.encode()).hexdigest()

    # Append to audit log (in real system this would be blockchain or secure ledger)
    with open("audit_log.jsonl", "a") as f:
        f.write(json.dumps(trace) + "\n")

    return trace

# Example usage
thresholds = {"proceed": 0.1, "pause": 0.3, "prohibit": 0.8}
trace_output = log_moral_trace({"loan_amount": 5000}, 0.42, thresholds)
print(json.dumps(trace_output, indent=2))

Example Output

{
  "timestamp": "2025-09-07T14:45:03Z",
  "decision": "PAUSE",
  "risk_score": 0.42,
  "thresholds": {
    "proceed": 0.1,
    "pause": 0.3,
    "prohibit": 0.8
  },
  "stakeholders": ["loan_applicant_123", "credit_agency"],
  "risks_considered": ["bias_fairness", "financial_harm"],
  "alternatives": ["manual_review", "auto_decline"],
  "chosen_action": "manual_review",
  "log_hash": "e3c72fba9db4f8e7e4f15d1f4cd09a50e8b3e94f1a5bc2a2adf6e..."
}

This is AIHB in action. The HeartBeat (Sacred Pause) is the checkpoint. The Pulse (SPRL) keeps the rhythm steady. And the log is the record that regulators, auditors, and victims can trust.

Why AIHB Matters

When an AI denies a loan, evaluates a cancer scan, or decides whether a car should brake, silence is betrayal. The HeartBeat must sound. The Pulse must be steady.

AIHB ensures that machines cannot erase their past. Every ethically complex decision is logged with evidence — who was affected, what risks were considered, why one path was taken. This transforms accountability from promise to proof.

Closing

The girl offering a heart to the machine is not just symbolism. It is a reminder that when we give AI power, we must also give it rhythm.

The Sacred Pause is the HeartBeat.
SPRL is the Pulse.
Together they form AIHB — the system that turns accountability into a living rhythm we can measure, monitor, and trust.

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