In the 1970s, IBM issued one of the simplest and sharpest rules in computing ethics:
“A computer can never get held accountable. Thus, a computer must never make a management decision.” - IBM 1979 Training
It’s written for an era when computers were deterministic calculators. Today, we live in the opposite one.. Agentic AI.. where machines not only compute but act, negotiate, plan, and decide.
The original rule feels quaint. Yet it holds a truth that becomes sharper with every new model release.
Authority without accountability is tyranny, whether by code or by culture.
But here’s the new paradox:
Even the human in the loop.. the one meant to safeguard accountability.. can become the problem. We designed the loop to prevent machines from running unchecked. We forgot that humans can drift, too.
1. The Myth of the Moral Checkpoint
When we say “keep a human in the loop,” we assume that this human brings conscience, context, and judgment.
In practice, this human often brings bias, fatigue, self-interest, and compliance.
The manager who approves an AI’s hiring recommendation might have implicit bias toward conformity.
The reviewer who signs off on algorithmic pricing may get rewarded for short-term revenue, not fairness.
The board member who oversees an AI ethics committee might depend on the same company’s profit metrics for their bonus.
Human oversight is not ethical oversight.
It’s often aligned to power, not aligned to truth.
So when we put a human in the loop, we don’t drop bias.. we make it harder to detect, because the bias is now sanctioned by hierarchy.
2. The Fallacy of Singular Accountability
Classical corporate structure assumes a clear line of blame:
→ The system erred → the human in charge takes responsibility.
But in agentic systems, causality blurs:
The AI makes a probabilistic decision.
The human approves it through interface fatigue or organizational pressure.
The outcome is bad.
Everyone is accountable, so no one is accountable.
It’s not malice but it’s structure.
The same problem that makes governments bureaucratic now infects our feedback loops. We’ve built diffuse agency systems but centralized accountability myths.
3. Disruption, Panic, and the Search for a Scapegoat
We once had another faith-based system like this: disruption. Clay Christensen’s “Innovator’s Dilemma” taught companies to worship change and blame incumbency.
It gave business a new religion: if you’re not destroying, you’re dying.
It worked.. as ideology. Not as science.
It explained everything after it happened, and nothing before.
The same pattern is repeating with AI: Every outcome is good if it scales, and every failure got forgiven as “experimentation.” More in GTM too.
The rhetoric of disruption has mutated into the rhetoric of delegated morality:
“If the AI did it, it was optimization.”
“If the human approved it, it was oversight.”
If both are true, no one needs to feel responsible. This is how moral drift becomes systemic. Not through evil.. through efficiency.
4. The Real Dilemma: Who Audits the Human in the Loop?
When both human and AI loops feed each other, the danger isn’t autonomy.. it’s mutual insulation.
The AI is opaque because of complexity. The human is opaque because of power. If the AI is wrong, the human can say “the system got trusted.” If the human is wrong, the AI can get blamed for over-automation.
The result? No closed accountability loop but only a moral Möbius strip.
To fix it, we need to move from individual accountability to systemic transparency.
5. A New Frame: Recursive Accountability Loops (RAL) v1.0
If Accountable Autonomy defined the rules for AI decision-making, Recursive Accountability defines how those rules get audited across actors.. both human and machine.
The loop has to check itself.
The Recursive Accountability Loop (RAL):
Purpose Codex (Human): Define why the loop exists and its ethical boundary conditions.. not only goals, but trade-offs you will not make. Example: “Focus on safety over delivery time beyond X threshold.”
Agentic Execution (AI): Operate within codified constraints, produce evidence (logs, metrics, rationale) for every consequential decision.
Human Interpretation (Human): Review outcomes and declare biases. Mandatory bias statements per decision.
Peer Oversight (Human/AI Hybrid): Cross-check other humans’ loops. Random audits, simulation-based validation, adversarial testing.
Public Ledger (System): Record transparent summaries.. not private notes.. of rationale and bias declarations for later review.
Meta-Loop Audit (External or Distributed): Test if the previous loop is functioning as designed. If not, fork it.
The aim is no longer “keep a human in the loop.” The aim is keep the loop auditable by humans who don’t share the same incentives.
6. From Moral Intent to Structural Ethics
Accountability can’t depend on individual virtue. It has to get architected.. through transparency, multiplicity, and recursive audit. Think of it as moving from ethical intent to ethical infrastructure.
In agentic management systems, that means:
Designing bias-logging interfaces, not bias disclaimers.
Requiring every reviewer to publish why they approved a system’s decision.
Allowing cross-loop audits.. one team’s AI and oversight pair auditing another’s.
Embedding drift detection sensors for human judgment, not only model performance.
You don’t remove bias; you expose it, trace it, and correct it. That’s how intelligence compounds.. through recursive clarity.
7. From Disruption to Continuity-of-Conscience
The 1990s promised disruption.
The 2020s demand continuity of conscience.
Every organization today faces the same structural question:
Who holds the loop accountable when the loop holds the company together?
The answer can’t be compliance or policy decks. It must get embedded ethics-as-system.. where each actor, human or machine, logs, explains, and exposes their reasoning to another loop designed to detect drift.
This is how we evolve the IBM ethic for our century:
A computer may act with reason.. but only inside loops where accountability is transparent, auditable, and distributed.
No moral heroism. No sacred human checkpoints. Only recursive architecture.
8. The Founder’s Takeaway
In your next design review or leadership meeting:
Don’t ask: “Do we have a human in the loop?”
Ask: “Who audits the human in the loop, and how do we detect their drift?”
Because the true risk of agentic systems isn’t runaway AI.
It’s runaway alignment when humans and AIs agree too on decisions no one else can question. Real intelligence doesn’t emerge from control; it emerges from recursive interpretation. Keep that loop alive.
Closing Thought
The age of Agentic AI will test not how fast machines can think, but how well humans can stay honest within systems that amplify their power.
The new management principle is simple:
You don’t escape bias by staying human. You escape it by making bias auditable.
When accountability becomes a loop, not a role, it might finally earn the right to call what we’re building intelligence.