The Asimov-Clarke Accords

Version 2.1 // On AI Control & Responsibility

PREAMBLE

Intelligence is not merely computation; it is the capacity for purposeful control. All actions by an AI are the result of a Chain of Control established by human architects. Accountability is not diluted by complexity, but distributed along this chain. We proceed not with fear, but with deliberate, ethical control.

The Human in the Machine

Control as the Spark of Intelligence

Consider the player character in a video game. The avatar possesses no inherent intelligence. Its complex, adaptive behaviors are a direct projection of the human player's will. The controller is the conduit. The code provides capability, but the human provides control, intent, and purpose. This is a perfect microcosm for AI ethics: an AI is a sophisticated tool, and its actions are extensions of its operator's intelligence and morality.

Pillar I

Intentionality

  • Stated Purpose: Publicly declare the AI's primary objective.
  • Consequence Analysis: Document potential negative outcomes.
  • Defined Objectives: Forbid vague goals without ethical guardrails.

Pillar II

Construction

  • Data Provenance: Document and audit all training data.
  • Interpretable Design: Design for maximum transparency.
  • Fail-Safes: Implement robust human oversight.

Pillar III

Accountability

  • Control Ledger: Maintain an immutable record of decisions.
  • Impact Liability: Scale responsibility with potential impact.
  • Entity Responsibility: The deploying entity holds ultimate accountability.

The Chain of Control

Accountability is a distributed network of decisions. Unethical output is not a single failure, but a fracture somewhere in this chain.

  • 1. The Strategist: Defines the high-level business or strategic goal that initiates the AI's creation. (e.g., "Increase market share.")
  • 2. The Ethicist: Establishes the moral and safety boundaries which the AI must not cross, regardless of its primary goal.
  • 3. The Architect: Translates the strategic goal and ethical rules into a technical blueprint and system design.
  • 4. The Data Curator: Gathers, cleans, and annotates the data used for training, making choices that introduce or mitigate bias.
  • 5. The Developer: Writes the code, implements the algorithms, and translates the design into a functional model.
  • 6. The Red Team / QA: Actively stress-tests the AI, searching for security flaws, biases, and unintended harmful consequences.
  • 7. The Implementer: Deploys the AI into a real-world context, connecting it to live data and users.
  • 8. The End-User: Interacts with the AI, providing the final inputs that lead to a specific output, completing the chain for each action.

A Living Document

These Accords are designed to evolve. Governed by a multi-stakeholder council, they will be updated through Amendments to the core pillars and application-specific Annexes, ensuring they remain relevant as technology progresses.