Ethics & Policy

⚖️ The Seven principles of GAIRC Ethics

The GAIRC identifies seven core principles that every AI research project must satisfy to receive validation.

1. Understandable by Design (XAI)

GAIRC rejects “Black Box” models. If a researcher cannot explain how a neural network reached a specific conclusion, the research is deemed ethically incomplete.

• Requirement: Must use Explainable AI (XAI) toolsets (like SHAP, LIME, or Grad-CAM) to provide a “Glass Box” view of decision-making.

2. Fairness & Equity

AI must not inherit or amplify human biases.

• Requirement: Mandatory Bias Audits on training datasets to ensure parity across gender, race, age, and socioeconomic status.
• Standard: Level III accredited labs must use automated bias-detection tools (e.g., AI Fairness 360) during the model validation phase.

3. Human-in-the-Loop (HITL)

AI should augment, not replace, human judgment, especially in “high-stakes” sectors like medicine, law, or national security.

• Requirement:

4. Privacy by Design

Data is the “blood” of AI, and its protection is paramount.

• Requirement: Use of Differential Privacy, federated learning, or data anonymization techniques to ensure that sensitive information cannot be “reverse-engineered” from the model.

5. Accountability & Governance

There must always be a “Human-in-Command.”

• Requirement: Every AI project must have a designated Accountable Officer who is legally and ethically responsible for the model’s outputs.

6. Technical Robustness & Safety

AI must be resilient against adversarial attacks and “edge case” failures.

• Requirement: Stress-testing models against Adversarial Machine Learning (attempts to trick the AI) and ensuring stable performance under systemic shocks.

7. Sustainability & Resilience

Large-scale AI models consume massive resources (energy/water).

• Requirement: A “Green AI” audit reporting the estimated carbon footprint of training the model.