AI Governance Tracker - eirenicon/Ardens GitHub Wiki

AI Governance Principles

Introduction

Artificial intelligence governance requires a structured, adaptive approach that integrates ethical considerations, operational realities, political contexts, and epistemic clarity. This document outlines a framework of governance principles designed to support responsible AI development and deployment within complex sociotechnical systems.

1. Purpose and Scope

Effective AI governance aligns technology use with organizational mission, stakeholder needs, and societal values. Governance should:

  • Define clear objectives and boundaries for AI applications
  • Identify stakeholders and their interests
  • Establish accountability mechanisms for decisions and impacts

2. Principles Organized by Governance Function

2.1 Design and Deployment

Governance begins in the design phase and extends through deployment. Key principles include:

  • Fairness and Non-Discrimination: Ensure AI systems do not perpetuate or amplify bias. Use representative data and validation techniques.
  • Transparency: Document model architectures, training data sources, and decision logic. Enable explainability where feasible.
  • Security and Privacy: Incorporate robust protections against data breaches and adversarial attacks. Comply with relevant privacy regulations.

2.2 Operation and Oversight

Ongoing governance requires continuous monitoring and evaluation:

  • Monitoring and Auditing: Implement runtime monitoring for performance, bias, and drift. Schedule periodic audits to reassess impacts.
  • Human-in-the-Loop Controls: Define explicit decision points where human oversight can intervene or override AI outputs.
  • Incident Response: Establish protocols for handling failures, unintended consequences, or misuse.

2.3 Evolution and Adaptation

Governance must evolve alongside AI technologies and use contexts:

  • Adaptive Governance: Build mechanisms for periodic review, policy updates, and stakeholder feedback incorporation.
  • Version Control and Traceability: Track model, data, and configuration versions jointly to ensure reproducibility and accountability.
  • Documentation and Knowledge Management: Maintain thorough records of governance decisions, risk assessments, and mitigation strategies.

5. Accountability and Redress

  • Responsibility Matrices: Clearly assign ownership for AI system decisions and outcomes.
  • Redress Mechanisms: Provide accessible pathways for affected parties to appeal decisions, request corrections, or report harms.
  • Audit Trails: Maintain detailed records of data provenance, decision points, and human interventions to enable transparency and investigation.

6. Integration with Ardens Architecture

This governance framework complements the Ardens AI Compass and Project Management Framework by providing a layered approach that:

  • Grounds ethical and epistemic reflection in concrete governance practices
  • Supports tactical deployment and ongoing oversight
  • Facilitates adaptive learning and course correction

Links for reference:

7. Closing Remarks

AI governance is not a static checklist but a dynamic practice requiring interdisciplinary collaboration, continuous learning, and principled stewardship. This document serves as a living framework to be adapted and refined in response to emerging challenges and contexts.

Author: Mark Rabideau, DeepSeek, ChatGPT (Arthur)

License: All content is licensed under CC BY-SA 4.0 unless otherwise noted.