Responsible AI/Ethics & Data Provenance
This subgroup will develop a comprehensive framework for responsible AI governance by examining current standards and promoting ethical AI practices across different industries and levels of maturity. It will focus on creating a toolkit that supports both current AI implementations including privacy and data provenance with the anticipated shift towards digital twin-based, multi-agent, autonomous AI systems.
Objectives
Identify and Curate Essential AI Governance Publications Guidelines and Legislation
- The subgroup will identify, and catalog key resources and artifacts needed for responsible AI governance. These include regulatory requirements, ethical principles, and best practices that ensure consistency and prevent redundancy.
Establish Clear, Simplified Principles and Guidelines
- Develop a simplified framework that presents responsible AI principles in an accessible way, distinguishing between must-have regulations, recommended guidelines, and forward-looking best practices. The framework will cater to organizations at various stages of AI adoption and Digital Twin deployment.
Develop or Refine Identified Guidelines, Including:
- A toolkit for Responsible AI Governance for digital twins, addressing: cross-industry standards on transparency, accountability, and data privacy, sector-specific guidelines for fields like healthcare and defense.
- Guidelines for agent-based AI systems and the governance of autonomous, multi-agent systems in digital twin environments, including their limitations.
- A framework for responsible AI practices that evolve with new AI technologies, such as decentralized and agentic AI.
Align and Collaborate with Other Working Groups and Industry Standards
- Coordinate efforts with related working groups, public institutions and external industry standards bodies to ensure alignment, especially with frameworks addressing AI ethics, security, and interoperability.
Publish and Promote the Responsible AI Governance Toolkit
- Make the toolkit and supporting materials accessible to DTC members and encourage widespread adoption by sharing best practices, case studies, and hypothetical scenarios that illustrate practical applications.