AI, GenAI, and LLM Ethics and Laws Framework

A Framework for Trustworthy AI, GenAI/LLM, Foundations of Trustworthy AI, Realizing Trustworthy AI, Requirements of Trustworthy AI, Technical and Non-Technical Methods to Realize Trustworthy AI, Assessing Trustworthy AI

Purpose and Evaluation:

• AI, GenAI, and LLM technologies must serve a defined purpose that aligns with the Intelligence Community’s mission while minimizing risks.
• The appropriateness of AI deployment should be evaluated against potential risks, considering alternatives with lower risk when feasible.
• Risks to civil liberties, privacy, and potential negative impacts must be assessed and minimized through effective risk mitigation strategies.

Compliance and Data Usage:

• AI usage must comply with legal obligations, respecting individual rights, and using data lawfully and ethically.
• Human judgment and accountability must be integrated into AI processes to address risks and inform decision-making.
• Undesired bias in AI must be identified, accounted for, and mitigated without compromising effectiveness.

Testing and Validation:

• AI systems should undergo rigorous testing commensurate with foreseeable risks, ensuring accountability for iterations, versions, and changes.
• Documentation of AI purpose, limitations, and design outcomes is essential for transparency and accountability.
• Explainable and understandable AI methods should be used, enabling users and overseers to comprehend AI outputs and decisions.

Continuous Review and Accountability:

• Regular review of AI systems is necessary to ensure ongoing effectiveness, identify issues, and facilitate resolution.
• Clear accountability structures must be established throughout the AI lifecycle, including responsibility for maintaining records and addressing ethical concerns.

Ethics and Laws Framework for AI in the Intelligence Community

Understanding Goals and Risks:

• Define AI goals, assess risks, and consider alternative non-AI methods with lower risk.
• Engage stakeholders to ensure a common understanding of AI goals and risks.
• Document goals and risks comprehensively for transparency and accountability.

Legal Obligations and Policy Considerations:

• Partner with risk management teams to understand legal obligations, data usage restrictions, and policy considerations.
• Ensure compliance with agreements, contracts, and regulations governing data and AI usage.
• Address legal and policy restrictions related to data collection, storage, and usage in AI development and deployment.

Human Judgment and Accountability:

• Determine human involvement and accountability levels based on AI purpose and potential consequences.
• Designate accountable humans with appropriate qualifications and training.
• Establish access controls and training requirements for personnel involved in the AI lifecycle.

Mitigating Bias and Ensuring Objectivity:

• Identify and mitigate undesired bias throughout the AI lifecycle.
• Communicate biases and mitigation strategies transparently to stakeholders.
• Maintain objectivity and accuracy in AI outputs, considering potential impacts of bias on analysis and decision-making.

Testing and Validation:

• Test AI systems rigorously to ensure accuracy and reliability in controlled environments.
• Document test methodologies, results, and changes made based on test outcomes.
• Consider third-party risks and acquisition requirements for AI deployment.

Accounting for Builds, Versions, and Evolutions:

• Ensure that AI builds, versions, and evolutions are designed to achieve authorized purposes.
• Account for data drift and changes in operational environments when refining and redeploying AI.
• Document data usage, parameters, and outputs to maintain accountability and transparency.

This framework emphasizes ethical AI development and deployment practices while complying with legal obligations, promoting transparency, and mitigating risks across the AI lifecycle in the Intelligence Community.