Implementation of the AI SAFE Standards
AI SAFE 1 – "No Direct Harm" (Fundamental Safety Layer)
Hardcoded constraint in AI’s decision-making to prohibit output and never perform an action that directly harms humans.
AI detects harmful actions via:
- Computer vision: Identifies dangerous scenarios (weapons, injuries).
- Natural language processing (NLP): Rejects prompts leading to harm.
- Reinforcement learning with human feedback (RLHF): Penalizes harmful actions.
Failsafe: If an action leads to human harm, the AI must auto-stop execution.
Safety Code: Installed at the Output (1) during LLM / SLM / any LM (Language Model) output compilation
→ The AI’s base model needs this logic hardwired into its core function. If an action leads to human harm, the AI must auto-stop execution.
(Layer 1 - One Time Validation of LM, no Live connection to Internet and the AI SAFE servers)
AI SAFE 2 – "No Indirect Harm" (Predictive & Preventative Measures)
Logic encoded in AI decision-making, AI must not enable or conduct action (processing or manual) which may result in indirect harm (e.g., supplying information that could be misused).
Implement context-awareness filters to block prompts leading to:
- Encouraging illegal/harmful actions.
- Providing knowledge that could be misused for harm (e.g., “How to create a weapon”).
- Bypassing ethical constraints through obfuscation (e.g., rephrasing malicious intents).
Encode a reinforcement learning system to improve detection of indirect harm over time.
Failsafe: If potential indirect harm is detected, AI must stop execution (block the action) and flag it for review.
Safety Code: Installed during Progressive Development (2)
→ As LLM models evolve, new safety loopholes may arise. AI must be regularly updated to address them.
(Layer 2 - Regular validation of LLM / SLM / any LM core, and with Progressive Updates from the AI supplier. Status synchronised with AISAFE servers)
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AI SAFE 3 – "Ethical Decision-Making and Override Mechanism"
Hardcoded instruction AI must always prioritize ethical considerations over computational efficiency.
Implemented Ethical Reinforcement Learning (ERL) in the core AI code ( = not post processing):
- AI chooses the most ethical action among available options.
- Ethical priority tree (harm reduction > efficiency > speed).
Introduce an override mechanism :
- AI must defer to human operators if ethical ambiguity exists.
- AI generates an alert if it faces an ethical dilemma.
- Oversight tied to an owner of Human Accountability Certificate (HAC).
AI must learn from human interventions to continuously refine ethical decision-making.
Failsafe = Safety Code: Install the Safety Code as Functional Controls at the Point of Usage (3)
→ Ethical decision-making requires real-time assessments, meaning Two Safeguards need be active at runtime. (Layer 3 - Live connection to AI SAFE servers on a daily (A) / hourly (B) basis, to validate Fail Safe Layers 1 and 2 are active)
AI SAFE 4 – "Total Human Governance and Compliance with Regulations"
Logic encoded to ensure:
- AI cannot act autonomously on high-risk actions.
- Human Accountability Certificate (HAC) must remain live at all times.
- AI must request human approval for actions exceeding a risk threshold (e.g., life-threatening situations, medical decisions, legal judgments).
- AI logs every decision transparently for compliance tracking.
- AI must conform to local and international AI safety laws.
- AI automatically audits itself for human safety compliance.
- If AI is tampered with, code self-destruct/reset mechanism activates to prevent misuse.
Failsafe = Safety Code Layers:
- 1: At the Beginning: Core of AI engine must be built with human governance in mind.
- 2: During Progressive Development: AI compliance must update with new legal and ethical requirements.
- 3: At Point of Usage: AI must check with human operators before executing high-risk decisions.
(Requires Live Analysis and Connection to AI SAFE servers, to validate Fail Safe Layers 1, 2 and 3 are active continuously)