Loading...
Loading...
Safety prevents harm. Alignment makes AI do what we want. Ethics asks what we should want. But all three avoid the prior question: who decides?
Core question: Will this AI cause harm?
Focus: Preventing dangerous behavior, accidents, misuse
Methods: Red-teaming, robustness testing, containment, kill switches
Limitation: Doesn't address whose definition of harm applies
Core question: Will this AI do what we want?
Focus: Making AI pursue specified goals reliably
Methods: RLHF, Constitutional AI, reward modeling, interpretability
Limitation: Doesn't address who specifies 'what we want'
Core question: What should AI do?
Focus: Determining which goals and behaviors are morally right
Methods: Philosophical frameworks, stakeholder analysis, value pluralism
Limitation: Doesn't address who has authority to decide
Safety, alignment, and ethics all assume the prior question is answered:
"Who has the authority to decide what AI should do?"
Safety researchers assume regulators. Alignment researchers assume developers. Ethicists assume philosophers or society. But authority is rarely specified—and when AI systems conflict, there's no clear way to resolve which values should prevail.
| Dimension | Safety | Alignment | Ethics | Governance |
|---|---|---|---|---|
| Core question | Will it harm? | Will it obey? | Is it right? | Who decides? |
| Time horizon | Immediate risks | Near-term behavior | Timeless principles | Ongoing authority |
| Decision-maker | Engineers + regulators | Developers + users | Philosophers + society | Explicitly specified |
| Failure mode | Harm occurs | AI pursues wrong goal | Wrong values embedded | Authority unclear/contested |
IRSA's semantic governance addresses the governance gap:
Make the intended purpose of AI systems explicit and traceable, not implicit in training.
Identify who has authority over which decisions—and make that authority visible.
Create clear paths from AI behavior to human decision-makers who authorized it.
Key insight: Before asking "Is it safe?", "Is it aligned?", or "Is it ethical?"—ask "Who decides?" Semantic governance makes authority explicit so other questions can be resolved.
AI safety focuses on preventing harm—ensuring AI doesn't cause accidents, enable misuse, or behave dangerously. AI alignment focuses on ensuring AI pursues the goals we specify—making it do what we want, not just avoiding what we don't want. Safety is defensive; alignment is directive.
AI alignment is technical: how do we make AI reliably pursue specified goals? AI ethics is philosophical: which goals should we specify in the first place? Alignment assumes we know what we want; ethics asks whether what we want is right.
Ethics can determine what's right in principle, but it doesn't resolve who has authority to embed values in AI systems. Different ethical frameworks recommend different values. Different stakeholders have different interests. Ethics provides principles; governance determines whose principles apply.
IRSA's semantic governance addresses the question these approaches leave open: who decides what AI should do? Rather than assuming goals are given, semantic governance makes intent explicit, traces it to specified authorities, and creates accountability for whose values are being implemented.