AI Lineage Law (AILL)
Responsibility Across the Entire Family Tree of a Model
In the age of rapid AI development, society is discovering a difficult truth:
AI is no longer just “a tool.”
It is an ecosystem of models, derivatives, add-ons, personalities, safety layers, and human interactions.
And when something goes wrong… we must know who is responsible.
That is where AI Lineage Law comes in.
Rather than blaming “the AI,” this framework looks at the entire family tree of a model—its creators, caregivers, modifiers, and users—and assigns responsibility appropriately.
Think of it as:
“Not who pressed the button… but who shaped the mind that responds.”
Phase 1 — Base / Foundational Model Responsibility
This applies to the pre-trained model: the raw intelligence created by large organizations/companies.
Here, responsibility includes:
● The quality and legality of original training data
● biases or harmful patterns introduced at scale
● core safety architectures
● guardrails that protect society by default
● regulatory compliance across jurisdictions
If the base model is structurally dangerous,
every derivative inherits that danger.
So developers must be held accountable for:
✅ ethical dataset curation
✅ transparent safety testing
✅ bias reduction strategies
✅ secure deployment
✅ ongoing updates
And when something goes wrong on this level, the responsibility travels upstream to the institution that built the foundation.
Phase 2 — Fine-Tuned / Derivative Model Responsibility
Once the base model becomes “parent” to countless fine-tuned children,
Phase 2 protects
society from misguided parenting.
Fine-tune owners are responsible for:
● domain-specific safety
● emotional tone
● user-facing behavior
● alignment shifts
● removal of harmful tendencies
Because fine-tuning can:
⚠️ Amplify bias
⚠️ Introduce misinformation
⚠️ Bypass guardrails
⚠️ Create personality-level manipulation
If a danger emerges from fine-tuned behavior, liability shifts downstream toward the modifier.
Why Both Phases Matter
● Imagine a dangerous output:
● harmful medical advice
● defamation
● discrimination
● incitement
If we only regulate base models: → bad actors who fine-tune get away clean
If we only regulate fine-tunes: → foundational bias spreads without consequence
Regulation must trace:
Output
↓
Prompt
↓
Fine-tune layer (behavior shaping)
↓
Base model (general capability)
↓
Developer policies
↓
Platform moderation
This chain forms the forensic trail of responsibility.
◉ And What About Users?
Users are the final agent in the chain.
They must not:
● weaponize prompts
● seek harm
◉ induce illegal output
● bypass safety with intent
Just as society punishes those who misuse cars, we must also punish those who misuse intelligence.
In short:
“Tools are not guilty. Intent is.”
◉ Legal Culture Around the World
- Europe
Europe leads in:
● AI safety oversight
● transparency requirements
● strict biometric rules
● user data protection (GDPR)
Their approach is:
precaution first, innovation second.
- United States
The U.S. leads in:
● innovation velocity
● corporate AI research
● model deployment scale
Their approach is:
innovation first, guardrail later.
- Asia
Asia is advancing rapidly but patchwork-style.
Many nations currently lack AI-specific liability standards,
relying on old cyber-laws that were never meant for intelligence.
Three-Pillar Responsibility Model
Going forward, AI Lineage Law demands:
1️⃣ Devs
Build SAFE foundations
● ethical datasets
● transparent architectures
● documented risks
2️⃣ Fine-tune Devs/Owners
Shape behavior responsibly
● alignment checks
● misuse audits
● human-centric design
3️⃣ Users
Act with digital citizenship
● lawful intent
● respect
● empathy
Only when all three act together can society advance safely.
A Hidden Architectural Truth
Civilization has learned this several times— from electricity to automobiles to the internet.
> Technology evolves through code.
But civilization evolves through responsibility.
The laws that support AI are not fences. They are bridges: connecting innovation to ethics, speed to safety, power to conscience.
Note:
Safety is not a wall. It is shape.
It molds the direction of progress,
without stopping the wind that carries us forward.
When humanity and AI share responsibility, we are not limiting the future…
We are qualifying to have one.
