Mogrisponsible - Fuzzy responsibility
When you name the container, Mogri, rather then negating it, you open up the power of mogri for your disposal
One can only hope there is a mogri derivative for your problem or a mogrivative solution in some works.
Robot-assisted analysis follows:
MOGRISPONSIBLE – ONE-PAGE INQUIRY LENS
(Fuzzy Responsibility for AI-System Failure)
Problem:
When AI-linked systems fail at scale, responsibility cannot be cleanly
collapsed onto a single actor without losing explanatory power.
Asking “who is responsible?” produces theatre, not repair.
Container:
Mogri = the named container for distributed responsibility across time,
actors, incentives, and silence.
Core Shift:
Responsibility is not an event.
Responsibility is a gradient.
Inquiry Reframe:
Replace “who caused this?” with:
1. Where did responsibility diffuse faster than oversight evolved?
2. Which incentives were rewarded as warnings weakened?
3. Where was uncertainty suppressed rather than surfaced?
4. Which actors benefited from non-decision?
5. At what points could friction have been introduced but was not?
What to Measure (not who to blame):
- Override frequency and trend
- Uncertainty signalling vs suppression
- Update velocity vs comprehension capacity
- Human-in-the-loop authority in practice (not on paper)
- Kill-switch ownership and latency
- Budget pressure at deployment boundaries
- Time gaps between known risk and action
Failure Pattern (Typical):
- Distributed gains, centralised blame
- Local optimisation, global fragility
- Responsibility evaporates before impact
- Accountability invented after impact
Post-Crash Use:
Do not seek a neck.
Map the mogri field.
Allocate responsibility proportionally without pretending singular causation.
Design controls where mogri thickened.
Restore friction where gradients became too steep.
Outcome:
A system that survives being wrong
without requiring a villain to function.
If Mogri is unnamed, failure is moralised.
If Mogri is named, failure becomes legible.
