I have published a small runnable sandbox that demonstrates how AI can support business decisions while generating structured, audit-ready logs.
This is not a theoretical framework.
This is a working decision environment.
The sandbox simulates a simple credit decision process for a new counterparty.
It integrates:
- AI-assisted decision making
- Structured logging (Decision, Rationale, Boundary, Intent)
- Internal audit review
- Validation
- Persistent storage as reusable decision nodes
Each decision is not only generated, but also:
- explained
- constrained
- validated
- and retained
as a reusable unit.
This allows decisions to become traceable, comparable, and reusable — rather than isolated outputs.
GitHub:
https://github.com/Shiraki5995/ai-governance-sandbox-6-credit_decision
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## What you can do
You don’t need to understand the full structure in advance.
Just run it.
Then try modifying the input data:
- increase debt ratio
- add late payments
- change country risk
Run it again and observe:
- how the AI decision changes
- how the rationale adapts
- how audit alignment may shift
This is where governance becomes visible.
---
## Why this matters
Most discussions on AI focus on model capability.
This sandbox focuses on something different:
**decision governance**
- What was decided?
- Why?
- Under what constraints?
- Is it auditable?
- Can it be reused?
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## Final note
If something is unclear,
ask your GPT — or ask your engineer.
But more importantly, just run it.
Understanding emerges from execution.
This is not a proposal.
This is something you can run.
- Žymos
- sandbox
- Prisijunkite, kad galėtumėte skelbti komentarus.
Komentarai
Mototsugu, thank you for this sandbox.
It provides a clear operational grounding by moving the discussion from largely conceptual governance to an executable environment, where one can concretely observe how a decision is produced, explained, constrained, reviewed, validated, and retained.
However, it also highlights a structural limitation (inherent to the current separation between decision and evidence): a structured log makes a decision visible, but not yet fully verifiable.
In a real-world scenario for instance, a credit refusal contested several years later the challenge goes well beyond explanation. It becomes necessary to reliably reconstruct: the data used, the constraints applied, the decision path, as well as the exact state of the system at the time of execution.
This directly aligns with the logic of Article 12 of Regulation (EU) 2024/1689, which requires high-risk AI systems to ensure logging capabilities and traceability throughout their lifecycle, enabling effective monitoring and accountability and also resonates with the purpose of AI regulatory sandboxes under Article 57, designed to test such mechanisms in supervised environments.
From this perspective, the challenge is not so much to add more logs, but to move towards what could be described as an “AI Evidence Layer”:
Because a decision can be perfectly traceable… while still being biased, miscalibrated, or contextually inadequate.
Traceability does not guarantee validity it only documents it.
This is precisely where external evaluation mechanisms (such as backtesting on real cases or independent third-party audits) become essential.
It is a tension I regularly observe in forensic investigation contexts.
> One question therefore arises regarding the current architecture of the sandbox:
does the persistence of “decision nodes” genuinely enable this level of evidence, or does it remain at a demonstrative level on this aspect?
In that respect, have you considered, as a next step in the repository, explicitly separating the decision component from the logging component, in order to introduce a proof layer independent from the decision engine itself in other words, a first concrete implementation of this “AI Evidence Layer”?