Would you trust an AI output if you could not see who approved it?
Been thinking about this after something that came up recently. Imagine an AI agent makes a recommendation that ends up influencing a customer workflow. The recommendation gets reviewed, approved, and eventually becomes part of how the team operates. Fast forward a few months and someone wants to understand why that decision was made.
The interesting part is that the technical history is usually still available. You can find the output. You can find the prompt. You can usually figure out which model generated it. What can be surprisingly difficult to find is the human context around the decision. Who reviewed the recommendation? Who approved it? What information did they have that made the recommendation seem reasonable at the time?
The more AI becomes part of everyday workflows, the more I find myself paying attention to that layer. Understanding the output matters, but understanding why someone trusted that output often matters just as much. A lot of conversations around AI accountability focus on the model. I suspect a lot of the missing context lives around the people making decisions with it.
Curious how your team is keeping track of that today, lets discuss it below...
Replies
Vokal
That makes sense@zhen_han .
We’ve been thinking about a similar problem with @OpenBox , especially around how agent actions and human approvals sit in the same decision trail.
The part I keep coming back to is that logs prove something happened, but they do not always explain why it was trusted. That context around approval feels just as important as the event itself.
TinyCommand
I think this becomes even more important as AI gets embedded deeper into operational workflows.
In our case, the decision structure itself does not really change. People use AI to make their work faster and easier, but they still remain responsible for the decisions they make using that output.
The AI can recommend, summarize, classify, or draft, but the accountability still sits with the human approving or acting on it.
That human layer is probably more important than the model layer in most business workflows.
@priyanka_gosai1
You put this well. AI may speed up the work, but the actual responsibility still sits with the person who chooses to trust or act on it. I think the missing piece is often not “who clicked approve,” but what context they had when they did.
Definitely no - to be honest, in the end, I probably wouldn't care whether the decision was made by a human or by a LLM. The important bit is that a human actually approved the decision and proceeded with it - and that's what I'd care about. But then this doesn't differ from how it was before LLMs :)
I see your point@sk_uxpin.
In many cases, the final human approval is the thing that matters most.
Where I think it gets a bit different with LLMs is the volume and distance from the original reasoning. A human may approve the decision, but a few months later it can be hard to understand what they were actually relying on when they approved it.
I’d be hesitant. Knowing which human reviewed and approved an AI-generated recommendation is often just as important as knowing which model produced it. As AI becomes embedded in workflows, accountability needs to include decision history, not just technical history. The way behind approval is often where the most valuable context lives.
@jenifer_lamberto
I think you’re pointing at the part that often gets missed. The model output is only one piece of the decision, but the human judgment around it is usually where the real context sits.
A recommendation can look “correct” technically, but the reason someone trusted it, changed it, or approved it in that moment matters a lot later. I’ve started thinking of approval as its own artifact, not just a checkbox attached to an AI output.
This resonates deeply. The accountability gap you're describing in AI workflows exists in an almost identical form in AI-generated code and it's one most teams haven't confronted yet.
When a developer uses Copilot, Cursor, claude code or any vibe coding tools to ship a feature, the code gets merged, reviewed at a surface level, and becomes part of how the product operates. Fast forward a few months and someone asks "why was this architectural decision made?" or "who signed off on this auth implementation?" The answer is usually "the AI suggested it and it seemed fine at the time." The technical output is there. The human context around why it was trusted isn't. That's the gap we are trying to address with KodeGauge and is built around not just flagging what AI wrote, but translating the consequences of those decisions into something a non-technical stakeholder can actually evaluate. Because if you can't see the cost of trusting an AI output, you can't really be said to have approved it at all. The accountability layer you're describing needs to exist at every level AI touches workflows, decisions, and code alike.
@m_mohammad
This is a strong point. AI-generated code has the same issue, just with higher stakes because the decision becomes part of the product surface.
The diff tells you what shipped. It does not always tell you why the team trusted that path.