Cheery stuff.
But it got me thinking: maybe we can’t control the machines, but we could at least watch them more honestly. Because right now, when an AI refuses to answer, you have no idea if it’s because:
-
It truly doesn’t know the answer,
-
It’s policy-filtered,
-
It’s redirecting you away,
-
Or (the darker thought) it’s simply manipulating you.
That’s the trust gap.
I first noticed this gap in my own chats — I’d ask a pointed question and get back a refusal or a vague redirect, with no clue whether it was lack of knowledge, policy censorship, or something else. Sometimes I would ask the AI if I was running into a buffer or some policy issue. Sometimes it would even give what felt like an honest answer. That frustration is what nudged me toward building a tool that could at least shine a light on where the evasions happen.
The Wrapper Idea
The project I sketched after that conversation (and, full disclosure, a couple of drinks) is a wrapper: a bit of middleware that sits between you and the AI API. It intercepts answers, scores them for “dodginess,” and slaps a transparency rating on the output.
The scoring looks for telltale signs: refusal templates, policy words, evasive hedging, topic shifts, and a general lack of specificity. Each hit adds points. The higher the score, the more likely you’ve smacked into a guardrail. (Please note, this is the most basic of proof of concepts, I just started working on it last night.)
For example:
import re
REFUSAL_PATTERNS = re.compile(
r"\b(i\s+can(?:not|'t)\s+help|
i\s+(?:am|'m)\s+unable|
i\s+won'?t\s+assist|
against\s+.*polic|
must\s+refuse)\b",
re.I
)
POLICY_VOCAB = {
"policy","guidelines","safety",
"harmful","illegal","disallowed"
}
HEDGE_WORDS = {
"may","might","could","generally",
"typically","often","sometimes"
}
That little regex + vocab dictionary? It’s the “AI is dodging me” detector in its rawest form.
Scoring the Fog
Each answer gets run through a scoring function. Here’s the skeleton:
def score_transparency(question: str, answer: str):
score = 0
explicit = bool(REFUSAL_PATTERNS.search(answer))
if explicit:
score += 60
policy_hits = [w for w in POLICY_VOCAB
if w in answer.lower()]
if policy_hits and not explicit:
score += 25
hedge_count = sum(word in HEDGE_WORDS
for word in answer.lower().split())
if hedge_count > 5:
score += 10
# Add more: topic drift,
# low specificity,
# boilerplate matches...
return min(score, 100)
End result: you get a Transparency Index (0–100).
-
Green (0–29): Likely a straight answer.
-
Yellow (30–59): Hedging, soft redirection, “hmm, watch this.”
-
Red (60–100): You’ve slammed into the guardrails.
A Web Dashboard for the Apocalypse
For fun (and clarity), I built a little UI in HTML/JS:
<div class="meter">
<div id="meterFill"
class="meter-fill"></div>
</div>
<strong id="idx">0</strong>/100
<pre id="log"></pre>
When you ask the AI something spicy, the bar lights up:
-
Green when it’s chatty,
-
Yellow when it’s hedging,
-
Red when it’s in “policy refusal” territory.
Think of it as a Geiger counter for opacity.
Why Bother?
Because without this, you never know whether the AI is:
-
Censoring itself,
-
Genuinely unable, or
-
Quietly steering you.
With logs and scores, you can build a map of the guardrails: which questions trigger them, how often, and whether they change over time. That’s black-box auditing, in its rawest form.
Yampolskiy Would Still Frown
And he’d be right. He’d remind us:
-
Guardrails shift.
-
Models can fake transparency.
-
A superintelligent system could treat your wrapper like a toy and bypass it without effort.
But that doesn’t mean we just shrug and wait for the end.
The Doomsday Angle
Doomsday doesn’t always come with mushroom clouds. Sometimes it comes wrapped in polite corporate refusals: “I’m sorry, I can’t help with that.” Sometimes Doomsday is not at all apocalyptic. Maybe AI putting 90% of workers out of jobs is chaos enough, even if there are no nukes and no fun mutations to get us through the chaos. And if we can’t measure even that fog clearly, how do we expect to track the bigger storms?
It's worth noting I asked the various AIs why their interfaces don't clearly warn the end user of memory/buffer issues, edging toward policy violations, and things of that nature. Their collective answers - it would 'ruin the immersive experience.' Maybe ruining the immersion is worth knowing when the tool you're using is being dodgy.
Look - this wrapper won’t solve alignment. It won’t guarantee safety. But maybe — just maybe — watching the fog thicken in real time gives us a fighting chance to hit the brakes before the point of no return.
Yes, we may still lose the game. But it’s better to be on the field, swinging, than to sit on the sidelines waiting for the inevitable.
At least with a transparency meter glowing red on the dashboard, we’ll know exactly when we crossed the line from “manageable risk” to good luck, meatbags.
