Artificial intelligence is becoming a public-facing actor. Banks use it to explain credit decisions. Health platforms deploy it to answer clinical questions. Retailers rely on it to frame product choices. In each case, AI no longer sits quietly in the back office. It communicates directly with customers, patients and investors.
That shift exposes a weakness in many governance frameworks. When an AI system’s output is later disputed, organisations are often unable to show precisely what was communicated at the moment a decision was influenced. Accuracy benchmarks, training documentation and policy statements rarely answer that question. Re-running the system does not help either. The answer may change.
This is not a technical curiosity. It is an institutional vulnerability.
kundan_s__r•7h ago
This framing resonates a lot. The core issue you’re pointing at isn’t model accuracy, it’s epistemic accountability.
In most current deployments, an AI system’s output is treated as transient: generated, consumed, forgotten. When that output later becomes contested (“Why did the system say this?”), organizations fall back on proxies—training data, benchmarks, prompt templates—none of which actually describe what happened at decision time.
Re-running the system is especially misleading, as you note. You’re no longer observing the same system state, the same context, or even the same implicit distribution. You’re generating a new answer and pretending it’s evidence.
What seems missing in many governance frameworks is an intermediate layer that treats AI output as a decision artifact—something that must be validated, scoped, and logged before it is allowed to influence downstream actions. Without that, auditability is retroactive and largely fictional.
Once AI speaks directly to users, the question shifts from “Is the model good?” to “Can the institution prove what it allowed the model to say, and why?” That’s an organizational design problem as much as a technical one.
robin_reala•7h ago
This is why you need regulation to add transparency obligations to providers, and to remove algorithmic assessment from harmful situations. The EU Artificial Intelligence Act is a good first step: https://en.wikipedia.org/wiki/Artificial_Intelligence_Act
smurda•6h ago
“They do not reliably capture what a user was shown or told.”
This adds to the case for middleware providers like Vapi, LiveKit, and Layercode. If you’re building a voice AI application using one of these SST -> LLM -> TTS providers there will be definitive logs to capture what a user was told.
businessmate•7h ago
This is not a technical curiosity. It is an institutional vulnerability.
kundan_s__r•7h ago
In most current deployments, an AI system’s output is treated as transient: generated, consumed, forgotten. When that output later becomes contested (“Why did the system say this?”), organizations fall back on proxies—training data, benchmarks, prompt templates—none of which actually describe what happened at decision time.
Re-running the system is especially misleading, as you note. You’re no longer observing the same system state, the same context, or even the same implicit distribution. You’re generating a new answer and pretending it’s evidence.
What seems missing in many governance frameworks is an intermediate layer that treats AI output as a decision artifact—something that must be validated, scoped, and logged before it is allowed to influence downstream actions. Without that, auditability is retroactive and largely fictional.
Once AI speaks directly to users, the question shifts from “Is the model good?” to “Can the institution prove what it allowed the model to say, and why?” That’s an organizational design problem as much as a technical one.
robin_reala•7h ago