Who Is Responsible When AI Gets Your Brand Wrong?
When a journalist writes something inaccurate about your business, you know who to call. There is an author, an editor, a publication. The correction path is clear, even if it’s difficult.
When AI says something inaccurate about your business — wrong founding date, conflated with a competitor, fabricated capabilities presented as fact — there is no one to call.
This isn’t a gap in current infrastructure that will be filled as the industry matures. It is a structural feature of how AI-generated content works, and Harvard’s Kennedy School has a name for it: distributed agency.
No one to call
The Harvard Kennedy School Misinformation Review draws a sharp distinction between two fundamentally different categories of inaccuracy.
Human misinformation is produced by actors with beliefs, motivations, and goals. The correction strategies follow from that: identify the actor, apply social or legal pressure, issue counter-statements, reduce amplification. The accountability chain exists because there is an agent at the origin.
AI hallucination emerges from a different structure entirely. A probabilistic system generates statistically likely text sequences — predicting the next token based on patterns across billions of documents. There is no intent. There is no understanding of accuracy. There is no actor who made a decision about your brand. The output is the aggregate product of training data patterns meeting a query.
Grok 3 sent 154 of 200 users to error pages in the CJR/Tow Center study. It did so with no awareness that anything had gone wrong. ChatGPT correctly identified one of ten San Francisco Chronicle articles despite Hearst’s formal content partnership with OpenAI. OpenAI did not decide to misrepresent those articles; the model generated plausible text and the text was wrong.
The practical consequence is that you cannot send a correction to a probability distribution. The remediation path for AI-generated brand misinformation is fundamentally different from correcting a journalist, a review, or a social media post. There is no actor to change their mind.
Why the belief doesn’t update
If there’s no actor to correct, you might assume the fix is at least clean: insert accurate information into the training and retrieval layers and let the model update. The reality is harder.
Mark Coeckelbergh (University of Vienna, Social Epistemology, 2025) describes the structural dynamics of AI-mediated belief as an “economy of belief revision.” Maintaining an existing belief is the cheaper option — it requires less work. Revising a belief requires more. The architecture is structurally biased toward its current representation of the world. “Using AI is believing.”
The parallel for AI systems themselves is direct. A model that has formed a representation of your brand — its authority tier, its associations, its positioning — holds that representation with inertia. Training-mode knowledge, once embedded, is not updated by a single counter-publication. It is shifted by sustained, consistent signals across multiple retrieved sources over time.
Coeckelbergh’s broader concept sharpens this further. AI-generated brand claims are not testimony in the ordinary sense — there is no human originator who holds the belief that needs revising, who can be questioned, who can update their view. They are a new epistemic category: machine-generated assertions delivered in natural language, without a human at the origin, resistant to the correction mechanisms we normally apply because those mechanisms assume an agent.
This is why correcting AI brand representation is not a one-shot content operation. It requires sustained provenance infrastructure — consistent signals across earned media, structured data, and knowledge graph entries — built and maintained across retrieval cycles to overcome structural inertia. One press release does not move the needle. One corrected Wikidata entry does not move the needle. The pattern of evidence, accumulated over time, is what shifts the model’s representation.
From answering to acting
The distributed agency problem would be serious if AI only answered questions. It becomes structurally different as AI moves from information to action.
The spectrum of agency runs from direct human browsing at one end to fully autonomous AI action at the other. The middle position — AI-assisted human activity, where software executes tasks on behalf of humans who have stated a goal — has been steadily moving toward the autonomous end.
Large Action Models are the technical substrate making this concrete. Where Large Language Models answer questions, LAMs take actions: executing task sequences, navigating interfaces, filling forms, making decisions in dynamic environments. OpenAI’s Operator and Google’s Auto Browse in Chrome can already compare products, fill forms, and make purchases without human intervention. Baidu’s production AI search system runs a four-agent architecture — a master agent classifying query complexity, a planner decomposing it into sub-tasks, an executor running tools, a writer synthesising the output — all without a human in the loop.
When AI answers a question about your brand inaccurately, a potential customer may be misled. The harm is probabilistic, downstream, mediated by human judgment.
When AI acts on behalf of a user — selecting vendors, booking suppliers, making purchase decisions based on whatever knowledge it has assembled — wrong brand knowledge doesn’t mislead. It determines concrete commercial outcomes, without any human review step between the AI’s representation and the result.
The accountability gap does not get smaller as AI becomes more capable. It gets wider, because the consequences of distributed agency are no longer hypothetical.
The trust chain
Gartner identifies trust as the primary adoption constraint for agentic AI: users will only delegate real decisions to agents they trust. But trust works in a chain.
If users trust agents, those agents must in turn trust the sources they consult. An agent making a vendor recommendation is implicitly asserting the credibility of whatever information it has assembled about that vendor. If that information is wrong — stale, conflated, fabricated — the trust chain carries the error forward, at scale, automatically.
Coeckelbergh’s concept of epistemic bubble is directly relevant here. Once a brand is positioned within a particular cluster in an AI system’s training or retrieval logic, counter-evidence may be de-weighted by the same mechanisms that created the cluster. AI-mediated knowledge environments can sustain incorrect representations not through malice but through the structural logic of how relevance and consistency are weighted. A brand that has been miscategorised, or weakly evidenced, or consistently underdescribed relative to competitors, faces an uphill correction problem that gets steeper the longer the representation persists.
The brand that is well-represented in the AI’s knowledge layer — consistent entity signals, authoritative third-party coverage, structured data that is unambiguous — sits inside the trust chain. The brand that is poorly represented, misidentified, or absent sits outside it.
When agents move from recommendation to action, being outside that trust chain is not a visibility problem. It is a market access problem.
The only available lever
None of this changes by filing a complaint. The accountability is distributed; the responses must be structural.
The Harvard HKS framework is explicit about this: the remediation path for distributed agency problems runs through the supply side. What the model has learned, and what it can retrieve, are the inputs that determine what it says and does. Those inputs are the only levers available to brands.
In practice, this means:
Earned media in publications the model retrieves reliably. The 82% of AI citations that come from editorial sources (Muck Rack, 1M+ citations) are the primary signal pool. The model learns which brands belong in which categories largely from this layer.
Structured entity data — schema markup, sameAs links, Wikidata entries — that gives the model unambiguous resolution of who you are and what you do. The technical layer that prevents conflation and suppresses drift.
Consistent, sustained signals across retrieval cycles. Not one-shot publications, but ongoing patterns of corroboration. The economy of belief revision works against you on one input; it works for you when the signals are consistent enough to shift the prior.
The question “who is responsible for what AI says about your brand?” has a clean answer: no one who can be corrected.
The question that follows is different: what is in the supply layer that shapes what the model has learned?
That is what entity confidence measures — not accountability for the current representation, but the reliability of the evidence available to AI systems when they reach for your brand. As AI moves from answering to acting, the stakes of that measurement become concrete in a way they weren’t when the only output was text.
A weak entity signal was a visibility problem when AI gave advice. When AI makes decisions, it becomes something closer to market exclusion.