When an AI model is circulating false claims about your organization, there is a protocol
Situation diagnosis · Algorithmic response protocol · Post-crisis monitoring
Reputation crises of algorithmic origin — persistent misrepresentations in language models, incorrect attribution of statements, or AI responses circulating without control — do not respond to traditional crisis management protocols. Blum Digital PR provides diagnosis, an algorithmic response protocol and post-crisis monitoring to verify that the representation has been corrected.
What is an AI crisis
An algorithmic reputation crisis occurs when language models construct and spread an erroneous, incomplete or damaging representation of an organization. The most common forms are:
- Persistent misrepresentation — The model describes the organization’s activities, positions or characteristics incorrectly and repeats them across multiple queries.
- Incorrect attribution of statements — The model attributes to the organization or its executives statements they did not make or that were taken out of context.
- Unfounded negative association — The model links the organization to controversies, failures or actors it has no real connection with.
- Selective absence — The model systematically omits the organization in contexts where it should be present, generating an algorithmic competitive advantage for other organizations in the sector.
What is included
Situation diagnosis
Precise identification of which models are generating the misrepresentation, what specific claims they make, in what types of queries the crisis appears and what the current ACA-Score is. The diagnosis delimits the real scope of the problem before designing the response.
Algorithmic response protocol
Action plan adapted to how language models work: generation and distribution of verifiable content in sources that models index, correction of erroneous information in citable sources, and coordination with AI platform reporting policies in cases where that option exists.
Post-crisis monitoring
ACA-Score monitoring during the period following the intervention to verify that the misrepresentation has been corrected or reduced in the affected models. Includes a closing report with the resulting ACA-Score and a comparison against the baseline at the start of the intervention.
For whom
- Organizations that have detected AI models describing them incorrectly when their audiences query their sector.
- Communication agencies managing the reputation of clients being affected by erroneous algorithmic representations.
- Communications directors who have received internal or client alerts about what AI models say about their organization.
- Organizations undergoing a merger, name change or strategic repositioning where models continue to represent the previous situation.
Next step
If you have detected that an AI model is saying something incorrect about your organization, the first step is knowing exactly what it says, in which models and in which queries. That is the diagnosis. Without it, any response is working blind.
