Answer capsule
ICO guidance makes fair collection, explanation, preference, and harm assessment part of the revenue-data model.
What the source establishes
- The ICO defines profiling broadly around interests, habits, behavior, and predictions.
- People can object to direct-marketing profiling.
- Sensitive data and significant effects require added care.
Name the inference
Revenue leaders should know whether a score infers budget, authority, intent, urgency, risk, or personal characteristics and which data supports it.
The accountable team should translate this point into a named workflow, affected population, source data, human owner, approval right, exception path, retained evidence, and review date. That translation is what separates an interesting AI development from a decision that can be governed and evaluated.
Inspect exclusions
A prioritization model can systematically withhold attention from segments even when it never explicitly rejects a person.
The accountable team should translate this point into a named workflow, affected population, source data, human owner, approval right, exception path, retained evidence, and review date. That translation is what separates an interesting AI development from a decision that can be governed and evaluated.
Respect purpose
Data collected for product use, support, events, or partners may not be expected as prospecting input without clear notice and an appropriate basis.
The accountable team should translate this point into a named workflow, affected population, source data, human owner, approval right, exception path, retained evidence, and review date. That translation is what separates an interesting AI development from a decision that can be governed and evaluated.
Provide correction
Account and contact data errors need a practical route to correction, suppression, and downstream propagation.
The accountable team should translate this point into a named workflow, affected population, source data, human owner, approval right, exception path, retained evidence, and review date. That translation is what separates an interesting AI development from a decision that can be governed and evaluated.
Decision test
Ask whether the source changes the decision itself, the evidence required, the implementation sequence, or only the language used to describe an existing capability. Record which claims are directly supported, which are provider statements, which require an independent test, and which remain unknown. A source-linked review should make uncertainty easier to see, not bury it inside a blended score.
Questions to take into review
- Which outcome was the model built to support?
- Can a rep see and challenge the factors?
- Which sources and dates support the brief?
- What is inferred rather than observed?
- Why is this contact appropriate now?
- Which claim and source support each sentence?
- Was recording lawful and expected?
- Can participants correct material transcript errors?
The publication supports research and executive decision preparation. It does not provide legal, financial, accounting, employment, clinical, cybersecurity, investment, procurement, or implementation advice.