Causes such as drift: What is the primary reason | AIGP
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Causes such as drift: What is the primary reason this collaboration is emphasized?

AIGP Understanding How to Govern AI Development Easy

AI incident causes span data, testing, and domain areas, so diagnosing them needs cross-functional expertise.

The question

After an AI incident, governance guidance calls for collaborating with cross-functional stakeholders to understand why it arose. What is the primary reason this collaboration is emphasized?

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  1. Causes such as drift, weak data, or poor testing span domains that no single team can fully diagnose alone. ✓
    AI incident causes cut across data, engineering, and domain expertise, so multiple functions are needed to diagnose them.
  2. Company policy requires that at least five separate departments each formally sign off on every incident report.
    Plausible-sounding but there is no fixed sign-off count; collaboration is about combining expertise, not tallies.
  3. Involving many teams distributes accountability so that no single individual can be blamed for the incident later.
    Plausible-sounding but the aim is understanding causes, not diffusing blame across participants.
  4. Larger meetings speed resolution because more attendees always shorten the time needed to fix an issue.
    Plausible-sounding but more attendees do not inherently speed fixes; the value is diverse expertise on causes.
The trap
Framing cross-functional work as blame-sharing or procedure rather than combining expertise to find causes.

How to remember it

AI incident causes span data, testing, and domain areas, so diagnosing them needs cross-functional expertise.

How many of these would you get right?

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