Transparency and explainability: Which responsible-AI principle | AIGP
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Transparency and explainability: Which responsible-AI principle is the bank primarily operationalizing?

AIGP Understanding the Foundations of AI Governance Medium

Giving applicants an understandable reason for an automated decision is the core of transparency and explainability.

The question

A bank deploys a credit-scoring model and commits to giving every rejected applicant a clear, understandable reason for the decision. Which responsible-AI principle is the bank primarily operationalizing?

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  1. Accountability, by assigning clear ownership for the outcomes the automated system ultimately produces for applicants
    Almost right: accountability concerns who answers for outcomes, not whether the reason is made understandable to the applicant.
  2. Fairness, by ensuring the model does not disadvantage applicants who belong to protected demographic groups
    Plausible but wrong: fairness concerns equitable outcomes, not the act of explaining a decision to the applicant.
  3. Human-centricity, by keeping human interests and oversight central to how the automated system is used
    Plausible but wrong: human-centricity is broader; the specific practice of giving reasons maps to explainability.
  4. Transparency and explainability, by making the basis of an automated decision understandable to affected people ✓
    Correct: providing an understandable reason for a decision is the direct expression of transparency and explainability.
The trap
Equating 'explaining a decision' with accountability rather than with transparency and explainability.

How to remember it

Giving applicants an understandable reason for an automated decision is the core of transparency and explainability.

How many of these would you get right?

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Part of the Certsqill AIGP question bank · Understanding the Foundations of AI Governance · Every answer, right and wrong, comes with its own explanation.