Quick Answer
The correct answer is an explanation of the factors considered in the decision. This is because transparency in AI loan approvals hinges on explainability, a core ethical AI principle that requires the system to disclose the key inputs—such as credit score, income, or debt-to-income ratio—that influenced the outcome, rather than revealing the full algorithm or raw training data, which would risk proprietary exposure and violate data privacy regulations like GDPR. On the Salesforce AI Associate exam, this concept tests your understanding of how to balance transparency with compliance, often appearing in scenario-based questions where you must choose between providing a clear rationale versus oversharing sensitive model details. A common trap is selecting “provide the full algorithm” or “share training data,” but remember that ethical AI focuses on user understanding, not system exposure. Memory tip: Think “Factors, not Formulas” to recall that transparency means explaining the what, not the how.
This AI Associate practice question tests your understanding of ethical considerations of ai. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company deploys an AI system that makes decisions about loan approvals. For transparency, what should they provide to applicants?
Explanation of factors considered
Correct. Providing the key factors used in the decision meets transparency requirements.
The training data
Why wrong: Training data may contain sensitive information and is not typically shared.
The full algorithm
Why wrong: Revealing the full algorithm may be proprietary and is not necessary for transparency.
Confidence scores
Why wrong: Confidence scores may help but do not fully explain the decision.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Explanation of factors considered
Option A is correct because transparency in AI-driven loan approvals requires providing applicants with an explanation of the factors considered in the decision, such as credit score, income, or debt-to-income ratio. This aligns with ethical AI principles like explainability and fairness, enabling applicants to understand and potentially contest the decision. Providing the full algorithm or training data would expose proprietary information and potentially violate data privacy regulations like GDPR.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Explanation of factors considered
Why this is correct
Correct. Providing the key factors used in the decision meets transparency requirements.
Related concept
Read the scenario before looking for a memorised answer.
The training data
Why it's wrong here
Training data may contain sensitive information and is not typically shared.
The full algorithm
Why it's wrong here
Revealing the full algorithm may be proprietary and is not necessary for transparency.
Confidence scores
Why it's wrong here
Confidence scores may help but do not fully explain the decision.
Common exam traps
Salesforce often tests the distinction between transparency (explaining the decision) and disclosure (revealing the model internals), trapping candidates who think providing the full algorithm or training data is necessary for transparency.
Detailed technical explanation
Under the hood, explainable AI (XAI) techniques like SHAP (SHapley Additive exPlanations) or LIME (Local Interpretable Model-agnostic Explanations) can generate feature importance values for each applicant, quantifying how each factor (e.g., income, loan amount) contributed to the decision. In a real-world scenario, a bank using a gradient-boosted decision tree for credit scoring must provide a reason code (e.g., 'high debt-to-income ratio') to comply with the Equal Credit Opportunity Act (ECOA), which mandates adverse action notices. This goes beyond a simple confidence score, which might be 0.85 but fails to explain why the applicant was denied.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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What to study next
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FAQ
Ethical Considerations of AI — This question tests Ethical Considerations of AI — Read the scenario before looking for a memorised answer..
The correct answer is: Explanation of factors considered — Option A is correct because transparency in AI-driven loan approvals requires providing applicants with an explanation of the factors considered in the decision, such as credit score, income, or debt-to-income ratio. This aligns with ethical AI principles like explainability and fairness, enabling applicants to understand and potentially contest the decision. Providing the full algorithm or training data would expose proprietary information and potentially violate data privacy regulations like GDPR.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Same concept, more angles
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Variation 1. Which TWO actions help ensure transparency in AI systems according to Salesforce's ethical AI guidelines?
easyWhy D: Option D is correct because documenting model assumptions and limitations is a core transparency practice under Salesforce's ethical AI guidelines. It ensures stakeholders understand the boundaries and potential biases of the AI system, enabling informed trust and accountability.
Last reviewed: Jun 30, 2026
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