Transparency in AI Systems
Which TWO actions best promote transparency in an AI system?
Quick Answer
The answer is providing clear explanations for individual predictions and publishing an audit trail of model inputs and decisions. These two actions best promote transparency in an AI system because they directly address the core technical requirement of explainability and verifiability. Clear explanations allow end-users to understand why a specific output was generated, while an audit trail enables external stakeholders to trace the system’s logic from input to output, ensuring accountability and facilitating compliance audits. On the Salesforce AI Associate exam, this concept tests your understanding that transparency is not just about disclosing data sources but about making the model’s decision-making process inspectable. A common trap is confusing transparency with fairness or accuracy—remember that transparency specifically concerns visibility into how decisions are made, not the quality of those decisions. A useful memory tip: think “Explain and Trace” for the two pillars of AI transparency.
⚠ Common exam trap
Salesforce often tests the misconception that transparency is about protecting the model or maximizing accuracy, when in fact it is about openness and explainability of decisions.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Publish an audit trail of model inputs and decisions.
Publishing an audit trail of model inputs and decisions enables external verification of the AI system's behavior, which is a core requirement for transparency. This allows stakeholders to trace how specific inputs led to particular outputs, ensuring accountability and facilitating debugging or compliance audits.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Limit access to the model's logic to protect intellectual property.
Why it's wrong here
Limiting access reduces transparency.
- ✓
Publish an audit trail of model inputs and decisions.
Why this is correct
Audit trails provide insight into decision process.
- ✗
Use a complex deep learning model for higher accuracy.
Why it's wrong here
Complex models are often less interpretable.
- ✓
Provide clear explanations for individual predictions.
Why this is correct
Explanations help stakeholders understand outputs.
- ✗
Remove feature importance to simplify the model.
Why it's wrong here
Removing feature importance reduces transparency.
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Same concept, more angles
3 more ways this is tested on AI Associate
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. Which TWO actions are most effective in promoting transparency in AI systems? (Choose two.)
medium- A.Publish the entire source code of the model online.
- ✓ B.Provide a model card that describes the purpose, accuracy, and limitations of the AI model.
- C.Withhold the data sources used for training to protect the company's competitive advantage.
- D.Encrypt all customer data used in the model.
- ✓ E.Offer an explanation feature that shows why a specific prediction was made for a given user.
Why B: Options B and E are correct. A model card (B) provides a concise summary of the AI model's purpose, accuracy, and limitations, promoting transparency. An explanation feature (E) allows users to understand why a specific prediction was made, enhancing interpretability. Option A is incorrect because publishing the entire source code does not necessarily convey meaningful information about model behavior and may be excessive. Option C is incorrect because withholding data sources reduces transparency. Option D is irrelevant to transparency; encryption pertains to data security and privacy, not openness.
Variation 2. Which TWO actions are essential for ensuring transparency in an AI system? (Choose two.)
medium- A.Hide the model's internal logic to protect intellectual property
- ✓ B.Log all AI decisions and allow audit
- C.Train the model on the largest dataset available
- ✓ D.Provide clear explanations for AI decisions
- E.Obtain consent from all data subjects
Why B: Options B and D are correct because providing explanations and logging decisions are key to transparency. Option A is wrong because hiding the model reduces transparency. Option C is wrong because training on all data may embed biases, and does not directly relate to transparency. Option E is wrong because consent is about privacy, not transparency.
Variation 3. Which TWO actions promote transparency in AI decision-making?
easy- A.Use black-box models that maximize accuracy.
- ✓ B.Document the model's limitations and potential biases.
- C.Keep the AI algorithms proprietary to protect intellectual property.
- D.Only share the final results of the AI system.
- ✓ E.Provide clear explanations for significant AI decisions.
Why B: Options B and E are correct because documenting limitations and biases helps users understand the model's constraints, and providing clear explanations for significant decisions fosters trust and accountability. Option A (black-box models) increases opacity, so it does not promote transparency. Option C (keeping algorithms proprietary) hinders external scrutiny. Option D (only sharing final results) hides the decision-making process, reducing transparency.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI Associate practice question is part of Courseiva's free Salesforce certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI Associate exam.