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Ethical Considerations of AIeasyMultiple SelectObjective-mapped

AI Associate Ethical Considerations of AI Practice Question

Which TWO practices help ensure accountability in AI systems?

⚠ Common exam trap

Salesforce often tests the distinction between technical transparency (like open-sourcing code) and operational accountability (like assigning a human owner), leading candidates to mistakenly choose open-sourcing as a sufficient accountability practice.

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

Assign a human owner for each AI system.

Assigning a human owner for each AI system establishes clear accountability, ensuring that a specific individual is responsible for the system's behavior, decisions, and compliance with ethical guidelines. This practice aligns with the principle of human oversight, which is critical for maintaining trust and addressing failures in AI systems.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Blame the AI system for mistakes to protect employees.

    Why it's wrong here

    Blame should be attributed to humans.

  • Assign a human owner for each AI system.

    Why this is correct

    Human ownership ensures responsibility.

  • Automate all decisions to eliminate human error.

    Why it's wrong here

    Automation without oversight reduces accountability.

  • Implement logging and auditing of model decisions.

    Why this is correct

    Logging enables review and accountability.

  • Open-source the model code to share responsibility.

    Why it's wrong here

    Open-sourcing does not assign responsibility.

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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.