Describe Artificial Intelligence workloads and considerations →mediumMultiple ChoiceObjective-mapped
AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
What does the responsible AI principle of 'human in the loop' refer to?
⚠ Common exam trap
Candidates often confuse 'human in the loop' with general human involvement (like data entry or CAPTCHA) rather than recognizing it specifically as oversight of consequential AI 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
✓
Maintaining human oversight and the ability to review or override consequential AI decisions
The 'human in the loop' principle ensures that humans maintain meaningful oversight over AI systems, particularly for high-stakes or consequential decisions. This means humans can review, override, or intervene in AI-generated outputs, preventing fully automated decision-making in critical scenarios such as medical diagnosis, loan approvals, or criminal justice. It is a core component of responsible AI, balancing automation with accountability.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A requirement for humans to manually enter all data into AI systems
Why it's wrong here
Requiring every data item to be manually entered shifts data preparation to humans, yet the AI system would still operate without human oversight at decision time. Manual entry is a pipeline workflow and often introduces transcription errors; it says nothing about who reviews or overrides the model's consequential conclusions. This misreads a responsibility principle as an input-handling chore.
- ✓
Maintaining human oversight and the ability to review or override consequential AI decisions
Why this is correct
The correct definition: human-in-the-loop in responsible AI is a governance design that keeps humans accountable for high-stakes decisions, enabling them to review, approve, override, or reverse model outputs before or after they take effect. This control loop matters because models can be confidently wrong or operate in evolving contexts. Human oversight is proportionate to the decision's consequence level, from automated low-risk actions to mandatory review for irreversible actions.
- ✗
Training AI models using feedback from human labelers only
Why it's wrong here
Reinforcement learning from human feedback is a training-time technique that uses labeler ratings to steer model behavior, but it does not guarantee that any human remains in control after deployment. Human-in-the-loop is broader, operationally grounded, and focuses on accountability for each consequential output. Training feedback and live oversight are different stages in the AI lifecycle; one does not substitute for the other.
- ✗
Requiring users to prove they are human before using AI services
Why it's wrong here
CAPTCHA-based human verification is an access-control mechanism that blocks automated scripts, but it does not establish any ongoing human review of AI outputs. This option conflates user authentication with governance oversight. In responsible AI, human-in-the-loop specifically means a person can inspect, veto, or alter high-impact model decisions, not just prove a session is human.
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Related to this question
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Responsible AI Principles
Key term
Responsible AI
A framework of ethical principles and practices that ensure artificial intelligence systems are developed and deployed in a transparent, fair, accountable, and safe manner.
Key term
Accountability
Accountability is the security principle that ensures actions and identity are linked so that a person or system can be held responsible for their activities.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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