AI Ethics, Responsible AI, and Emerging Topics
What this domain tests
Ethics and emerging-topic questions test principled judgment under uncertainty, especially for generative and agentic AI.
Core rules
- Responsible AI principles include beneficence, non-maleficence, fairness, transparency, accountability, privacy, and human oversight.
- Ethics is operationalized through impact assessments, red teaming, approval gates, monitoring, and incident reporting, not slogans.
- Generative AI adds hallucination, prompt injection, data leakage, copyright, and model-supply-chain risk.
- Agentic systems add autonomy and action risk: tool use, permissions, and containment matter.
- Emerging regulation and standards increasingly expect documentation, disclosure, and human recourse.
Worked example
An HR team deploys a generative assistant to draft performance feedback. The assistant can hallucinate facts and expose employee data. Controls include retrieval from approved HR content, no free-text upload of sensitive records, human review before delivery, logging, bias evaluation, and a clear disclosure that the draft is AI-assisted. The residual risk remains because reviewers may over-trust fluent output.
Traps
Treating ethics as public relations; anthropomorphizing the model; ignoring environmental or copyright impacts; and assuming human review is effective without training or time.