A manager wants to summarise the main ethical considerations of using a generative AI coding tool responsibly for a short team briefing. Which grouping best captures those considerations?
- APrompt length, suggestion latency, and acceptance rate, since tuning how quickly and often suggestions are taken is the main responsible-use consideration when adopting the tool.
- BOutput accuracy and validation, fairness and bias, and human accountability, since these capture the core ethical considerations of using the tool responsibly. Correct
- CRepository size, language coverage, and indexing speed, because how thoroughly the tool can read a codebase is the central responsible-use question a team should address first.
- DLicence compatibility, dependency updates, and vulnerability scanning, as keeping the dependency tree clean is the principal responsible-use concern when relying on AI suggestions.
Why A is wrong: Latency and acceptance metrics are performance and adoption measures, so they miss the accuracy, fairness, and accountability themes the responsible-use briefing needs to cover.
Why B is correct: Accuracy and validation, fairness and bias, and human accountability are the genuine responsible-use themes, so this grouping correctly frames the ethical briefing.
Why C is wrong: Codebase coverage and indexing describe capability and reach rather than ethics, so they do not frame the validation, bias, and oversight issues at stake here.
Why D is wrong: Dependency hygiene and scanning are sound engineering practices but sit beside the human-oversight and fairness questions, so they do not capture the ethical core the briefing requires.