A is correct: Assessing the impact and testing for fairness, safety, and privacy before deployment directly supports responsible AI requirements. By conducting thorough assessments and tests, the system can ensure that it meets standards for fairness, reliability, safety, privacy, security, inclusiveness, transparency, and accountability, as requested by the risk, legal, and compliance teams.
B is incorrect: Prioritizing speed and cost over reliability, safety, security, and user trust in the system does not directly support responsible AI requirements. Responsible AI standards emphasize the importance of reliability, safety, security, and user trust, which may be compromised if speed and cost are prioritized over these critical factors.
C is incorrect: Removing human review to keep the process objective may not directly support responsible AI requirements. Human review can play a crucial role in ensuring fairness, reliability, safety, privacy, security, inclusiveness, transparency, and accountability in AI systems. Removing human review may lead to biases and ethical issues that contradict responsible AI standards.
D is incorrect: Focusing only on accuracy metrics in model training may not directly support responsible AI requirements. Responsible AI standards encompass various aspects beyond accuracy, such as fairness, reliability, safety, privacy, security, inclusiveness, transparency, and accountability. Ignoring these factors in favor of accuracy alone may result in an AI system that does not meet the required responsible AI standards.