Ensuring the ethical and responsible use of AI in financial risk management involves not just the development of AI models but also the continuous oversight of their implementation. Conducting regular audits by external, independent AI ethics boards is a key practice that allows financial institutions to maintain transparency, fairness, and accountability in their AI systems. These audits help to uncover any biases or ethical issues that may have gone undetected during model development. By proactively addressing these concerns, institutions can adhere to ethical standards, comply with regulatory requirements, and maintain the trust of their customers and the public. These audits are also instrumental in establishing ongoing dialogue around the ethical implications of AI and in driving improvements that align AI systems with societal values.

A is incorrect. Excluding socio-demographic factors might avoid direct biases, but it may lead to indirect biases due to proxy variables. Responsible AI implementation involves careful consideration of all factors influencing the decision-making process, including any indirect biases that may arise.

C is incorrect. High-performing AI models lacking in transparency can embed biases and lead to unethical outcomes. The institution should prioritize transparency and fairness over mere performance to ensure a responsible implementation of AI.

D is incorrect. Focusing exclusively on performance metrics ignores critical aspects such as fairness, interpretability, and the potential impact of decisions on individuals. Performance metrics need to be balanced with ethical considerations for responsible AI use.