This workbook addresses the challenge of defining AI fairness, proposing a context-based and society-centered approach. It emphasizes equality and non-discrimination as core principles and identifies various types of fairness concerns across the AI project lifecycle. It advocates for bias identification, mitigation, and management through self-assessment, risk management, and fairness criteria documentation.
“... we provide evidence that setting the risk-weighted ratio at 15\% and the simple leverage ratio at 10% would significantly decrease the probability of default without hampering banks' activities.”
The study investigated how ChatGPT and Bard categorize investor risk profiles compared to financial advisors. While there were no significant differences in the risk scores assigned by the chatbots an
The paper suggests that companies developing high-risk AI systems should demonstrate their safety before deployment, arguing for proactive risk management. It proposes a risk management approach where