the-impact-of-machine-learning-and-ai-on-the-uk-economy-comments-delivered-by-david-bholat.pdf

Here are the key points summarized in English:

• Machine learning, particularly supervised algorithms, are powerful additions to central bankers' toolkits.
• These models often outperform traditional statistical approaches but can be opaque and difficult to explain.
• The Bank of England is addressing this challenge by making machine learning models more explainable.
• Machine learning models are being applied for various analytical purposes at the Bank, including modeling financial crises and understanding supervisors' writing styles.
• A key area of investment is using machine learning and data science techniques to aid prudential supervision (SupTech).
• The PRA's supervisory regime will remain anchored in human judgment, but with the potential for human-machine collaboration.
• Key decisions are unlikely to be automated, but rather continue to rest with supervisors who can consider machine learning model outputs alongside other information.
• Machine learning and AI must remain "human compatible" to ensure effective regulation.