Correlation Pitfalls With ChatGPT: Would You Fall for Them?

Date : Tags : , ,
"This paper presents an intellectual exchange with #chatgpt, … , about correlation pitfalls in #riskmanagement. … Our findings indicate that ChatGPT possesses solid knowledge of basic and mostly non-technical aspects of the topic, but falls short in terms of the mathematical goring needed to avoid certain pitfalls or completely comprehend the underlying concepts."

Measuring Ai Safety

This paper addresses the challenges associated with the adoption of #machinelearning (#ml) in #financialinstitutions. While ML models offer high predictive accuracy, their lack of explainability, robustness, and fairness raises concerns about their trustworthiness. Furthermore, proposed #regulations require high-risk #ai systems to meet specific #requirements. To address these gaps, the paper introduces the Key AI Risk Indicators (KAIRI) framework, tailored to the #financialservices industry. The framework maps #regulatoryrequirements from the #euaiact to four measurable principles (Sustainability, Accuracy, Fairness, Explainability). For each principle, a set of statistical metrics is proposed to #measure, #manage, and #mitigate #airisks in #finance.

Multivariate risk measures for elliptical and log‑elliptical distributions

"This paper introduces the multivariate range Value-at-Risk (MRVaR) and multivariate range covariance (MRCov) as #risk#measures for #riskmanagement in #regulation and investment… Frequently-used cases in industry, such as normal, student-t, logistic, Laplace, and Pearson type VII distributions, are presented with numerical examples."

Taking AI Risks Seriously: A Proposal for the AI Act

Date : Tags : , , , , , , , , ,
"... we propose applying the #risk categories to specific #ai #scenarios, rather than solely to fields of application, using a #riskassessment #model that integrates the #aia [#eu #aiact] with the risk approach arising from the Intergovernmental Panel on Climate Change (#ipcc) and related literature. This model enables the estimation of the magnitude of AI risk by considering the interaction between (a) risk determinants, (b) individual drivers of determinants, and (c) multiple risk types. We use large language models (#llms) as an example."

Central bank supervisory role: micro‑prudential supervision and regulation of ESG risks

This paper discusses the role of #centralbanks in #regulating and #supervising #esgrisks in the #banking sector. The authors review recent international and regional rules requiring banks to consider #esg factors in their #governance, and analyze the practices of #microprudential #supervisors in several jurisdictions.