Nos derniers articles

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.

This article discusses the proposed #eu #cyberresilienceact (#cra) as a response to the growing #cybersecurityrisks associated with the fourth industrial revolution and the Internet of Things (#iot). It provides an overview of the CRA's provisions, including its #risk-based approach, #regulatoryrequirements, and scope of application, and critically evaluates them.

This article discusses the need for high-level frameworks to guide the #regulation of #artificialintelligence (#ai) technologies. It adapts a #fintechinnovation Trilemma framework to argue that regulators can prioritize only two of three aims when considering AI oversight: promoting #innovation, mitigating #systemicrisk, and providing clear #regulatoryrequirements.