Nos derniers articles

This paper explores the use of #generativeai models in financial analysis within the Rumsfeldian framework of "known knowns, known unknowns, and unknown unknowns." It discusses the advantages of using #ai #models, such as their ability to identify complex patterns and automate processes, but also addresses the #uncertainties associated with generative AI, including #accuracy concerns and #ethical considerations.

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.

"... 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."

"... the combinatorial approach of #ai and #cybersecurity is beneficial but still the associated danger persist owing to its manipulation by #cyberattackers. This chapter highlights the recent AI techniques deployed in cybersecurity and identifies the #ethical challenges thereof."

#ethical dilemmas and #regulatory considerations associated with #ai and #chatgpt adoption in financial analysis are ... addressed, emphasizing the need for responsible AI usage and human oversight in critical #financial judgments.

These papers examine the role of #collectivebargaining and #governmentpolicy in shaping strategies to deploy new #digital and #ai-based technologies at work. The authors argue that efforts to better #regulate the use of AI and #algorithms at work are likely to be most effective when underpinned by social dialogue and collective #labourrights. The articles suggest specific lessons for #unions and policymakers seeking to develop broader strategies to engage with AI and #digitalisation at work.

The study emphasizes the need for a better understanding of #ai to avoid policies that may hinder its benefits. It argues for a cross-disciplinary approach to AI #governance and clarifying its core concepts to build trust. The paper addresses two key questions: 1) What is the best way to safely introduce AI to maximize well-being and #sustainability in light of its potential #risks? and 2) What specific policy steps should be taken to implement it?

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.

This paper discusses the relationship between standards and private law in the context of #liability #litigation and #tortlaw for damage caused by #ai systems. The paper highlights the importance of #standards in supporting policies and legislation of the #eu, particularly in the #regulation of #artificialintelligence. The paper assesses the role of AI standards in private law and argues that they contribute to defining the duty of care expected from developers and professional operators of AI systems.

This paper discusses the role of public policy in #regulating the development of #ai, #ml, and #robotics, and the potential #risks of different approaches to #governance. It explores the tension between precautionary principles that prioritize risk avoidance and permissionless innovation that encourages entrepreneurship, and advocates for a more flexible, #bottomup governance approach that can address risks without hindering innovation.