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pour « algorithms »
#machinelearning #algorithms are increasingly for #riskassessment in the #insuranceindustry, with hybrid methods often outperforming individual ones. Research has identified challenges such as tackling imbalanced datasets, selecting features, and improving interpretability. Newer methods such as #deeplearning and ensembles may further improve accuracy.
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.
"... studies in various findings suggests a positive link between ESG and the financial performance of an organization."
"These attacks are unknown to the human eye due to malicious intent to harm any underlying infrastructure. So, to overcome the problems and make a flexible solution, we propose a framework where machine learning algorithms are applied to find relevant features from the existing dataset."
" Predictive machine learning algorithms used in banking environments, especially in risk and control functions, are generally subject to regulatory and technical constraints limiting their complexity. Knowledge distillation gives the opportunity to improve the performances of simple models without burdening their application, using the results of other - generally more complex and better-performing - models."
"This paper first reports on proposed and enacted transatlantic AI or algorithmic audit provisions. It then draws on the technical, legal, and sociotechnical literature to address the who, what, why, and how of algorithmic audits, contributing to the literature advancing algorithmic governance."
"The European Artificial Intelligence Board (EAIB) would be established as a new enforcement authority at the Union level. National supervisors will flank EAIB at the Member State level. Fines of up to '6% of global turnover, or 30 million euros for individual corporations' can be imposed."
"By comparing the decisions output by diverse settings, we find that ML algorithms can mitigate both the preference-based bias and the belief-based bias, while the effects vary for new and repeated applicants. Based on our findings, we propose a two-step human-AI collaboration framework for practitioners to reduce decision bias most effectively."
"... in a world where algorithmic opacity has become a strategic tool for firms to escape accountability, regulators in the EU, the US, and elsewhere should adopt a human-rights-based approach to impose a social transparency duty on firms deploying high-risk AI techniques."