Nos derniers articles

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.

This article discusses the need for #regulation of #robots and #ai in #europe, focusing on the issue of #civil #liability. Despite multiple attempts to harmonize #eu#tort #law, only the liability of producers for defective products has been successfully harmonized so far. The #aiact, published by the #europeancommission in 2021, aims to #regulate AI at the European level by classifying #smartrobots as "high risk systems", but does not address liability rules. This article explores liability issues related to AI and robots, particularly when using #deeplearning #machinelearning techniques that challenge the traditional liability paradigm.

The paper argues that seeing #riskmanagement as a question of defining the partnership between business and government is crucial to improving it rather than focusing solely on the amount of #regulation.Sometimes these partnerships are adversarial, as they can be with government regulation. Other times they are seemingly invisible, such as when society relies on private #insurance markets to manage risk.

This paper advocates for the use of #sandbox#regulation to complement a strict #liabilityregime in regulating #artificialintelligence (#ai). The #eu#regulators have already embraced this concept.

"... model uncertainty is a vital component of the current challenges in risk measurement, and therefore the regulator should design risk measures encouraging well-understood prudent decisions over (less understood) risky ones. From this perspective robust regulation should be a desirable goal. To achieve such an objective, simple – but not simpler – rules are needed."

Proposes a set of novel modeling mechanisms to regulate the size of banks' macroprudential capital buffers by using market-based estimates of systemic risk combined with a structural framework for credit risk assessment. It applies the model to the European banking sector and finds differences with the capital buffers currently assigned by national regulators, which have substantial implications for systemic risk in the EEA.

Proposes a new framework for regulating operational threats such as damage to physical assets, business disruption, and system failures. It suggests replacing rwa regulation with simple buffers of equity and outlines what a "macro-operational" approach to banking supervision might look like. It also acknowledges the limitations of macro-operational supervision and considers what new types of operations-specific emergency tools might need to be devised in response.

"… almost 50 percent of insurers at risk of facing additional regulatory scrutiny due to failing four Insurance Regulatory Information System (IRIS) ratios received sufficient internal capital to avoid enhanced regulation. Moreover, the likelihood and extent of internal capital allocation are related to regulatory scrutiny risk and the amount of capital allocated is typically just enough to avoid regulatory scrutiny."