The increasing complexity of regulatory updates from global authorities presents significant challenges for medical device manufacturers, necessitating agile strategies to sustain compliance and maintain market access. Concurrently, regulatory bodies must effectively monitor manufacturers' responses and develop strategic surveillance plans. This study employs a multi-agent modeling approach, enhanced with Large Language Models (LLMs), to simulate regulatory dynamics and examine the adaptive behaviors of key actors, including regulatory bodies, manufacturers, and competitors. These agents operate within a simulated environment governed by regulatory flow theory, capturing the impacts of regulatory changes on compliance decisions, market adaptation, and innovation strategies. Our findings illuminate the influence of regulatory shifts on industry behaviour and identify strategic opportunities for improving regulatory practices, optimizing compliance, and fostering innovation. By leveraging the integration of multi-agent systems and LLMs, this research provides a novel perspective and offers actionable insights for stakeholders navigating the evolving regulatory landscape of the medical device industry.

Safety Management Systems (SMSs) have been used in many safety-critical industries and are now being developed and deployed in the automated driving system (ADS)-equipped vehicle (AV) sector. Industries with decades of SMS deployment have established frameworks tailored to their specific context. Several frameworks for an AV industry SMS have been proposed or are currently under development. These frameworks borrow heavily from the aviation industry although the AV and aviation industries differ in many significant ways. In this context, there is a need to review the approach to develop an SMS that is tailored to the AV industry, building on generalized lessons learned from other safety-sensitive industries. A harmonized AV-industry SMS framework would establish a single set of SMS practices to address management of broad safety risks in an integrated manner and advance the establishment of a more mature regulatory framework. This paper outlines a proposed SMS framework for the AV industry based on robust taxonomy development and validation criteria and provides rationale for such an approach. Keywords: Safety Management System (SMS), Automated Driving System (ADS), ADS-Equipped Vehicle, Autonomous Vehicles (AV)

Mark Zuckerberg's Meta will not release an advanced version of its artificial intelligence model in the EU, blaming the decision on the "unpredictable" behaviour of regulators. The owner of Facebook, Instagram and WhatsApp is preparing to issue its Llama model in multimodal form, meaning it is able to work across text, video, images and audio instead of just one format. Llama is an open source model, allowing it to be freely downloaded and adapted by users. However, a Meta spokesperson confirmed the model would not be available in the EU. "We will release a multimodal Llama model over the coming months – but not in the EU due to the unpredictable nature of the European regulatory environment," the spokesperson said.

The deceleration of global poverty reduction in the last decades suggests that traditional redistribution policies are losing their effectiveness. Alternative ways to work towards the #1 United Nations Sustainable Development Goal (poverty eradication) are required. NGOs have insistingly denounced the criminalization of poverty, and the social science literature suggests that discrimination against the poor (a phenomenon known as aporophobia) could constitute a brake to the fight against poverty. This paper describes a proposal for an agent-based model to examine the impact that aporophobia at the institutional level has on poverty levels. This aporophobia agent-based model (AABM) will first be applied to a case study in the city of Barcelona. The regulatory environment is central to the model, since aporophobia has been identified in the legal framework. The AABM presented in this paper constitutes a cornerstone to obtain empirical evidence, in a non-invasive way, on the causal relationship between aporophobia and poverty levels. The simulations that will be generated based on the AABM have the potential to inform a new generation of poverty reduction policies, which act not only on the redistribution of wealth but also on the discrimination of the poor.

Insurance is a $1.2 trillion industry in the U.S. alone, employing 2.9 million people. Historically, the insurance industry hasn’t felt the effects of digital disruption, due to a strict regulatory environment, the scale required to create a risk portfolio, and the time needed to establish trust with customers. But in a recent IBM Institute for Business Value (IBV) survey, insurance executives identified changing market forces (such as increased competition and changing customer preferences) as the top driver affecting their enterprise. The core function of the insurance industry, risk management, has gotten more complex as customer data continues to compound. Insurance companies collect data scattered across siloed business units in paper or various unstructured digital formats. In this data-rich environment, underwriting and claims management workers don’t have immediate access to the information needed for informed internal and external decision-making, leading to burnout and costly mistakes. In fact, knowledge workers spend 30% of their time finding information required to…

Artificial intelligence (AI) systems employ learning algorithms that adapt to their users and environment, with learning either pre-trained or allowed to adapt during deployment. Because AI can optimize its behavior, a unit's factory model behavior can diverge after release, often at the perceived expense of safety, reliability, and human controllability. Since the Industrial Revolution, trust has ultimately resided in regulatory systems set up by governments and standards bodies. Research into human interactions with autonomous machines demonstrates a shift in the locus of trust: we must trust non-deterministic systems such as AI to self-regulate, albeit within boundaries. This radical shift is one of the biggest issues facing the deployment of AI in the European region.

Nearly two-thirds of C-level AI leaders can't explain how specific AI decisions or predictions are made, according to a new survey on AI ethics by FICO, which says there is room for improvement. FICO hired Corinium to query 100 AI leaders for its new study, called "The State of Responsible AI: 2021," which the credit report company released today. More than two thirds of survey-takers say the processes they have to ensure AI models comply with regulations are ineffective, while nine out of 10 leaders who took the survey say inefficient monitoring of models presents a barrier to AI adoption. Seeing as how the regulatory environment is still developing, it's concerning that 43% of respondents in FICO's study found that "they have no responsibilities beyond meeting regulatory compliance to ethically manage AI systems whose decisions may indirectly affect people's livelihoods," such as audience segmentation models, facial recognition models, and recommendation systems, the company said. At a time when AI is making life-altering decisions for their customers and stakeholders, the lack of awareness of the ethical and fairness concerns around AI poses a serious risk to companies, says Scott Zoldi, FICO's chief analytics officer.

Twenty years ago, there was a huge gulf between China and the United States on AI research. While the U.S. was witnessing sustained growth in research efforts by both public institutions and private sectors, China was still conducting low-value-added activities in global manufacturing. But in the intervening years, China has surged to rapidly catch up. From a research perspective, China has become a world leader in AI publications and patents. This trend suggests that China is also poised to become a leader in AI-empowered businesses, such as speech and image recognition applications.

Last Friday, the World Economic Forum (WEF) sent out a press announcement about an artificial intelligence (AI) toolkit for corporate boards. The release pointed to a section of their web site titled Empowering AI Leadership. For some reason, at this writing, there is no obvious link to the toolkit, but the press team was quick to provide the link. It is well laid out in linked we pages, and some well-produced pdfs are available for download. For purposes of this article, I have only looked at the overview and the ethics section, so here are my initial impressions. As would be expected from an organization focused on a select few in the world, the AI toolkit is high level.

It's an exciting time to be in robotics. Driven by increasing diversification in the industry, the $100 billion global sector has been growing by leaps and bounds. Industrial robots are no longer the exclusive domain of heavy industry or huge factories. Collaborative robots in particular have helped expand the enterprise customer base to include mid-sized and even small businesses in light manufacturing, materials handling, fulfillment, and beyond. But are the good times coming to an end?