This paper examines the current landscape of AI regulations, highlighting the divergent approaches being taken, and proposes an alternative contextual, coherent, and commensurable (3C) framework. The EU, Canada, South Korea, and Brazil follow a horizontal or lateral approach that postulates the homogeneity of AI systems, seeks to identify common causes of harm, and demands uniform human interventions. In contrast, the U.K., Israel, Switzerland, Japan, and China have pursued a context-specific or modular approach, tailoring regulations to the specific use cases of AI systems. The U.S. is reevaluating its strategy, with growing support for controlling existential risks associated with AI. Addressing such fragmentation of AI regulations is crucial to ensure the interoperability of AI. The present degree of proportionality, granularity, and foreseeability of the EU AI Act is not sufficient to garner consensus. The context-specific approach holds greater promises but requires further development in terms of details, coherency, and commensurability. To strike a balance, this paper proposes a hybrid 3C framework. To ensure contextuality, the framework categorizes AI into distinct types based on their usage and interaction with humans: autonomous, allocative, punitive, cognitive, and generative AI. To ensure coherency, each category is assigned specific regulatory objectives: safety for autonomous AI; fairness and explainability for allocative AI; accuracy and explainability for punitive AI; accuracy, robustness, and privacy for cognitive AI; and the mitigation of infringement and misuse for generative AI. To ensure commensurability, the framework promotes the adoption of international industry standards that convert principles into quantifiable metrics. In doing so, the framework is expected to foster international collaboration and standardization without imposing excessive compliance costs.

While Artificial Intelligence (AI) technologies are progressing fast, compliance costs have become a huge financial burden for AI startups, which are already constrained on research & development budgets. This situation creates a compliance trap, as many AI startups are not financially prepared to cope with a broad spectrum of regulatory requirements. Particularly, the complex and varying regulatory processes across the globe subtly give advantages to well-established and resourceful technology firms over resource-constrained AI startups [1]. The continuation of this trend may phase out the majority of AI startups and lead to giant technology firms' monopolies of AI technologies. To demonstrate the reality of the compliance trap, from a field deployment perspective, we delve into the details of compliance costs of AI commercial operations.

According to Deloitte, compliance costs have risen by 60% for banks and other financial institutions since the 2008 recession. The situation is not much different in other industries as well. As a result, enterprises across the globe are struggling to minimize the cost of compliance under control. Even though there are many ways to keep compliance costs in check, none are as effective as using automation and artificial intelligence. Artificial intelligence and automation can not only increase your efficiency of compliance operations but can also minimize costs.

While regulations are created to protect consumers and markets, they're often complex, making them costly and challenging to adhere to. Highly regulated industries like Financial Services and Life Sciences have to absorb the most significant compliance costs. Deloitte estimates that compliance costs for banks have increased by 60% since the financial crisis of 2008, and the Risk Management Association found that 50% of financial institutions spend 6 to 10% of their revenues on compliance. Artificial intelligence (AI) and intelligent automation processes, such as RPA (robotic process automation) and NLP (natural language processing) can help drive efficiencies up and costs down in meeting regulatory compliance. In a single year, a financial institution may have to process up to 300 million pages of new regulations, disseminated from multiple state, federal, or municipal authorities across a variety of channels.

The EU's forthcoming regulation on Artificial Intelligence could cost the bloc's economy up to €31 billion over the next 5 years and cause investments to shrink by as much as 20%, according to a study published on Monday (26 July). The assessment by the Centre for Data Innovation looked into the administrative costs of the Artificial Intelligence Act (AIA), a horizontal EU regulation to introduce increasing obligations based on the level of risk associated with the application of the groundbreaking technology. The study author stresses the administrative burden the new legislation is expected to create, which they say will disincentivise innovation and technology uptake. "The Commission has repeatedly asserted that the draft AI legislation will support growth and innovation in Europe's digital economy, but a realistic economic analysis suggests that argument is disingenuous at best," said senior policy analyst and report author Ben Mueller. That goal would require roughly 10 times the level of current investment in the technology, yet the study author says compliance costs would eat up just under 20% of those investments.

Microsoft AI is a platform used to develop AI solutions in conversational AI, machine learning, data sciences, robotics, IoT, and more. Its Cognitive Services is described as a "comprehensive family of AI services and cognitive APIs to help you build intelligent apps". Tom Bernard Krake is the Azure Cloud Executive at Microsoft, responsible for leveraging and evaluating the Azure platform. Tom is joined by a team of experienced executives to optimise the Azure platform and oversee the many cognitive services that it provides. Uber uses Cognitive Services to boost its security through facial recognition to ensure that the driver using the app matches the user that is on file.

In case you haven't noticed, regulatory compliance is expensive. The banking industry spends an estimated $60-$70 billion a year on compliance, and many banks complain they have been forced to expand their compliance staffs in recent years just to keep up with the increase in regulations. Indeed, compliance-related activities can account for nearly 20 percent of a bank's overhead. The compliance function is also critically important. The three federal prudential bank regulators consider a poor compliance track record to be an indictment of a bank's overall management capability, and they will severely punish any bank that has a significant compliance violation, especially of the Bank Secrecy Act (BSA) and related anti-money laundering (AML) regulations.

The elimination of millions of jobs by battalions of artificial intelligence-powered robots makes for sensational headlines. But like many stories regarding both the threat and opportunity from technological change, the real story is both more nuanced and more interesting. A recent report from the World Economic Forum predicted that intelligent automation could eliminate five million jobs in developed countries by 2020. So, you would think a recent spate of announced job reductions in Japanese banking over the next decade – over 30,000 in total at the three major banking groups - would be a cause for concern. Instead, on a recent trip to Tokyo, I heard from a senior executive at one of those banks that automation is vital to deal with a shrinking labor force as the country ages, and that busy robots are a better alternative for the country than unfilled job vacancies.

Artificial intelligence (AI) in the banking sphere has gained significant interest in recent months. Combined with the noise around regtech (regulatory technology), both are starting to gain real traction and are actually beginning to be put to practical use. It might even be the turning point for the evolution of wider information-technology (IT) transformation for which the industry has been crying out to deliver sustainable, lower operating costs and smarter businesses. The tsunami of new regulations since the financial crisis has been the single biggest headache for banks to deal with. Already stressed IT systems have come under pressure, as have already bloated cost bases through the huge volumes of new people required to implement and monitor ongoing compliance.