Updated: Aug 15, 2020
There are those that suggest that we’re moving into the age of implementation with artificial intelligence, and other emerged technologies. This notion of implementation has been extrapolated from standard processes of technology development and applied to the global context of emerging technologies and their current state of maturity. Every innovative new idea goes through a period of research and discovery whereby assumptions are validated, use cases are defined, and prospective challenges are better understood. We then move into a stage of development where issues are ironed out and the solution is brought to reality. Finally, we move into the implementation or user adoption phase, where these developed technologies are put into real world use.
It can be said that many emerging technologies from genetic modification and artificial intelligence to autonomous vehicles and decentralized technologies, are globally moving into a stage of implementation. We see this most transparently as regulators demand better definitions and compliances where the laws do exist and are making moves to create the laws where they don’t yet exist. We can’t have real world adoption if we don’t have the regulatory frameworks to manage it – Microsoft’s experiment with the Twitter chat bot Tay is a fantastic case in point of what happens when new systems are introduced into a real world, non-laboratory, environment. The chat bot learned from the world of social media dialogue and became a personality that was an aggregate of this data. Within 24hrs Tay was a racist, homophobic, sexist, human hating, algorithmicized blunder.
While we might generally prescribe to the notion that “sticks and stones may break my bones, but words can never hurt me”, the extrapolation of these words into actions and the implications of careless implementation begins to paint a very telling portrait of our possible future. What’s the flip side? Technological evolution is also imperative to our continued progress across all domains – with the fact that machine learned models are already outperforming humans in the diagnosis of cancer as a great case in point.
So, How to Reconcile?
According to the UNSGSA, the first regulatory sandbox was launched in the UK in 2015, and at the beginning of 2018 there were at least 20 jurisdictions using this model. The concept of a regulatory sandbox is another borrowed from technology, whereby a ‘sandbox’ environment refers to an isolated environment where computer code can be tested against possible real-world scenarios. In this way, we can discover possible pitfalls or ways in which the program is not functioning as intended, without creating any actual problems. This has been said time and time again, but worth reiteration here – the law and its application, just is code: a set of rules. In this vein, the sandbox environment applied to testing regulatory frameworks is the testing of the rules set out in that body of regulation to see just how practical they really are.
These are confined, sometimes time bound, testing environments for innovation with regulatory oversight. The purpose is to determine the viability of technologies, put them into practice in a controlled environment, and to see how their implementation interacts with regulation and real-world scenarios. The outcomes of these experiments could be accepted implementation, changes in regulation or the technology, or a hold on implementation until the right rules and guidelines can be established or compliancy with existing laws is satisfied.
Regulatory Sandboxes Proving to be a Great Solution
Crypto and Financial Accessibility:
The most explored use case for these sandbox environments is with governments granting blockchain and related companies access to a controlled regulatory environment in which models for cryptocurrency use can be explored.
In 2018 alone, the Financial Conduct Authority (FCA) and the Consumer Financial Protection Bureau (CFPB) began working with crypto companies in regulatory sandboxes to promote adoption in the UK and USA respectively. Now in 2019 and moving into 2020, multiple jurisdictions have issued their own cryptocurrencies and models are being explored for global universal currencies; for example, Facebook’s Libra coin, a universal coin operated by a consortia of companies and organizations – evidently, further exploration of sound regulatory frameworks is greatly needed.
The success of these models are opening up vast possibilities for financial inclusion, such as methods for the unbanked to have economic participation, micro financing, methods for alternative credit scoring, and increased competition leading to fair market pricing on access to these technologies.
Autonomous Vehicles (AVs) are one of the most tangible use cases for regulatory sandbox testing and the model’s benefits. Many countries have AV testing sites, where we will see massive pieces of land dedicated to testing these vehicles in laboratory like environments. These sites are just like mini cities, with scenarios created that mimic real-world environments, with traffic regulations, other vehicles, even pedestrians, that interact with these AVs to support the understanding of their use on actual city roads. Many jurisdictions now have these vehicles on their roads and driving alongside human driven vehicles.
KPMG released an Autonomous Vehicle Readiness Index for 2019 that ranked governments on their preparedness for having these vehicles on their roads and functioning in a sound regulatory environment. Singapore ranked second, next to the Netherlands in first place and the USA in third. When we look at how these governments are preparing for AVs, the use of regulatory sandboxes is a prominent, if not the main, factor in their high rankings. Singapore in particular has implemented a sandbox intended to be active for five years, and is key to building their already world renowned control-oriented strategy for AV adoption.
These examples illustrate quite vividly how we might reconcile the incentive to speed up innovation with the stark need of ensuring we’re ready for it. Regulatory sandboxes are a solid example of the ways in which we can bridge this gap between law and technology, and we can expect a year 2020 and beyond that brings us a continued, and very needed, propagation of these models.
About the Author
Aileen Schultz is Senior Manager, Labs Programs at Thomson Reuters; Founder & President, World Legal Summit.; Fmr. Co Founder & Global Organizer, Global Legal Hackathon.
Aileen is a Toronto based award winning growth and innovation strategist with a global footprint, and a passion for creating better exponential systems. She works with SME's across several sectors with a focus in legal and blockchain technology.