How can we regulate disruptive technologies?

This article is brought to you thanks to the collaboration of The European Sting with the World Economic Forum. Author: Nidhi Singh, Co-Founder & Counsel, BlackPearl Chambers (Advocates & Solicitors)

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Advances in technology, often referred to as “disruptive innovation” have changed people’s lives. Undoubtedly, they have led to better services and the quality of products being offered to consumers today.

The world has become more integrated and inclusive because of liberalization and globalization. The Fourth Industrial Revolution (4IR) has facilitated the growth of disruptive technologies.

Although these innovations have contributed to the digital space, they have also raised legal and regulatory concerns around the world. In light of this, we must understand that the market should be balanced so that competition does not suffer at the cost of regulating disruptive technologies. Further, governments should encourage innovation while protecting the interests of consumers.

Several technological disruptions have raised questions about the efficacy of the current legal and regulatory framework around the world. Some common digital innovations include Artificial Intelligence (AI), drones, Internet of Things (IoT), blockchain and distributed ledger technology that includes cryptocurrencies and smart contracts; fintech, RegTech and Big Data.

These technologies have betrayed the traditional business and regulatory models, which thus calls for an in-depth study of their modus operandi in order to frame effective legislation.

Artificial Intelligence

According to the Merriam-Webster dictionary, AI means the development of software and computers capable of self-learning and intelligent behaviour. The Oxford Dictionary, meanwhile, defines AI as “computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision making and translation between languages”.

AI has proved useful in several sectors, such as e-health, industrial operations, commerce and online markets, owing to the fact that it can collect and analyze data in real time.

These industry specific applications of AI have proved beneficial to various sectors of an emerging economy. However, while developing future systems for AI, it is important to bear in mind the standard-setting process to safeguard the future balance of competitive forces in the market and to not overlook the legal challenges posed by AI.

It raises questions about the relationship between man and machine; the ability of humans to control “deep-learning” algorithms which are fed by data; the humans’ liability and accountability for machine activities; and antitrust liability of algorithm creators and users.

Take, for example, the sophisticated pricing algorithms being used by commercial giants in online platform markets. They raise a potential risk of tacit collusion. Prima facie, they appear to be promoting information symmetry and perfect price transparency. However, they contribute to data-driven business models that aid in predicting markets. Interestingly, this has also helped online trading platforms to process Big Data in real time, thus helping make more accurate decisions.

Drones

Another application of AI is drones. A drone is an unmanned aerial vehicle (UAV), or essentially a flying robot that can be remotely controlled or flown autonomously, through software-controlled flight plans in their embedded systems working in conjunction with onboard sensors and GPS.

They have often been used for intelligence gathering, military purposes, predicting weather, search and rescue operations, and surveillance. Drones use AI technology to mitigate crop-related risks and are often used by companies to identify damages to heavy bridges and construction sites.

Blockchain

Blockchain, as defined by Ian Walden and Theodora Christou, is in effect a database, which uses cryptographic functions to maintain data integrity and identity authentication. It keeps a record of all data exchanges, which is referred to as a “ledger” in the cryptocurrency world, and each data exchange is a “transaction”. This ledger tracks transactions, whereas a distributed ledger uses a decentralized P2P network to maintain the ledger. Every verified transaction is added to the ledger as a “block”.

The first major application of blockchain in 2009 was Bitcoin, which is a cryptocurrency and runs on a blockchain. The second major application, called Ethereum, was introduced in 2014 and was premised on the principle of smart contracts.

The advances of blockchain technology – from cryptocurrencies to smart contracts – raise the issue of data privacy and security. Cryptocurrencies operate on trust and market forces, and are not backed by any legal recognition. Because they are low cost and easy to access, it is popular and can be profitable to invest in cryptocurrencies. However, lack of legal recognition raises potential issues of trust and money laundering.

Smart contracts also work on blockchain technology. The self-executing capability of smart contracts, unlike electronic agreements, is susceptible to fewer disputes, which thus makes them smart.

It could still raise legal concerns, as it would adversely impact e-commerce, online agreements and Online Dispute Resolution. Additionally, as these contracts are legally binding and based on coding, it is not free from faults or mistakes, which makes it harder for the parties negotiating the “digital assets” in dispute.

Legal and regulatory challenges

AI raises a typical “black box” problem wherein decisions are made based on algorithms. These algorithms are programmed by companies and are, at times, so complex that even the creator fails to detect whether they have breached any of the market norms, or even identify the perpetrator of the crime.

This inability to see what is inside an algorithm is a black-box problem. If this algorithm is made public, it may lead to further compromising of data security and commercial viability of big companies.

However, in May 2018, the European Union brought in General Data Protection Regulation (GDPR) that requires companies to explain how algorithms using personal data work and make decisions.

These technological disruptions also raise the issue of cyber security. Fintech, intelligent transportation systems, and developers of autonomous vehicles are more prone to malicious cyber attacks.

A study commissioned by Deloitte Insights summarizes the challenges to traditional regulation into two buckets: business and technological, as shown below:

The pacing problem is one of the business challenges. This means that the existing legal and regulatory framework is quite slow in adapting to the fast-changing technological interventions. The regulatory agencies are also risk averse, which inhibits better regulation.

The future of regulation

According to the strategic triangle theory, a good policy should be technically correct, politically stable and organizationally implementable. Given the spate in technological disruption, it is important to have adaptive regulation in place that adjusts to rapid change and pivoting business models.

Firstly, in the case of disruptive technologies, there is a need to rely more on trial and error, rather than going through the lengthy process of legislation. A rapid feedback mechanism is an essential component to enable regulators to evaluate policies against certain set standards.

Secondly, in order to change the way in which the government intervenes to correct the market, it is pertinent to shift the focus from input to outcome-based regulation. Complex technological disruption requires room to innovate.

Governments can encourage drones, AI and blockchain-enabled applications, without impeding their development and inhibiting their freedom to choose their way of complying with the law.

As trade today has no boundaries, technological disruption calls for effective and profound cooperation, and regulatory convergence among global and regional institutions.