AuthorSimon CHESTERMAN1 BA/LLB (Hons) (Melbourne), DPhil (Oxford); Dean and Provost's Chair Professor, Faculty of Law, National University of Singapore.
Publication year2021
Citation(2021) 33 SAcLJ 10005
Published date01 December 2021
Date01 December 2021
I. Introduction

1 The financial markets opened in New York on Thursday, 6 May 2010, much as they did on any other morning. A headline in the Wall Street Journal warned of possible economic chaos in Greece; the European Union (“EU”) and the International Monetary Fund were

cobbling together a rescue package. On Wall Street itself, concerns about European debt had seen the Dow Jones Industrial Average, an index of market value, fall nearly 60 points to close the previous day at 10,868.2

2 As the bell rang at the New York Stock Exchange, stocks were expected to continue their decline. Uncertainty about a looming election in Britain and an upcoming jobs report further dampened sentiment. In Washington DC, the Senate was debating a bill on financial regulation — part of ongoing efforts to guard against a crisis like that sparked by subprime mortgages three years earlier. Trading commenced and, as predicted, the Dow maintained its downward trajectory. Some traders moved funds into gold, long seen as a safe haven in times of economic downturn. None of this was especially unusual: markets go down as well as up.

3 One thing that did go up was known by the acronym “VIX”. Calculated by the Chicago Board Options Exchange, the volatility index is a measure of the variance of options from underlying share prices — essentially, the extent to which traders are betting that prices will change over time. A higher number theoretically means that the market could rise or fall, though VIX is also referred to as the “fear index”. That Thursday morning, it had risen by more than 20%. Traders reassured themselves that this was still far below the heights reached during the global financial crisis of 2007–2008.

4 At 2.32pm, however, the market began to collapse. Within a quarter of an hour, the Dow lost nearly 1,000 points or almost a tenth of its value — the biggest point drop over the course of a single day in its history.3 Shares in Proctor & Gamble, a blue-chip stock long seen as one of the market's most stable, fell by more than a third.4 Consulting company Accenture essentially lost all of its value, the price of its shares plummeting from US$40 to US$0.01. For reasons that no one could explain, more than a trillion dollars in market value vanished in minutes. On the floor of the New York Stock Exchange, traders shouted or watched open-mouthed as their screens flashed with sell orders and phones rang off the hook. National Economic Council Director Lawrence Summers was pulled out of a meeting. At the White House, Treasury Secretary Tim Geithner hastily briefed President Obama about what some were already calling “Black Thursday”.

5 And then, just as quickly, the market recovered.

6 In 90 seconds, half the losses were reversed. By 3.00pm, the price of most stocks had returned to previous levels. In the dry prose of a report by staff of the key regulatory bodies, “trading resumed in a more orderly fashion”.5 The day ended with the Dow 347 points below its previous close — a 3.2% drop, but suggestive of a correction rather than a catastrophe.

7 Over subsequent weeks, analysts and regulators struggled to explain what had happened during that 30-minute period. Speculation was rife that a trader had accidentally triggered a massive sale of Proctor & Gamble stock, in what came to be known as the “fat finger theory”. But attention soon turned to trading algorithms. After a five-month investigation, a government report concluded that a mutual fund's attempt to sell a large number of futures contracts had triggered the “Flash Crash”. High-frequency traders (“HFTs”) executing the sale — algorithms able to buy and sell stocks and options in a fraction of a second — were unable to find traditional purchasers and instead sold and resold the options to other HFTs. This generated what the report termed a “hot-potato” effect, as the same positions were rapidly passed back and forth between computer programs. In a 14-second period, more than 27,000 such contracts were concluded, accounting for almost half the total trading volume.6

8 The increased speed of information technology is an essential component of the artificial intelligence (“AI”) systems that are at the vanguard of what has been called a fourth industrial revolution. Moore's law famously predicts that processing speed will continue to increase —

doubling approximately every two years, as it has for half a century.7 Though there are signs that the rate of increase is slowing, ever more efficient machines mean that the marginal costs of data storage and computing power are trending towards zero.8 The increasing complexity of those systems means that, although general AI remains science fiction for the time being, current applications of narrow AI have already moved significantly beyond human cognitive abilities. As the 2010 Flash Crash demonstrated, there is also a danger that such systems can move faster than humans can control.

9 This article considers the regulatory challenges posed by speed. Many of the transformations in the digital economy are more accurately linked to the speed and efficiency of data processing rather than true cognitive ability or “intelligence” as such. Speed has, nevertheless, raised legal problems when rules designed for 20th-century society are confronted with the changing practices of the 21st. The article examines three of them.

10 The first is also the best known: the effacement of distance by the speed with which data can flow around the world. The focus here is the combination of speed with increasingly sophisticated software, posing difficulties for would-be regulators in areas from protection of intellectual property to combating “fake news”.

11 Secondly, the author returns to the “Flash Crash” of 2010 and the efforts to accommodate high-frequency trading. In theory, algorithms executing trades are subject to the same regulations as the human brokers that set them in motion. In practice, the possibility of disruption or manipulation due to the speed at which those algorithms operate has led bourses to explore ways of slowing them down. There is also a larger argument that computer-based trading has changed not only the culture but also the very nature of the market.

12 A third set of problems concerns competition law, also known as antitrust law. The digital economy offers consumers access to information previously unimaginable in any traditional marketplace. Yet that information and more is also available to retailers who are able to use pricing software to maximise profits. In the past, anti-competitive conduct required proof of a meeting of the minds to collude on prices or abuse market dominance. The speed with which prices can be adjusted

today means that tacit collusion may take place without any intent on the part of market actors — or even without any formal co-ordination between their computer programs.

13 Individually, these challenges point to practical obstacles to regulation of information technology in a globalised world. Together, particularly when combined with AI systems that are autonomous9 and opaque,10 they show the danger that those systems will operate in a manner that is uncontainable, unstoppable or undetectable.

II. The globalisation of information

14 One of the most basic challenges posed by speed, built into the structure of the Internet itself, is the globalisation of information. The ability to access data almost instantly from almost anywhere on the planet and project it globally presents obvious challenges to legal regimes premised on territorially-bounded states. Those challenges are not conceptual so much as practical, often requiring co-ordination across jurisdictions. Here, discussion will be limited to a few brief examples that should suffice to explain the problem.

15 Protection of intellectual property rights, for example, has always been challenged by the ability to make copies. The replacement of analogue technologies — the tape recorder, the photocopier — with digital ones radically transformed the economics of copying: the laborious task of making one copy gave way to the ability to share music and other content at effectively no cost and without regard to distance.11 Lawsuits and legislative changes12 led to most media platforms adopting copyright policies and takedown protocols,13 while others were shut down

completely.14 Producers and distributors developed technical means to limit copying, but a certain amount of piracy is often priced in as the cost of doing business.15

16 As with the unauthorised sharing of intellectual property, the Internet also facilitates the unwanted dissemination of prohibited material. The speed with which information can spread across the globe regularly frustrates efforts to contain it, while also challenging the legal rules intended to deter or punish tortious or criminal behaviour.16 Indeed, attempts to ban material in one jurisdiction may merely serve to increase its prominence — while not curtailing its availability from other jurisdictions. Again, this is not new: when Peter Wright's scandalous memoir of his career in MI5 was banned in the UK in the 1980s, that legal action almost certainly increased worldwide sales even before the ban was finally lifted.17 More recently, organisations such as WikiLeaks have built disaggregated distribution into their operating model.18

17 Another example of the difficulties posed by the speed of information flow is the modern phenomenon of “fake news”.19 The ability for malicious rumours to be spread online had long been identified as a problem with respect to bullying and distorting share prices, but it was the 2016 US election that led to concerns that it could be used for larger political purposes also.20 As with sharing of protected or prohibited material, the speed with which fake news flows is not a

problem caused exclusively by AI. Novel developments that are linked to new technologies...

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