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Can Code be Law?

Jeremy Barnett has co-authored a paper on Algorithms and the Law.  Can Code be Law?

Our aim is to start a discussion in the legal profession on the legal impact of algorithms on firms, software developers, insurers, and lawyers. In a longer paper which can be found here, we consider whether algorithms should have a legal personality, an issue which will likely provoke an intense debate between those who believe in regulation and those who believe that ‘code is law’.

In law, companies have the rights and obligations of a person. Algorithms are rapidly emerging as artificial persons: a legal entity that is not a human being but for certain purposes is legally considered to be a natural person. Intelligent algorithms will increasingly require formal training, testing, verification, certification, regulation, insurance, and status in law.

The science fiction writer Isaac Asimov proposed ‘Three Laws of Robotics: Robots (1) may not injure a humans or, through inaction, allow humans to come to harm; (2) must obey orders given by humans except where such orders would conflict with the first law; and (3) must protect their own existence as long as such protection does not conflict with the first or second laws.

In 2007, the South Korean government proposed a Robot Ethics Charter. In 2011, the UK Research Council EPSRC published five ethical principles for industry. In 2017, the Association for Computing Machinery published seven principles for algorithmic transparency and accountability.

Already algorithmic trading systems account for 70%-80% of US equity trades. Apple, Google and Amazon provide ‘intelligent’ virtual assistants, chatbots, and ‘smart’ devices that interact with speech. Numerous financial firms provide ‘Robo’ advisers. Car manufacturers are working on autonomous vehicles.

In response, governments and regulators are modifying national laws to encourage innovation, with lawyers and insurers scrambling to absorb the implications.

Key technologies

Key technologies to consider are artificial intelligence (knowledge-based systems and machine learning NLP and sentiment analysis), blockchain (distributed ledger technologies and smart contracts), the Internet of Things, and behavioural and predictive analysis.

Rogue trading

Algorithmic trading has significantly impacted financial markets. Notably, the 2010 Flash Crash wiped $600bn in market value off US stocks in 20 minutes. Market-making firm Knight Capital deployed an untested trading algorithm which went rogue and lost $440m in 30 minutes, destroying the company.

The law

After the 2008 financial crisis, Warren Buffet warned: “Wall Street’s beautifully designed risk algorithms contributed to the mass murder of $22 trillion.”

Trading algorithms are increasingly regulated. Concerns around market manipulation and trading of competitor algorithms has led to some industry practices being banned under legislation such as MiFID II.

Although robo-advisers are registered with the Securities and Exchange Commission, they are not fiduciaries, nor do they fit under the traditional standard applied to human registered investment advisers.

The European ESMA proposed regulatory and technical standards based on existing guidance. High frequency algorithmic traders now have obligations to store records and trading algorithms for at least five years.


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