UK: Legal Aspects Of Artificial Intelligence (v2.0)

Last Updated: 25 October 2018
Article by Richard Kemp

Since the first version of this white paper in 2016, the range and impact of Artificial Intelligence (AI) has expanded at a dizzying pace as the area continues to capture an ever greater share of the business and popular imaginations. Along with the cloud, AI is emerging as the key driver of the 'fourth industrial revolution', the term (after steam, electricity and computing) coined by Davos founder Klaus Schwab for the deep digital transformation now under way.

This white paper is written from the perspective of the in-house lawyer working on the legal aspects of their organisation's adoption and use of AI.

Download the white paper here.


LEGAL ASPECTS OF ARTIFICIAL INTELLIGENCE (v2.0)1

A. INTRODUCTION

  1. Artificial Intelligence in the mainstream. Since the first version of this white paper in 2016, the range and impact of Artificial Intelligence (AI) has expanded at a dizzying pace as the area continues to capture an ever greater share of the business and popular imaginations. Along with the cloud, AI is emerging as the key driver of the 'fourth industrial revolution', the term (after steam, electricity and computing) coined by Davos founder Klaus Schwab for the deep digital transformation now under way. 2
  1. What is 'Artificial Intelligence'? In 1950, Alan Turing proposed what has become known as the Turing Test for calling a machine intelligent: a machine could be said to think if a human interlocutor could not tell it apart from another human.3 Six years later, at a conference at Dartmouth College, New Hampshire, USA to investigate how machines could simulate intelligence, Professor John McCarthy was credited with introducing the term 'artificial intelligence' as:

'the science and engineering of making intelligent machines, especially intelligent computer programs'.

Textbook definitions vary. One breaks the definition down into two steps, addressing machine intelligence and then the qualities of intelligence:

"artificial intelligence is that activity devoted to making machines intelligent, and intelligence is that quality that enables an entity to function appropriately and with foresight in its environment".4

Another organises the range of definitions into a 2 x 2 matrix of four approaches – thinking humanly, thinking rationally, acting humanly and acting rationally.5

In technical standards, the International Organization for Standardization (ISO) defines AI as an:

"interdisciplinary field ... dealing with models and systems for the performance of functions generally associated with human intelligence, such as reasoning and learning." 6

Most recently, in its January 2018 book, 'The Future: Computed', Microsoft thinks of AI as:

"a set of technologies that enable computers to perceive, learn, reason and assist in decision- making to solve problems in ways that are similar to what people do."7

  1. The technical context. For fifty years after the 1956 Dartmouth conference, AI progressed unevenly. The last decade however has seen rapid progress, driven by growth in data volumes, the rise of the cloud, the refinement of GPUs (graphics processing units) and the development of AI algorithms. This has led to the emergence of a number of separate, related AI technology streams - machine learning, natural language processing (NLP), expert systems, vision, speech, planning and robotics (see Figure 2, para B.8 below).

Although much AI processing takes place between machines, it is in interacting with people that AI particularly resonates, as NLP starts to replace other interfaces and AI algorithms 'learn' how to recognise images ('see') and sounds ('hear' and 'listen'), understand their meaning ('comprehend'), communicate ('speak') and infer sense from context ('reason').

  1. The business context. Many businesses that have not previously used AI proactively in their operations will start to do so in the coming months. Research consultancy Gartner predicts that business value derived from AI will increase by 70% from 2017 to total $1.2tn in 2018, reaching $3.9tn by 2022. By 'business value derived from AI', Gartner means the areas of customer experience, cost reduction and new revenue. Gartner forecasts that up to 2020 growth will be at a faster rate and focus on customer experience (AI to improve customer interaction and increase customer growth and retention). Between 2018 and 2022, "niche solutions that address one need very well, sourced from thousands of narrowly focused, specialist AI suppliers" will make the running. Cost reduction (AI to increase process efficiency, improve decision making and automate tasks) and new revenue and growth opportunities from AI will then be the biggest drivers further out.8
  1. The legal, policy and regulatory context. The start point of the legal analysis is the application to AI of developing legal norms around software and data. Here, 'it's only AI when you don't know what it does, then it's just software and data' is a useful heuristic. In legal terms, AI is a combination of software and data. The software (instructions to the computer's processor) is the implementation in code of the AI algorithm (a set of rules to solve a problem). What distinguishes AI from traditional software development is, first, that the algorithm's rules and software implementation may themselves be dynamic and change as the machine learns; and second, the very large datasets that the AI processes (as what was originally called big data). The data is the input data (training, testing and operational datasets); that data as processed by the computer; and the output data (including data derived from the output).

In policy terms, the scale and societal impact of AI distinguish it from earlier generations of software. This is leading governments, industry players, research institutions and other stakeholders to articulate AI ethics principles (around fairness, safety, reliability, privacy, security, inclusiveness, accountability and transparency) and policies that they intend to apply to all their AI activities.

As the rate of AI adoption increases, general legal and regulatory norms – in areas of law like data protection, intellectual property and negligence – and sector specific regulation – in areas of business like healthcare, transport and financial services – will evolve to meet the new requirements.

These rapid developments are leading governments and policy makers around the world to grapple with what AI means for law, policy and regulation and the necessary technical and legal frameworks.9

  1. Scope and aims of this white paper. This white paper is written from the perspective of the in- house lawyer working on the legal aspects of their organisation's adoption and use of AI. It:
    • overviews at Section B the elements and technologies of AI;
    • provides at Section C four case studies that look at technology and market developments in greater depth to give more practical context for the types of legal and regulatory issues that arise and how they may be successfully addressed. The case studies are legal services (C.14 and C.15), connected and autonomous vehicles (C.16 and C.17), smart contracts (C.18 and C.19) and practical scenarios from the automotive, space, banking, logistics, construction, transportation, domestic and healthcare sectors (C.20 and Annex 1);
    • reviews at section D the legal aspects of AI from the standpoints of policy and regulatory approaches (D.23), data protection (D.24), agency law (D.25), contract law (D.26), intellectual property law (D.27 and D.28) and tort law (D.29); and
    • considers at Section E ethics and governance of AI in the organisation (E.30 to E.34Error! Reference source not found.).

Annex 2 is a short glossary of terms used. This paper is general in nature and not legal advice. It is written as at 31 August 2018 and from the perspective of English law.

B. THE TECHNOLOGIES AND STREAMS OF AI

  1. The cloud and AI as twinned convergences: importance of the cloud. Developments in AI have been fuelled by the ability to harness huge tides of digital data. These vast volumes of varied data arriving at velocity are a product of the cloud, shown in Figure 1 below as the convergence of data centres, the internet, mobile and social media. Data centres are the engine rooms of the Cloud, where $1bn+ investments in millions of square feet of space housing over a million servers accommodate current annual growth rates of between 50% and 100% of the three largest cloud service providers (CSPs), AWS (Amazon), Microsoft and Google. Internet, mobile and social media use at scale are in turn driving the cloud: for a global population of 7.6bn in mid-2018, there are currently estimated to be more than 20bn sensors connected to the internet, 5bn mobile users, 4bn internet users and 2.5bn social medial users10. Increasing internet, mobile and social media use is in turn fuelling an explosion in digital data volumes, currently growing at an annual rate of around 40%, or ten times every five years. It is the availability of data at this scale that provides the raw material for AI.

Download >> Legal Aspects Of Artificial Intelligence (v2.0)

Footnotes

1 The main changes in v2.0 are (i) expanding Section B (AI technologies and streams); updating and extending Section C (case studies); (iii) in Section D, adding a new data protection and expanding the IP paragraphs; and (iv) new Section E (ethics and governance). All websites referred to were accessed in September 2018.

2 'The Fourth Industrial Revolution', Klaus Schwab, World Economic Forum, 2016.

3 'Computing Machinery and Intelligence', Alan Turing, Mind, October 1950

4 'The Quest for Artificial Intelligence: A History of Ideas and Achievements', Prof Nils J Nilsson, CUP, 2010.

5 'Artificial Intelligence, A Modern Approach', Stuart Russell and Peter Norvig, Prentice Hall, 3rd Ed 2010, p. 2

6 2382:2015 is the ISO/IEC's core IT vocabulary standard - https://www.iso.org/obp/ui/#iso:std:iso-iec:2382:ed-1:v1:en:term:2123769

7 igence and its role in society', Microsoft, January 2018, p.28 -

https://news.microsoft.com/uploads/2018/01/The-Future-Computed.pdf

8 Lovelock, Research Vice President, Gartner, April 25, 2018 - https://www.gartner.com/newsroom/id/3872933

9 See for example the following recent developments: China: 12 Dec 2017: Ministry of Industry & Information Technology (MIIT), 'Three-Year Action Plan for Promoting Development of a New Generation Artificial Intelligence Industry (2018-2020)' - https://www.newamerica.org/cybersecurity-initiative/digichina/blog/translation-chinese-government-outlines-ai-ambitions-through-2020/.

European Union: 18 April 2018: Commission Report, 'the European Artificial Intelligence landscape' - https://ec.europa.eu/digital-single-market/en/news/european-artificial-intelligence-landscape; 25 April 2018: Commission Communication, 'Artificial Intelligence for Europe' - https://ec.europa.eu/digital-single-market/en/news/communication-artificial-intelligence-europe; 25 April 2018: Commission Staff Working Document, 'Liability for emerging digital technologies' - https://ec.europa.eu/digital-single-market/en/news/european-commission-staff-working-document-liability-emerging-digital-technologies.

Japan: 30 May 2017, Japan Ministry of Economy, Trade and Industry (METI), 'Final Report on the New Industrial Structure Vision' - http://www.meti.go.jp/english/press/2017/0530_003.html.

UK:

USA: 10 May, 2018, 'White House Summit on Artificial Intelligence for American Industry' - https://www.whitehouse.gov/wp-content/uploads/2018/05/Summary-Report-of-White-House-AI-Summit.pdf.

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