Data Protection Act and General Data Protection RegulationBig data, artificialintelligence, machinelearning and dataprotection
ContentsInformation Commissioner’s foreword . 3Chapter 1 – Introduction . 5What do we mean by big data, AI and machine learning? . 6What’s different about big data analytics? . 9What are the benefits of big data analytics? . 15Chapter 2 – Data protection implications . 19Fairness . 19Effects of the processing . 20Expectations . 22Transparency . 27Conditions for processing personal data . 29Consent . 29Legitimate interests . 32Contracts . 35Public sector. 35Purpose limitation . 37Data minimisation: collection and retention . 40Accuracy . 43Rights of individuals . 46Subject access. 46Other rights . 47Security . 49Accountability and governance . 51Data controllers and data processors. 56Chapter 3 – Compliance tools . 58Anonymisation . 58Privacy notices . 62Privacy impact assessments . 70Privacy by design . 72Privacy seals and certification. 75Ethical approaches . 77Personal data stores . 84Algorithmic transparency . 86Chapter 4 – Discussion . 90Big data, artificial intelligence, machine learning and data protection20170904Version: 2.2
Chapter 5 – Conclusion . 94Chapter 6 – Key recommendations . 97Annex 1 – Privacy impact assessments for big data analytics. 99Big data, artificial intelligence, machine learning and data protection20170904Version: 2.22
Information Commissioner’s forewordBig data is no fad. Since 2014 when my office’s first paper on this subjectwas published, the application of big data analytics has spread throughoutthe public and private sectors. Almost every day I read news articlesabout its capabilities and the effects it is having, and will have, on ourlives. My home appliances are starting to talk to me, artificially intelligentcomputers are beating professional board-game players and machinelearning algorithms are diagnosing diseases.The fuel propelling all these advances is big data – vast and disparatedatasets that are constantly and rapidly being added to. And what exactlymakes up these datasets? Well, very often it is personal data. The onlineform you filled in for that car insurance quote. The statistics your fitnesstracker generated from a run. The sensors you passed when walking intothe local shopping centre. The social-media postings you made last week.The list goes on So it’s clear that the use of big data has implications for privacy, dataprotection and the associated rights of individuals – rights that will bestrengthened when the General Data Protection Regulation (GDPR) isimplemented. Under the GDPR, stricter rules will apply to the collectionand use of personal data. In addition to being transparent, organisationswill need to be more accountable for what they do with personal data.This is no different for big data, AI and machine learning.However, implications are not barriers. It is not a case of big data ‘or’data protection, or big data ‘versus’ data protection. That would be thewrong conversation. Privacy is not an end in itself, it is an enabling right.Embedding privacy and data protection into big data analytics enables notonly societal benefits such as dignity, personality and community, butalso organisational benefits like creativity, innovation and trust. In short,it enables big data to do all the good things it can do. Yet that’s not to saysomeone shouldn’t be there to hold big data to account.In this world of big data, AI and machine learning, my office is morerelevant than ever. I oversee legislation that demands fair, accurate andnon-discriminatory use of personal data; legislation that also gives me thepower to conduct audits, order corrective action and issue monetarypenalties. Furthermore, under the GDPR my office will be working hard toimprove standards in the use of personal data through theimplementation of privacy seals and certification schemes. We’re uniquelyplaced to provide the right framework for the regulation of big data, AIand machine learning, and I strongly believe that our efficient, joined-upand co-regulatory approach is exactly what is needed to pull back thecurtain in this space.Big data, artificial intelligence, machine learning and data protection20170904Version: 2.23
So the time is right to update our paper on big data, taking into accountthe advances made in the meantime and the imminent implementation ofthe GDPR. Although this is primarily a discussion paper, I do recognisethe increasing utilisation of big data analytics across all sectors and I hopethat the more practical elements of the paper will be of particular use tothose thinking about, or already involved in, big data.This paper gives a snapshot of the situation as we see it. However, bigdata, AI and machine learning is a fast-moving world and this is far fromthe end of our work in this space. We’ll continue to learn, engage,educate and influence – all the things you’d expect from a relevant andeffective regulator.Elizabeth DenhamInformation CommissionerBig data, artificial intelligence, machine learning and data protection20170904Version: 2.24
Chapter 1 – Introduction1.This discussion paper looks at the implications of big data, artificialintelligence (AI) and machine learning for data protection, andexplains the ICO’s views on these.2.We start by defining big data, AI and machine learning, andidentifying the particular characteristics that differentiate them frommore traditional forms of data processing. After recognising thebenefits that can flow from big data analytics, we analyse the mainimplications for data protection. We then look at some of the toolsand approaches that can help organisations ensure that their big dataprocessing complies with data protection requirements. We alsodiscuss the argument that data protection, as enacted in currentlegislation, does not work for big data analytics, and we highlight theincreasing role of accountability in relation to the more traditionalprinciple of transparency.3.Our main conclusions are that, while data protection can bechallenging in a big data context, the benefits will not be achieved atthe expense of data privacy rights; and meeting data protectionrequirements will benefit both organisations and individuals. After theconclusions we present six key recommendations for organisationsusing big data analytics. Finally, in the paper’s annex we discuss thepracticalities of conducting privacy impact assessments in a big datacontext.4.The paper sets out our views on the issues, but this is intended as acontribution to discussions on big data, AI and machine learning andnot as a guidance document or a code of practice. It is not acomplete guide to the relevant law. We refer to the new EU GeneralData Protection Regulation (GDPR), which will apply from May 2018,where it is relevant to our discussion, but the paper is not a guide tothe GDPR. Organisations should consult our website www.ico.org.ukfor our full suite of data protection guidance.5.This is the second version of the paper, replacing what we publishedin 2014. We received useful feedback on the first version and, inwriting this paper, we have tried to take account of it and newdevelopments. Both versions are based on extensive desk researchand discussions with business, government and other stakeholders.We’re grateful to all who have contributed their views.Big data, artificial intelligence, machine learning and data protection20170904Version: 2.25
What do we mean by big data, AI and machine learning?6.The terms ‘big data’, ‘AI’ and ‘machine learning’ are often usedinterchangeably but there are subtle differences between theconcepts.7.A popular definition of big data, provided by the Gartner IT glossary,is:“ high-volume, high-velocity and high-variety information assetsthat demand cost-effective, innovative forms of informationprocessing for enhanced insight and decision making.”1Big data is therefore often described in terms of the ‘three Vs’ wherevolume relates to massive datasets, velocity relates to real-time dataand variety relates to different sources of data. Recently, some havesuggested that the three Vs definition has become tired throughoveruse2 and that there are multiple forms of big data that do not allshare the same traits3. While there is no unassailable single definitionof big data, we think it is useful to regard it as data which, due toseveral varying characteristics, is difficult to analyse using traditionaldata analysis methods.8.This is where AI comes in. The Government Office for Science’srecently published paper on AI provides a handy introduction thatdefines AI as:“ the analysis of data to model some aspect of the world. Inferencesfrom these models are then used to predict and anticipate possiblefuture events.”41Gartner IT glossary Big data. http://www.gartner.com/it-glossary/big-data Accessed 20June 20162Jackson, Sean. Big data in big numbers - it's time to forget the 'three Vs' and look atreal-world figures. Computing, 18 February ree-vs-and-look-at-real-world-figures Accessed 7 December 2016 Accessed7December 20163Kitchin, Rob and McArdle, Gavin. What makes big data, big data? Exploring theontological characteristics of 26 datasets. Big Data and Society, January-June 2016 vol.3 no. 1. Sage, 17 February 2016.4Government Office for Science. Artificial intelligence: opportunities and implications forthe future of decision making. 9 November 2016.Big data, artificial intelligence, machine learning and data protection20170904Version: 2.26
This may not sound very different from standard methods of dataanalysis. But the difference is that AI programs don’t linearly analysedata in the way they were originally programmed. Instead they learnfrom the data in order to respond intelligently to new data and adapttheir outputs accordingly5. As the Society for the Study of ArtificialIntelligence and Simulation of Behaviour puts it, AI is thereforeultimately about:“ giving computers behaviours which would be thought intelligent inhuman beings.”69.It is this unique ability that means AI can cope with the analysis ofbig data in its varying shapes, sizes and forms. The concept of AI hasexisted for some time, but rapidly increasing computational power (aphenomenon known as Moore’s Law) has led to the point at whichthe application of AI is becoming a practical reality.10. One of the fasting-growing approaches7 by which AI is achieved ismachine learning. iQ, Intel’s tech culture magazine, defines machinelearning as:“ the set of techniques and tools that allow computers to ‘think’ bycreating mathematical algorithms based on accumulated data.”8Broadly speaking, machine learning can be separated into two typesof learning: supervised and unsupervised. In supervised learning,algorithms are developed based on labelled datasets. In this sense,the algorithms have been trained how to map from input to outputby the provision of data with ‘correct’ values already assigned tothem. This initial ‘training’ phase creates models of the world onwhich predictions can then be made in the second ‘prediction’ phase.5The Outlook for Big Data and Artificial Intelligence (AI). IDG Research, 11 November2016 nd-artificial-intelligence-ai/Accessed 7 December 2016.6The Society for the Study of Artificial Intelligence and Simulation of Behaviour. What isArtificial Intelligence. AISB Website. http://www.aisb.org.uk/public-engagement/what-isai Accessed 15 February 20177Bell, Lee. Machine learning versus AI: what's the difference? Wired, 2 December ng-ai-explained Accessed 7 December20168Landau, Deb. Artificial Intelligence and Machine Learning: How Computers Learn. iQ,17 August 2016. achine-learning/Accessed 7 December 2016.Big data, artificial intelligence, m
Artificial intelligence: opportunities and implications for the future of decision making. 9 November 2016. Big data, artificial intelligence, machine learning and data protection 20170904 Version: 2.2 7 This may not sound very different from standard methods of data analysis. But the difference is that AI programs don’t linearly analyse data in the way they were originally programmed .
Artificial Intelligence, Machine Learning, and Deep Learning (AI/ML/DL) F(x) Deep Learning Artificial Intelligence Machine Learning Artificial Intelligence Technique where computer can mimic human behavior Machine Learning Subset of AI techniques which use algorithms to enable machines to learn from data Deep Learning
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