{"id":685,"date":"2019-04-11T16:28:56","date_gmt":"2019-04-11T16:28:56","guid":{"rendered":"http:\/\/blogs.sussex.ac.uk\/policy-engagement\/?p=685"},"modified":"2019-04-11T16:28:58","modified_gmt":"2019-04-11T16:28:58","slug":"if-data-are-the-new-oil-how-can-their-value-be-shared-fairly","status":"publish","type":"post","link":"https:\/\/blogs.sussex.ac.uk\/policy-engagement\/if-data-are-the-new-oil-how-can-their-value-be-shared-fairly\/","title":{"rendered":"If data are the \u2018new oil,\u2019 how can their value be shared fairly?"},"content":{"rendered":"\n

Some reflections on the EC High Level Expert Group Report on the impact of digital transformation on labour markets<\/strong><\/p>\n\n\n\n

Professor Maria Savona<\/a><\/strong>
Science Policy Research Unit (SPRU), University of Sussex <\/strong> <\/p>\n\n\n\n

Digital transformations are creating great\nopportunities but also challenges for modern labour markets. Supporting and\nsteering transformations while reducing disruption is a pertinent policy\nchallenge. While discussions must address the technological anxiety and\ndeclining working conditions associated with advances in AI and automation,\nthey should also consider how digital transformations could reduce unemployment\nand underemployment, and increase prosperity and inclusion at the European\nlevel. <\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

To explore possible solutions to these policy\nchallenges, the European Commission in September 2018 convened an expert\ngroup on the impact of the digital transformation on EU labour markets<\/a>.\nThe group included experts from the public and private sector, alongside a few\nacademics. The HLG convened over five monthly meetings that provided\nopportunities to cross-fertilise our own expertise. I was able to share the\nresults of our ESRC SDAI project (TEMPIS)\non the effects of technological innovation on employment and self-employment in\nthe UK\u2019s local labour markets<\/a> and discuss our views on making innovation\nmore inclusive. We were asked to think outside the box, providing ground-breaking\npolicy recommendations based on empirical evidence \u2013 no mean feat! These have\nfed into a final report<\/a>,\nreleased on 8 April 2019 and discussed in Brussels at the High-Level\nConference on the Future of Work.<\/a><\/p>\n\n\n\n\n\n\n\n

Our policy recommendations covered three grand\nareas: <\/p>\n\n\n\n

a) a\nskilled workforce<\/strong>, enabling digital skills and lifelong learning, career\ncounselling and labour market intermediaries that reduce skill mismatches; <\/p>\n\n\n\n

b) new\nlabour relations<\/strong>, that prevent risks to occupational safety and\noccupational health, particularly mental health, equalise the administrative\ntreatment of standard and non-standard work arrangements, and reinvigorating\nsocial dialogue; <\/p>\n\n\n\n

(c) a new\nsocial contract<\/strong>, that ensures neutral social protection, create a Digital\nSingle Window and redistribute the value of digital ownership. <\/p>\n\n\n\n

The nine recommendations are summarized on the expert\ngroup\u2019s home page<\/a>. <\/p>\n\n\n\n

In this blog, I focus on the policy challenges around\nredistributing gains from digital ownership, giving my own perspective on the\nrecommendations we made in our report<\/a>.\n<\/p>\n\n\n\n

What is\nthe value of data?<\/strong><\/h3>\n\n\n\n

So far, the collection and appropriation of data\nby companies has gone largely unquestioned. People willingly provide their\npersonal data in exchange for using an online service, as in a barter. And\nfirms also benefit from collecting their employees\u2019 data, uncompensated.<\/p>\n\n\n\n

But there is an increasing need to consider \u2013 and\nmeasure \u2013 the value of data. Companies\u2019 capabilities to innovate and grow are\nnow determined not only by their investments in R&D, training, engineering,\ndesign and so on, but also their ability to manipulate and learn from the data\nthey are accumulating. Advancements in data development and analytics mean that\nsuch information are increasingly seen and measured as \u2018knowledge-based capital\u2019<\/em> or \u2018intangible assets.\u2019<\/p>\n\n\n\n

For example, the algorithms behind Artificial\nIntelligence (AI) are fed by a constant stream of data generated as workers\ncarry out their tasks. Yet through machine-learning, this AI will eventually\ndevelop the capacity to replace these workers. So not only are they\ncontributing, unrewarded, to this process, but its very progress may ultimately\ncost them their job, as the firm strives to increase its productivity.<\/p>\n\n\n\n

How can this value\nbe distributed fairly?<\/strong><\/h3>\n\n\n\n

Thanks\nto the EU General Data Protection\nRegulation (GDPR),\npeople now have much greater control over the data they release to firms. But\nthere is still no regulation for tracking this immense stock of data, nor an\neasy way to enforce such laws. <\/p>\n\n\n\n

To\naddress this issue, one has to ask:<\/p>\n\n\n\n

\u201cWho\ngives data their value, and who owns (or should own) this value?\u201d <\/p>\n\n\n\n

Is\nit the individuals who generate the information, or \u201cdata capitalists\u201d (who\ninvest in ways to capture, accumulate, analyse and use it)?<\/p>\n\n\n\n

We\ncould think of \u2018Data as Capital\u2019<\/strong> \u2013\nas an asset (albeit an intangible one), the owners of which could be taxed by\nfiscal authorities. Such a taxation scheme could be implemented by new fiscal\ninstitutions operating at a supranational level, given that traditional,\nnational tax bases are being increasingly undermined.<\/p>\n\n\n\n

Or,\nif we think of \u2018Data as Labour,\u2019 <\/strong>those\nwho create and process them could be paid a wage premium. This approach could\nyield a range of advantages, from improving the quality and quantity of data to\nincreasing the productivity of AI systems.<\/p>\n\n\n\n

A\nthird, potentially more inclusive option we identified is to treat \u2018Data as Intellectual Property.\u2019<\/strong> The\nownership of data belongs with those who generate it \u2013 either workers within a\nfirm or <\/em>consumers \/ users of a firm\u2019s\nservices \u2013 and is treated as their intellectual property. If these data are\nthen used by the firm to increase their (intangible) capital stock, this\nownership must be recognised and paid an Intellectual Property Right (IPR).\nThrough this different type of contract (on the basis of IPR rather than labour\ncontract), firms would pay a license to use such data, but that license should\nbe tax-free for the workers \/ consumers. <\/p>\n\n\n\n

This\nthird approach avoids the administrative burden of enforcing a digital tax, or\nshifting digital ownership. It also means that consumers who stop paying a firm\nfor goods or services don\u2019t relinquish their rights to the data they provided\nat the beginning and throughout. And a similar protection would apply to\nworkers, on leaving a firm.<\/p>\n\n\n\n

But\nit requires first and foremost a change in the way we think about the nature\nand value of big data, in a context in which data is considered the \u2018new oil.\u2019\nA crucial distinction must be made: while oil is essentially a source of \u2018rent\u2019\nfor those who stock it, the ownership and subsequently the value of data is\nmuch more diffused. It must not only be tracked, but be recognised at time of\nprovision. <\/p>\n\n\n\n

In\nlight of this, I recommend that the next generation of data regulations should\ninclude, for example, a General Data Tracking Regulation (GDTR) and a General\nData Using Licence Regulation (GDULR). <\/p>\n","protected":false},"excerpt":{"rendered":"

Some reflections on the EC High Level Expert Group Report on the impact of digital transformation on labour markets Professor Maria SavonaScience Policy Research Unit (SPRU), University of Sussex Digital transformations are creating great opportunities but also challenges for modern…<\/span><\/p>\n

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