What are tech titans Google, Amazon and Uber agitatingfor to further the marchof machine learning technology and ultimately inject more fuel in the engines oftheir own dominant platforms? Unsurprisingly, theyre after access to data. Lots and lots ofdata.

Specifically,theyre pushing for free and liberal accesstopublicly funded data urgingthat this type ofdata continue to be open by default, andstructured in a way that supports wider use of research data. After all, why pay to acquire data when there are vast troves of publicly funded informationripetobe squeezed for fresh economic gain?

Other itemson thismachine learning advancement wish-list include new open standards for data (including metadata); research study design that hasthe broadest consents that are ethically possible, and a stated desire to rethinkthe notion of consent as a core plankof good data governance to grease the pipe in favor ofdata access and make data holdings fit for purpose inthe AIage.

These suggestionscome in a 125-page report published today by the Royal Society, aka the U.K.s national academy of science, ostensibly aimed at fosteringan environment wheremachine learning technology can flourish in order to unlock mooted productivity gains and economic benefits albeit the question of who, ultimately, benefits as more and more data gets squeezed to give upits preciousinsights is the overarching theme and unansweredquestion here. (Though the supportive presence of voicesfrom three of techs most powerful machine learning deploying platform giants suggests oneanswer.)

Scramble for public data

The report, entitled Machine learning: the power and promise of computers that learn by example, isthe work ofthe Royal Societys working group on machine learning, whose15-strong membership includes employees of three companies currently deploying machine learning at scale:Demis Hassabis, the founder and CEO of Google DeepMind, along with DeepMinds research scientist, Yee Whye Teh; Neil Lawrence, Amazons director of machine learning; and Zoubin Ghahramani, chief scientist at Uber.

The reports top-linerecommendations boil down themore fleshed outconcerns in the meat of itschapters, and end up foregrounding encouragement at greater length than concern, as you might expect froma science academy though the level of concern contained withinits pages is notable nonetheless.

The report recommendationslaudwhat isdescribed asthe U.K.sgood progress in increasing the accessibility of public sector data, urging continued effort towardsa new wave of open data for machine learning by government to enhance the availability and usability of public sector data, and calling forthe government to explore ways of catalysing the safe and rapid delivery of new open standards for data which reflect the needs of machine-driven analytical approaches.

Butan early glancing reference tothe value of strategic datasets does getunpacked in more detail further into the report with therecognition that early access to such valuable troves of publicly funded data couldlock in commercial advantage. (Though you wont find a single use of the word monopoly in the entire document.)

It is necessary to recognise the value of some public sector data. While making such data open can bring benefits, considering how those benefits are distributed is important, they write. As machine learning becomes a more significant force, the ability to access data becomes more important, and those with access can attain a first mover feedback advantage that can be significant. When there is such value at stake, it will be increasingly necessary to manage significant datasets or data sources strategically.

There is noexample of this kind of first mover feedback advantage set outin the report, but you could point to DeepMinds data access partnershipswith the U.K.s National Health Service as a pertinent case study here. Not least as the original data-sharing arrangement that the Google-owned companyreached with the Royal Free NHS Trust in London iscontroversial, having been agreed without patient knowledge or consent, and having scaled significantly in scope from its launchas a starter app hosting an NHS algorithm to (now) an ambitiousplan to build a patient data APIto broker third-partyapp makersaccess to NHS data. Also relevant, but unmentioned: the original DeepMind-Royal Free data-sharing agreement remains under investigation by U.K. data protection watchdogs. (Its worth noting the Royal Society also has a separate working group on data governance thats due to publish a report this summer.)

Instead, the report flagsupthe value of NHS data describing it as one of the UKs key data assets before going on to frame the notion of third-partyaccess to U.K. citizensmedical records as a case of personal privacy vspublic good, suggesting that appropriately controlled access mechanisms couldbe developed to resolve what it dubs this balancing act (again, doing so without mentioning thatDeepMind has already set itself the self-appointed task of developing a controlled access mechanism).

If this balancing act is resolved, and if appropriately controlled access mechanisms can be developed, then there is huge potential for NHS data to be used in ways that will both improve the functioning of the NHS and improve healthcare delivery, they write.

Yet exactly who stands to benefit economically from unlocking valuable healthcare insights from apublicly fundedNHS is not discussed. Though common sense would tell you that Google/DeepMind believes there is aprofitable business to be built off of free access tomillions ofNHS patients health data and the first mover advantage that gives them including the chance to embed themselves into healthcare service delivery via control of an access infrastructure.

In an accompanyingsummary tothereport, a pullquote from another member of the working group, Hermann Hauser, co-founder of Amadeus Capital Partners, talks excitedly about potential transformative opportunities for businessesmaking use of machine learningtech. There are exciting opportunities for machine learning in business, and it will be an important tool to help organisations make use of their and other data, he is quoted as saying. To achieve these potentially significant economic benefits, businesses will need to be able to access the right skills at different levels.

The phrase economic benefits is at least mentioned here. But the raison detre of investorsis to achievea good exit. And there has been a rash of exits of machine learning firms to big tech giants engaged in the war for AItalent. DeepMind selling to Google for more than $500 millionin 2014 being just one example.Soinvestors have theirown dog in thefightfor a less stringent public sector data governance regime and still get to cash out if an AI startup they bet on sells to a tech giant, rather than scalesinto one itself.

Julia Powles, a tech law and policy researcher at Cornell Tech makes short shrift of thenotionthat lots of entrepreneurs stand to benefit if the public sector data floodgates are opened.The idea that small guys can make use of their data is just a ruse. Its only the big that will profit, she tells TechCrunch.

Seismic shifts

Another portion ofthe report spends a lot of timeapparently concerned with skills discussing ways thegovernment couldencourage a strong pipeline of practitioners in machine learning, as it puts it including urging it to makemachine learning a priority area for additional PhD places, and to make near-term funding available for 1,000 extra PhDs (or more). Machine learning PhDs are of course top of the hiring treefor big tech giants that have the most cash to suck up these highly prizedrecruits, keeping them from being hired by startups, or indeed from starting their own competing businesses. So any increase at the top academic tier will beGoogle et als gain, first and foremost more so if the public sector also paid to fund these extra PhD places.

The skills discussion (which includes suggestions to tweak school curriculum to includemachine learning over the next five years) has tolater be weighed against another portionof the report considering the potential impact of AIon jobs. Here the report cannot avoid the conclusion that machine learning will at the very least change work and may well lead to seismic shifts in the employment prospects for large swathes of the workforce, which could also, the authors recognize, increasesocietal inequality. All of which does rather undermine the earliersuggestion that everyone in society will be able toupskill for a machine learning-drivenfuture, given you cant acquireskillsfor jobs that dont exist So the risk of AI generating a drastically asymmetric wealth and employmentoutcome is bothfirmlylodged in the reports vision of future work yet alsokicked into ano mans landof collective (i.e. zero ownership) responsibility.

The potential benefits accruing from machine learning and their possibly significant consequences for employment need active management, they write. Without such stewardship, there is a risk that the benefits of machine learning may accrue to a small number of people, with others left behind, or otherwise disadvantaged by changes to society.

While it is not yet clear how potential changes to the world of work might look, active consideration is needed now about how society can ensure that the increased use of machine learning is not accompanied by increased inequality and increased disaffection amongst certain groups. Thinking about how the benefits of machine learning can be shared by all is a key challenge for all of society.

Ultimately, the report does call forurgent consideration to be given to what it describes as the careful stewardship needed over the next ten years to ensure that the dividends from machine learning benefit all in UK society. And its true to say, as weve said before, that policymakers and regulators do need to step up and start buildingframeworks and determining rules to ensure machine learning technologists do not have the chance to asset strip the public sectors crown jewels before theyve even been valued (not to mention leavefuture citizens unable to pay for the fancy services thatwill then be sold back to them, powered by machine learning modelsfreely fatted up on publicly funded data).

But the suggested 10-year time frame seems disingenuous, to put it mildly. With for instance very large quantitiesof sensitive NHS data already flowing from the public sector into the hands of one of the worlds most market capitalized companies (Alphabet/Google/DeepMind) there would seemto be rather more short-term urgency for policymakers to address this issue not leave it on the back burner for a decade or so. Indeed, parliamentarians have already been urging action on AI-related concernslike algorithmic accountability.

Perception and ethics

Public opinion is understandablya big preoccupation for the report authors unsurprisingly so, given thata technology that potentially erodespeoples privacyandimpacts theirjobs risks beingdrastically unpopular. The Royal Society conducteda public poll on machine learning for the report, and say they found mixed views among Brits. Concernsapparently included depersonalisation, or machine learning systems replacing valued human experiences; the potential impact of machine learning on employment; the potential for machine learning systems to cause harm, for example accidents in autonomous vehicles; and machine learning systems restricting choice, such as when directing consumers to specific products and services.

Ongoing public confidence will be central to realising the benefits that machine learning promises, and continued engagement between machine learning researchers and practitioners and the public will be important as the field develops, they add.

The report suggests that large-scale machine learning research programs should include funding for public engagement activities. So there may at least, in the short term, be jobs for PR/marketing types to put a good spin on the societal benefits of automation. They also call forethics to be taught as part of postgraduate study so thatmachine learning researchers are given strong grounding in the broader societal implications of their work. Which is a timelyreminder that most of the machine learning tech already deployed in the wild, including commercially, has probably been engineered and implementedby minds lacking such a strong ethical grounding. (Not that we really need reminding.)

Society needs to give urgent consideration to the ways in which the benefits from machine learning can be shared across society,the report concludes. Which is another way of sayingthat machine learning risks concentrating wealth and power in the hands of a tiny number of massively powerful companies and individuals at societys expense. Whichever way you put it, theres plenty of food for thought here.

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