Asserting collective ownership rights over data is one of the most fundamental policy issues of our time.

Parminder Jeet Singh


Control and ownership of data by a few global digital corporations has critical implications for the public sector, often making governments subservient to the dictates of private players.

There is an absolute necessity for not just community-owned data, but also public data infrastructures. A society's data and digital intelligence should be treated as a public good.

This article argues that such a public goods perspective provides us with a new point of departure towards a digital economy and society that is just, fair and equitable.

The advent of a digital society is fundamentally transforming our social and economic relationships. The nature of the dominant model of digital society means that global digital corporations – based in the US, and some in China – are dictating terms and shaping the global socio-economic architecture for the new digital era, with very little direction being provided by public policy.

Data is the key resource in this digital economy. The control and ownership of data almost entirely by a few global digital corporations is resulting in deep structural changes in our economy and society, with very critical implications for the public sector.

This piece is the first of a two-part series on how the public sector is positioned in the digital paradigm. It looks at the public sector’s legitimate role in the new digital context and lists important areas for engagement by public sector workers and unions.

This is an excerpt of a report by Public Services International (PSI), Friedrich-Ebert-Stiftung (FES), and IT for Change. You can read the full report here.

Data as Public Good, and the Public Sector

Understanding the nature of the emerging digital economy and society, and recognizing people’s collective rights to their data, can help to determine how society’s very impor­tant new data and digital intelligence based roles should be divided across public, community and private sectors. The objectives of high productivity as well as of fairness and justice need both be addressed in this regard.

The currently dominant models of digital technology, econ­omy, and society were born and developed at a place and time of ascendant neoliberal ideology, namely in the US of the decades of 1990s and 2000s. These models are conse­quently almost entirely ruled by the private sector, with practically no role for the public sector. Given the need for rapid innovation and disruption at the early stages of digi­tal technology application, private sector leadership may have had some justification. But with digital society structures becoming entrenched now, and increasingly dominating all sectors, public sector’s appropriate role in a digital society warrants assessment.

Key digital transportation data being largely in the hands of a few digital corporations,1 some cities in the US have considered handing over practically the entire public transportation sector to private management. 2 Massive AI-based private education projects may push into oblivion the school system as we know it, and along with it also the educational authorities. Corpo­rations holding health data are set to reorganize the health sector supplanting the role of public health systems. Digi­tal corporations are developing smart city projects in which their control over city data converts into de facto undertaking govern­ance of the city.

Not just provision of services, the very acts of public pol­icy-making and governance would soon be impossible without access to society’s digital data. Most of such da­ta currently remains a private resource of digital corpora­tions. They may pro bono share some of their data for purposes of public interest, for example, Facebook’s 'Data for Good' and Uber’s 'Uber Movement' initia­tives. But such sharing obviously happens on the whims and terms of these corporations, and follows their own interests. It can hardly serve as the basis for how public policy and governance are to be undertaken in the digi­tal age.

The key resources of the digital economy – data and digital intelligence – have some inherent features of a 'social commons'.

Let us consider a city that is planning smart traffic man­agement, which will require access to real-time commut­ing data that mostly is only available with Google. Would the city authorities have to beg Google for this data or, as the dominant data economy model becomes mainstream and accepted, have to buy it? Even more likely, they may have to let Google, or some such digital corporation, man­age city traffic services. This will involve monopoly service fees and lock-ins. Leveraging their new position, as the corporation involved gathers ever more city data, it will use it to forever keep improving its services – and increas­ing the fees. Such a situation of irretrievable lock-in and ever-deepening dependence on a private provider for a public service may prima facie look entirely untenable, but that is where we seem to be imminently headed. This traf­fic management example can be extrapolated to every sin­gle area of public sector work, from city planning, com­munity development and welfare services to utilities man­agement, education, health, agriculture support, and more.

The central role of a community's data for a whole range of ser­vices that have traditionally been provided by the public sector points to the immense and indispensable public value of such data. It makes a compelling case for com­munity ownership of this data. Such ownership can enable free access to community data held by private companies whenever needed for purposes of public interest.3 Such an ar­rangement in fact appears absolutely necessary, unless the public sector is soon to – more or less – collapse complete­ly. While data required for directly providing public services can be called as a core public interest need, other kinds of public interests are also relevant. Two such further purpos­es of public interest that require mandated sharing of data are (1) to ensure an open and competitive market for digi­tally intelligent products and services, and (2) to support domestic digital industrialization.4

Is the public sector ready for new data-based roles? An ap­propriate theory about such roles for the public sector, and the enabling policies and laws – like on community data ownership, are certainly needed first. But equally impor­tant are the practical details.

Much of the change and restructuring will take place with­in existing public sector bodies and institutions, like those providing services of transportation, health, education, welfare, etc. These bodies will have to become adept at collecting and curating the required data from their exist­ing activities, as well as privately held data that they will get access to under community data ownership rules. Competencies will have to be developed to convert data into necessary digital intelligence, and use it to provide in­telligent public services (of course with the help of data sci­entists). Considerable skill development and upgrading may be required for public sector workers, including bring­ing in new technical skills. But, at its core, digitalization and datafication of the public sector is not so much of a tech­nical challenge – as often feared – as it is of strategic vision­ing and able management. Public sector workers should be able to adapt to new data-intensive work processes as suc­cessfully they did to computerization in the public sector many years ago.

Some of the required public sector restructuring may be relatively intensive, even if undertaken gradually to accom­modate human and other kinds of costs. Some public-sec­tor roles may indeed become less important in the digital society, but many entirely new ones will emerge.

With industrialization, the public sector acquired the im­portant role of providing key industrial infrastructure. It should be taking up a similar role with regard to digital in­frastructure. If any such new role for the public sector is hardly ever discussed it owes largely to  digital society’s birth and upbringing in a neoliberal environment. Global, vertically integrated digital corporations, spanning several sectors of the economy, internalize what are appropriate infrastructural and public-sector roles. Not only are the new digital infrastructure roles private right from birth – a creeping acquisition of existing public infrastructure roles is also taking place. An illustration of this is private digital cur­rency initiatives like Facebook’s Libra seeking to take over the government role of managing currency as the token of value in economic exchanges.

New digital infrastructure areas range from digital connec­tivity and basic computing facilities to cloud computing and data provisioning.5 The focus here is on data and digital intelligence infrastructures.

As the very basis for intelligent production – of intelligent products and services – data is required for all important digital economy activities. Being in the nature of informa­tion, data is prima facie a non-rival good. Also, as data is combined with other data its value increases dramatically. This makes a case for provision of important data as a com­mon infrastructure to all digital economy actors in any sec­tor.

The current digital economy model, however, is based on exclusive appropriation of society’s data by a few monop­olistic digital corporations. They thus increasingly control the value chains in all sectors. Such exclusive use of the common resource of society’s data is the main reason for increasing concentration of digital power, and to a good extent also of increasing economic and social inequalities. Data sharing, or providing data as a common infrastructure, maximizes the benefits that a society can derive from data. Sufficient open availability of key data is also the sine qua non for a compet­itive digital economy, and for reversing the damage being caused by concentration of digital power in a few hands.

Exclusive use of the common resource of society’s data is the main reason for increasing concentration of digital power, and to a good extent also of increasing economic and social inequalities. Data sharing, or providing data as a common infrastructure, maximizes the benefits that a society can derive from data.

The concept of data infrastructure is drawing increasing at­tention. This differs from the earlier open data movement, which consisted mostly of putting public data out in the open for anyone to use. Key data in different sectors mostly used to be with the public authorities; but today private dig­ital platforms are the biggest holders of such data. Further­more, digital society’s granular and intrusive digital data is of a nature that requires considerable protection against misuse. Such data has to be shared in a regulated and managed manner.6 Data infrastructures are designed for safe sharing of sector-wide data taken from different sources.

Command over AI is the new basis of economic power.7 Various national AI strategies rightly focus on data availa­bility, which requires data sharing.8 They promote institu­tions like data infrastructures, data trusts, data exchanges and data markets, in order to ensure increased access to data for digital economy actors. Although mandated data sharing does get mentioned in some places, these nation­al strategies mostly discuss voluntary data sharing. It is not explained, however, why the biggest collectors of data – digital platform companies – will on their own share or even sell their data when they consider maintaining exclu­sive access to data to be their main business advantage. In pussy-footing the obvious need for mandated data shar­ing, the framers of these national AI strategies seem to be tactically avoiding too direct a confrontation with the dominant political economy of the digital society, backed as it is by the most powerful global economic and political interests. But since effective data access and data sharing lie at the heart of any possibilities for AI and digital industrialisation, this weakness ensures that these AI strategies are doomed to failure in their current forms.9

Data infrastructures are not ordinary optional projects that can provide certain benefits; they constitute the very foundation of a strong domestic digital and AI industry, and ensure its openness and fairness. Data’s privatization and monopolistic appropriation, on the other hand, is at the core of the dominant digital economy model. There is no escaping this paradox; it needs to be squarely addressed and urgently resolved.

Public data infrastructures have to be a key part of the new digital institutional ecologies. Most of them will be directly run by the public sector as a part of existing public departments or agencies in different areas, or will be operated by setting up new cross-sectoral agencies. Some data infrastructures could be managed in partnerships with non-profits or businesses, and others run privately as regulated utilities. Effective regulation for data markets is also required. Public sector capacities need to evolve for all these roles.

Public sector workers will need to work with progressive forces worldwide to shape an alternative model of digital society and digital economy. Such a model will have an appropriate distribution of roles between the public and private sectors, and include effective national digital regulation.

Public data infrastructures in different sectors – commerce, transportation, finance, tourism, agriculture, health, education, the labor market, and so on, are necessary to (1) deliver respective intelligent public services, and (2) robust private sector development, supporting a host of competitive digital businesses in each area. Data infrastructures play a central role in digital industrialization, especially by nurturing domestic businesses. When intelligent products and services are competitively available, and lock-ins made difficult with effective data-portability laws, it enables better distribution of digital power across an economy and society, as well as globally. This can ensure the best value for consumers, and greater bargaining power for workers and other small actors in digital supply chains.

India is developing public data infrastructures in many sectors, ranging from commerce and finance to health, education and agriculture.1 The EU is creating data exchanges in the areas of transport, logistics, and health, and a common database of health images to support AI applications in healthcare. Similar initiatives are cropping up all over the world. Public data infrastructures will in time further specialize and evolve to provide not just raw or semi-structured data, but also its higher derivative forms. These could range from structured data and trained AI models to actual AI as a (public) service.10

Striking at the heart of the default dominant model, such a public goods perspective provides us with a new point of departure towards a digital economy and society that is just, fair and equitable. It will take forward the mixed economy and welfare state models that are characterized by the dominant post-war consensus,11 but have been upstaged by the neoliberal assault.12 The latter has employed the cover of rapid digital flux to gain much new territory with respect to society’s systems and institutions. If properly conceptualized, strategized and politicized, the same digital shift can in fact be leveraged to rehabilitate the pre-neoliberal consensus. This is because, the key resources of the digital economy – data and digital intelligence – have some inherent features of a 'social commons'.

What Public Sector Workers Can Do

Most progressive engagement regarding public sector work­ers in the digital society focus on retrenchment due to auto­mation and informalization of work through digital plat­forms. Some attention is also being devoted to data-based surveillance and control over workers. Such a reactive stance to society’s digitalization will need to persist, addressing the 'here and now' of its negative impacts. However, in mid- to long-term strategy, it has to be coupled with a pro-active approach of positively engaging with digital changes. Con­siderable digital change needs to be seen as being largely in­evitable, and potentially useful to the extent that it can en­hance productivity and general social welfare, like the Indus­trial Revolution did in an earlier era. But there is no single necessary design or pathway when it comes to digitization of our societies, even though the Silicon Valley model tries to pose as such.

At the highest level, public sector workers will need to work with progressive forces worldwide to shape an alternative model of digital society and digital economy. Such a model will have an appropriate distribution of roles between the public and private sectors, and include effective national digital regulation. It should be fair to small economic actors, including workers, and lead to a just and equitable society. Such an alternative model is entirely possible, especially in this formative period of a new socio-economic paradigm, as the various tensions and developments discussed in this paper indicate.

At the next level, public sector workers need to engage with not only the general workers’ movement but all 'small actors' in the digital economy – like small enterprises, trad­ers and farmers, who are being squeezed by digital corpo­rations through unilateral appropriation of their data. Col­lective or community rights of data contributors will be the appropriate strategy to pursue by all these groups, who should apply their combined political weight to this end.

And, lastly, closer to home, public sector workers can help develop a new vision and role for the public sector in a dig­ital society, especially in terms of data and digital intelli­gence as public goods. This will help strengthen public ser­vices in every area as they are digitally transformed, instead of weakening them as is currently the case. Some impor­tant new public sector roles, like managing data infrastruc­tures, are also coming about. Shaping a strong new public sector for the digital society requires much more a change in mindset, and ideology, than it does technical expertise and upskilling. The latter are quite manageable if planned well.

References

  1. Uber, for instance, manages and controls the vast transportation ecology in the cities where it operates.
  2. See https://www.theverge.com/2016/6/27/12048482/alphabet-sidewalk-labs-public-transport-columbus-ohio, https://www.ft.com/content/dc111194-2313-11e9-b329-c7e6ceb5ffdfand https://www.opengovasia.com/malaysia-city-brain-initiative-to-use-real-time-anonymised-traffic-data-from-grab/
  3. India’s AI strategy refers to mandatory data sharing for purposes of public interest, and some EU policy documents are also beginning to veer towards this view.
  4. India’s draft e-commerce policy proposes such data sharing with small enterprises.
  5. The EU has infrastructural projects in areas of high-performance computing and low-power micro-processors required for large data and AI applications. https://ec.europa.eu/newsroom/dae/document.cfm?doc_id=56018
  6. 'Open data' is in general useful, with little potential for harm. Digital economy data provides granular intelligence on specific individuals and groups and may carry great potential for harm. It therefore cannot just be made open to anyone and everyone without protections.
  7. Russian President Vladimir Putin has observed that whoever takes the lead in AI will become the ruler of the world. This corresponds to leadership in industrialization in an earlier era.
  8. UK’s AI strategy at https://www.gov.uk/government/publications/ar­tificial-intelligence-sector-deal/ai-sector-deal; India’s at https://www.niti.gov.in/writereaddata/files/document_publication/NationalStrate­gy-for-AI-Discussion-Paper.pdf?utm_source=hrintelligencer; and France’s at https://www.aiforhumanity.fr/pdfs/MissionVillani_Report_ENG-VF.pdf
  9. The paths adopted by the US, as the first starter, and China, which thoroughly fire-walled its nascent digital economy, are generally not available at this stage to other countries for digital industrialization.
  10. 'AI as a service' is an emerging business model. The public sector will need to move away from just using AI applications – which compromises its hold over the value of very important data that passes through its hands – and also specialise in providing some public-infrastructural AI services.
  11. Somewhat arbitrarily, treating communism here as an exception.
  12. Caught on the wrong foot by a bipolar digital world dominated by the US and China, EU leaders are beginning to think aloud in favor of such a middle path political economy for the digital society.

This article is part of our Labor in the Digital Economy series.