Stefaan Verhulst
Patrick Leblond at The Conversation: “…Traditional antitrust approaches such as breaking up Big Tech firms and preventing potential competitor acquisitions are never-ending processes. Even if you break them up and block their ability to acquire other, smaller tech firms, Big Tech will start growing again because of network effects and their data advantage.
And how do we know when a tech firm is big enough to ensure competitive markets? What are the size or scope thresholds for breaking up firms or blocking mergers and acquisitions?
A small startup acquired for millions of dollars can be worth billions of dollars for a Big Tech acquirer once integrated in its ecosystem. A series of small acquisitions can result in a dominant position in one area of the digital economy. Knowing this, competition/antitrust authorities would potentially have to examine every tech transaction, however small.
Not only would this be administratively costly or burdensome on resources, but it would also be difficult for government officials to assess with some precision (and therefore legitimacy), the likely future economic impact of an acquisition in a rapidly evolving technological environment.
Open data access, level the playing field
Given that mass data collection is at the core of Big Tech’s power as gatekeepers to customers, a key solution is to open up data access for other firms so that they can compete better.
Anonymized data (to protect an individual’s privacy rights) about people’s behaviour, interests, views, etc., should be made available for free to anyone wanting to pursue a commercial or non-commercial endeavour. Data about a firm’s operations or performance would, however, remain private.
Using an analogy from the finance world, Big Tech firms act as insider traders. Stock market insiders often possess insider (or private) information about companies that the public does not have. Such individuals then have an incentive to profit by buying or selling shares in those companies before the public becomes aware of the information.
Big Tech’s incentives are no different than stock market insiders. They trade on exclusively available private information (data) to generate extraordinary profits.
Continuing the finance analogy, financial securities regulators forbid the use of inside or non-publicly available information for personal benefit. Individuals found to illegally use such information are punished with jail time and fines.
They also require companies to publicly report relevant information that affects or could significantly affect their performance. Finally, they oblige insiders to publicly report when they buy and sell shares in a company in which they have access to privileged information.
Transposing stock market insider trading regulation to Big Tech implies that data access and use should be monitored under an independent regulatory body — call it a Data Market Authority. Such a body would be responsible for setting and enforcing principles, rules and standards of behaviour among individuals and organizations in the data-driven economy.
For example, a Data Market Authority would require firms to publicly report how they acquire and use personal data. It would prohibit personal data hoarding by ensuring that data is easily portable from one platform, network or marketplace to another. It would also prohibit the buying and selling of personal data as well as protect individuals’ privacy by imposing penalties on firms and individuals in cases of non-compliance.
Data openly and freely available under a strict regulatory environment would likely be a better way to tame Big Tech’s power than breaking them up and having antitrust authorities approving every acquisition that they wish to make….(More)”.
Graham Smith at Involve: “As part of the “A democratic response to COVID-19” project, we have been scanning print and social media to get a sense of how arguments for participation and deliberation are resonating in public debates….
Researchers from the Institute for Development Studies point to learning from previous pandemics. Drawing from their experience of working on the ebola epidemic in West Africa, they argue that pandemics are not just technical problems to be solved, but are social in character. They call for more deliberation and participation to ensure that decisions reflect not only the diversity of expert opinion, but also respond to the experiential knowledge of the most vulnerable….
A number of these proposals call for citizens’ assemblies, perhaps to the detriment of other participatory and deliberative processes. The Carnegie Trust offers a broader agenda, reminding us of the pressing contemporary significance of their pre-COVID-19 calls for co-design and co-production.
The Nuffield Council offers some simple guidance to government about how to act:
- Show us (the public) what it is doing and thinking across the range of issues of concern
- Set out the ethical considerations that inform(ed) its judgements
- Explain how it has arrived at decisions (including taking advice from e.g. SAGE, MEAG), and not that it is just ‘following the science’
- Invite a broad range of perspectives into the room, including wider public representation
- Think ahead – consult and engage other civic interests
We have found only a small number of examples of specific initiatives taking a participatory or deliberative approach to bringing in a broader range of voices in response to the pandemic. Our Covid Voices is gathering written statements of the experience of COVID-19 from those with health conditions or disabilities. The thinktank Demos is running a ‘People’s Commission’, inviting stories of lockdown life. It is not only reflections or stories. The Scottish Government invited ideas on how to tackle the virus, receiving and synthesising 4,000 suggestions. The West Midlands Combined Authority has established a citizens’ panel to guide its recovery work. The UK Citizens’ Assembly (and the French Convention) produced recommendations on how commitments to reach net zero carbon emissions need to be central to a post-COVID-19 recovery. We are sure that these examples only touch the surface of activity and that there will be many more initiatives that we are yet to hear about.
Of course, in one area, citizens have already taken matters into their own hands, with the huge growth in mutual-aid groups to ensure people’s emergency needs are met. The New Local Government Network has considered how public authorities could best support and work with such groups, and Danny Kruger MP was invited by the Prime Minister to investigate how to build on this community-level response.
The call for a more participatory and deliberative approach to governance needs to be more than a niche concern. As the Financial Times recognises, we need a “new civic contract” between government and the people….(More)”.
Alice Moseley in the Oxford Research Encyclopedia of Politics: “Nudging” in public policy involves using behavioral, economic, and psychological insights to influence the behavior of policy targets in order to help achieve policy goals. This approach to public policy was advocated by Thaler and Sunstein in their book Nudge in 2008. Nudging is underpinned by a conception that individuals use mental shortcuts (heuristics) in day-to-day decision-making, shortcuts that do not always serve their long-term interests (for instance, in relation to eating and exercise patterns, road safety, or saving for the future). Nudging does not involve seeking to persuade individuals about the merits of pursuing particular courses of action that will better serve their long-term welfare. Rather, it involves altering the choice environment so that when people follow their instincts, using familiar mental shortcuts, the most prominent option available to the policy target will be one that is likely to promote their own welfare, and that of society more widely. Nudging has come to be considered a core part of the policy toolkit in many countries but academic scholarship has also debated the ethical dimensions of nudging, and there is a flourishing research literature on the efficacy, public acceptability, merits, and limitations of this approach within public policy….(More)”.
Paper by Gaurav Bhardwaj et al: “Today, over 4 billion people around the world—more than half the global population—live in cities. By 2050, with the urban population more than doubling its current size, nearly 7 of 10 people in the world will live in cities. Evidence from today’s developed countries and rapidly emerging economies shows that urbanization and the development of cities is a source of dynamism that can lead to enhanced productivity. In fact, no country in the industrial age has ever achieved significant economic growth without urbanization.
The underlying driver of this dynamism is the ability of cities to bring people together. Social and economic interactions are the hallmark of city life, making people more productive and often creating a vibrant market for innovations by entrepreneurs and investors. International evidence suggests that the elasticity of income per capita with respect to city population is between 3% and 8% (Rosenthal & Strange 2003). Each doubling of city size raises its productivity by 5%.
But the coronavirus pandemic is now seriously limiting social interactions. With no vaccine available, prevention through containment and social distancing, along with frequent handwashing, appear to be, for now, the only viable strategies against the virus. The goal is to slow transmission and avoid overwhelming health systems that have finite resources. Hence non-essential businesses have been closed and social distancing measures, including lockdowns, are being applied in many countries. Will such measures defeat the virus in dense urban areas? In principle, yes. Wealthier people in dense neighborhoods can isolate themselves while having amenities and groceries delivered to them. Many can connect remotely to work, and some can even afford to live without working for a time. But poorer residents of crowded neighborhoods cannot afford such luxuries.
To help city leaders prioritize resources towards places with the highest exposure and contagion risk, we have developed a simple methodology that can be rapidly deployed. This methodology identifies hotspots for contagion and vulnerability, based on:
– The practical inability for keeping people apart, based on a combination of population density and livable floor space that does not allow for 2 meters of physical distancing.
– Conditions where, even under lockdown, people might have little option but to cluster (e.g., to access public toilets and water pumps)…(More)”.
Book by Gary Hamel and Michele Zanini: “In the age of upheaval, top-down power structures and rule-choked management systems are a liability. They crush creativity and stifle initiative. As leaders, employees, investors and citizens, we deserve better. We need organizations that are bold, entrepreneurial and as nimble as change itself. Hence this book.
In Humanocracy, Gary Hamel and Michele Zanini make a passionate, data-driven argument for excising bureaucracy and replacing it with something better. Drawing on more than a decade of research, and packed with practical examples, Humanocracy lays out a detailed blueprint for creating organizations that are as inspired and ingenious as the human beings inside them….(More).
Report by the IPPR (UK): ” There are, today, almost no parts of life that are untouched by the presence of data. Virtually every action we take produces some form of digital trail – our phones track our locations, our browsers track searches, our social network apps log our friends and family – even when we are only dimly aware of it.
It is the combination of this near-ubiquitous gathering of data with fast processing that has generated the economic and social transformation of the last few years – one that, if current developments in artificial intelligence (AI) continue, is only likely to accelerate. Combined with data-enabled technology, from the internet of things to 3D printing, we are potentially on the cusp of a radically different economy and society.
As the world emerges from the first phase of the pandemic, the demands for a socially just and sustainable recovery have grown. The data economy can and should be an essential part of that reconstruction, from the efficient management of energy systems to providing greater flexibility in working time. However, without effective public policy, and democratic oversight and management, the danger is that the tendencies in the data economy that we have already seen towards monopoly and opacity – reinforced, so far, by the crisis – will continue to dominate. It is essential, then, that planning for a fairer, more sustainable economy in the future build in active public policy for data…
This report focusses closely on data as the fundamental building block of the emerging economy, and argues that its use, management, ownership, and control as critical to shaping the future…(More)”.
Blog by Carlos Santiso: “The COVID-19 crisis is putting our global digital resilience to the test. It has revealed the importance of a country’s digital infrastructure as the backbone of the economy, not just as an enabler of the tech economy. Digitally advanced governments, such as Estonia, have been able to put their entire bureaucracies in remote mode in a matter of days, without major disruption. And some early evidence even suggests that their productivity increased during lockdown.
With the crisis, the costs of not going digital have largely surpassed the risks of doing so. Countries and cities lagging behind have realised the necessity to boost their digital resilience and accelerate their digital transformation. Spain, for example, adopted an ambitious plan to inject 70 billion euro into in its digital transformation over the next five years, with a Digital Spain 2025 agenda comprising 10 priorities and 48 measures. In the case of Brazil, the country was already taking steps towards the digital transformation of its public sector before the COVID-19 crisis hit. The crisis is accelerating this transformation.
The great accelerator
Long before the crisis hit, the data-driven digital revolution has been challenging governments to modernise and become more agile, open and responsive. Progress has nevertheless been uneven, hindered by a variety of factors, from political resistance to budget constraints. Going digital requires the sort of whole-of government reforms that need political muscle and long-term vision to break-up traditional data silos within bureaucracies, jealous to preserve their power. In bureaucracies, information is power. Now, information has become ubiquitous and governing data, a critical challenge.
Cutting red tape will be central to the recovery. Many governments are fast-tracking regulatory simplification and administrative streamlining to reboot hard-hit economic sectors. Digitalisation is resetting the relationship between states and citizens, a Copernican revolution for our rule-based bureaucracies….(More)“.
Blog by Ed Humpherson at Data & Policy: “At the Office for Statistics Regulation, thinking about these questions is our day job. We set the standards for Government statistics and data through our Code of Practice for Statistics. And we review how Government departments are living up to these standards when they publish data and statistics. We routinely look at Government statistics are used in public debate.
Based on this, I would propose four factors that ensure that new data sources and tools serve the public good. They do so when:
- When data quality is properly tested and understood:
As my colleague Penny Babb wrote recently in a blog: “‘Don’t trust the data. If you’ve found something interesting, something has probably gone wrong!”. People who work routinely with data develop a sort of innate scepticism, which Penny’s blog captures neatly. Understanding the limitations of both the data, and the inferences you make about the data, are the starting point for any appropriate role for data and policy. Accepting results and insights from new data at face value is a mistake. Much better to test the quality, explore the risks of mistakes, and only then to share findings and conclusions.
2. When the risks of misleadingness are considered:
At OSR, we have an approach to misleadingness that focuses on whether a misuse of data might lead a listener to a wrong conclusion. In fact, by “wrong” we don’t mean in some absolute sense of objective truth; more that if they received the data presented in a different and more faithful way, they would change their mind. Here’s a really simple example: someone might hear that, of two neighbouring countries, one has a much lower fatality rate, when comparing deaths to positive tests for Covid-19. …
3. When the data fill gaps
Data gaps come in several forms. One gap, highlighted by the interest in real-time economic indicators, is timing. Economic statistics don’t really tell us what’s going on right now. Figures like GDP, trade and inflation tells us about some point in the (admittedly quite) recent past. This is the attraction of the real-time economic indicators, which the Bank of England have drawn on in their decisions during the pandemic. They give policymakers a much more real-time feel by filling in this timing gap.
Other gaps are not about time but about coverage….
4. When the data are available
Perhaps the most important thing for data and policy is to democratise the notion of who the data are for. Data (and policy itself) are not just for decision-making elites. They are a tool to help people make sense of their world, what is going on in their community, helping frame and guide the choices they make.
For this reason, I often instinctively recoil at narratives of data that focus on the usefulness of data to decision-makers. Of course, we are all decision-makers of one kind or another, and data can help us all. But I always suspect that the “data for decision-makers” narrative harbours an assumption that decisions are made by senior, central, expert people, who make decisions on behalf of society; people who are, in the words of the musical Hamilton, in the room where it happens. It’s this implication that I find uncomfortable.
That’s why, during the pandemic, our work at the Office for Statistics Regulation has repeatedly argued that data should be made available. We have published a statement that any management information referred to by a decision maker should be published clearly and openly. We call this equality of access.
We fight for equality of access. We have secured the publication of lots of data — on positive Covid-19 cases in England’s Local Authorities, on Covid-19 in prisons, on antibody testing in Scotland…. and several others.
Data and policy are a powerful mix. They offer huge benefits to society in terms of defining, understanding and solving problems, and thereby in improving lives. We should be pleased that the coming together of data and policy is being sped-up by the pandemic.
But to secure these benefits, we need to focus on four things: quality, misleadingness, gaps, and public availability….(More)”
Paper by Rebecca A. Johnson and Tanina Rostain: “The rise of big data and machine learning is a polarizing force among those studying inequality and the law. Big data and tools like predictive modeling may amplify inequalities in the law, subjecting vulnerable individuals to enhanced surveillance. But these data and tools may also serve an opposite function, shining a spotlight on inequality and subjecting powerful institutions to enhanced oversight. We begin with a typology of the role of big data in inequality and the law. The typology asks questions—Which type of individual or institutional actor holds the data? What problem is the actor trying to use the data to solve?—that help situate the use of big data within existing scholarship on law and inequality. We then highlight the dual uses of big data and computational methods—data for surveillance and data as a spotlight—in three areas of law: rental housing, child welfare, and opioid prescribing. Our review highlights asymmetries where the lack of data infrastructure to measure basic facts about inequality within the law has impeded the spotlight function….(More)”.
Paper by Nikolaus Franke, Kathrin Reinsberger and Philipp Topic: “Self-selection has been portrayed to be one of the core reasons for the stunning success of crowdsourcing. It is widely believed that among the mass of potential problem solvers particularly those individuals decide to participate who have the best problem-solving capabilities with regard to the problem at question. Extant research assumes that this self-selection effect is beneficial based on the premise that self-selecting individuals know more about their capabilities and knowledge than the publisher of the task – which frees the organization from costly and error-prone active search.
However, the effectiveness of this core principle has hardly been analyzed, probably because it is extremely difficult to investigate characteristics of those individuals who self-select out. In a unique research design in which we overcome these difficulties by combining behavioral data from a real crowdsourcing contest with data from a survey and archival data, we find that self-selection is actually working in the right direction. Those with particularly strong problem-solving capabilities tend to self-select into the contest and those with low capabilities tend to self-select out. However, this self-selection effect is much weaker than assumed and thus much potential is being lost. This suggests that much more attention needs to be paid to the early stages of crowdsourcing contests and particularly to those the hitherto almost completely overlooked individuals who could provide great solutions but self-select out.”…(More)”.