Paying Users for Their Data Would Exacerbate Digital Inequality


Blog post by Eline Chivot: “Writing ever more complicated and intrusive regulations rules about data processing and data use has become the new fad in policymaking. Many are lending an ear to tempting yet ill-advised proposals to treat personal data as traditional finite resource. The latest example can be found in an article, A Blueprint for a Better Digital Society, by Glen Weyl, an economist at Microsoft Research, and Jaron Lanier, a computer scientist and writer. Not content with Internet users being able to access many online services like Bing and Twitter for free, they want online users to be paid in cash for the data they provide. To say that this proposal is flawed is an understatement. Its flawed for three main reasons: 1) consumers would lose significant shared value in exchange for minimal cash compensation; 2) higher incomes individuals would benefit at the expense of the poor; and 3) transaction costs would increase substantially, further reducing value for consumers and limiting opportunities for businesses to innovate with the data.

Weyl and Lanier’s argument is motivated by the belief that because Internet users are getting so many valuable services—like search, email, maps, and social networking—for free, they must be paying with their data. Therefore, they argue, if users are paying with their data, they should get something in return. Never mind that they do get something in return: valuable digital services that they do not pay for monetarily. But Weyl and Lanier say this is not enough, and consumers should get more.

While this idea may sound good on paper, in practice, it would be a disaster.

…Weyl and Lanier’s self-declared objective is to ensure digital dignity, but in practice this proposal would disrupt the equal treatment users receive from digital services today by valuing users based on their net worth. In this techno-socialist nirvana, to paraphrase Orwell, some pigs would be more equal than others. The French Data Protection Authority, CNIL, itself raised concerns about treating data as a commodity, warning that doing so would jeopardize society’s humanist values and fundamental rights which are, in essence, priceless.

To ensure “a better digital society,” companies should continue to be allowed to decide the best Internet business models based on what consumers demand. Data is neither cash nor a commodity, and pursuing policies based on this misconception will damage the digital economy and make the lives of digital consumers considerably worse….(More)”.

Beyond the IRB: Towards a typology of research ethics in applied economics


Paper by Michler, Jeffrey D., Masters, William A. and Josephson, Anna: “Conversations about ethics often appeal to those responsible for the ethical behavior, encouraging adoption of “better,” more ethical conduct. In this paper, we consider an alternative frame: a typology of ethical misconduct, focusing on who are the victims of various types of unethical behavior. The typology is constructed around 1) who may be harmed and 2) by what mechanism an individual or party is harmed. Building a typology helps to identify times in the life cycle of a research idea where differences exist between who is potentially harmed and who the existing ethical norms protect.

We discuss ethical practices including IRB approvals, which focuses almost entirely on risks to subjects; pre-analysis plans and conflict of interest disclosures, which encourage transparency so as to not mislead editors, reviewers, and readers; and self-plagiarism, which has become increasing common as authors slice their research ever more thinly, causing congestion in journals at the expense of others….(More)”.

Seven design principles for using blockchain for social impact


Stefaan Verhulst at Apolitical: “2018 will probably be remembered as the bust of the blockchain hype. Yet even as crypto currencies continue to sink in value and popular interest, the potential of using blockchain technologies to achieve social ends remains important to consider but poorly understood.

In 2019, business will continue to explore blockchain for sectors as disparate as finance, agriculture, logistics and healthcare. Policymakers and social innovators should also leverage 2019 to become more sophisticated about blockchain’s real promise, limitations  and current practice.

In a recent report I prepared with Andrew Young, with the support of the Rockefeller Foundation, we looked at the potential risks and challenges of using blockchain for social change — or “Blockchan.ge.” A number of implementations and platforms are already demonstrating potential social impact.

The technology is now being used to address issues as varied as homelessness in New York City, the Rohingya crisis in Myanmar and government corruption around the world.

In an illustration of the breadth of current experimentation, Stanford’s Center for Social Innovation recently analysed and mapped nearly 200 organisations and projects trying to create positive social change using blockchain. Likewise, the GovLab is developing a mapping of blockchange implementations across regions and topic areas; it currently contains 60 entries.

All these examples provide impressive — and hopeful — proof of concept. Yet despite the very clear potential of blockchain, there has been little systematic analysis. For what types of social impact is it best suited? Under what conditions is it most likely to lead to real social change? What challenges does blockchain face, what risks does it pose and how should these be confronted and mitigated?

These are just some of the questions our report, which builds its analysis on 10 case studies assembled through original research, seeks to address.

While the report is focused on identity management, it contains a number of lessons and insights that are applicable more generally to the subject of blockchange.

In particular, it contains seven design principles that can guide individuals or organisations considering the use of blockchain for social impact. We call these the Genesis principles, and they are outlined at the end of this article…(More)”.

The Rise of Knowledge Economics


Cesar Hidalgo at Scientific American: “Nearly 30 years ago, Paul Romer published a paper exploring the economic value of knowledge. In that paper, he argued that, unlike the classical factors of production (capital and labor), knowledge was a “non-rival good.” This meant that it could be shared infinitely, and thus, it was the only thing that could grow in per-capita terms.

Romer’s work was recently recognized with the Nobel Prize, even though it was just the beginning of a longer story. Knowledge could be infinitely shared, but did that mean it could go everywhere? Soon after Romer’s seminal paper, Adam Jaffe, Manuel Trajtenberg and Rebecca Henderson published a paper on the geographic diffusion of knowledge. Using a statistical technique called matching, they identified a “twin” for each patent (that is, a patent filed at the same time and making similar technological claims).

Then, they compared the citations received by each patent and its twin. Compared to their twins, patents received almost four more citations from other patents originating in the same city than those originating elsewhere. Romer was right in that knowledge could be infinitely shared, but also, knowledge had difficulties travelling far….

What will the study of knowledge bring us next? Will we get to a point at which we will measure Gross Domestic Knowledge as accurately as we measure Gross Domestic Product? Will we learn how to engineer knowledge diffusion? Will knowledge continue to concentrate in cities? Or will it finally break the shackles of society and spread to every corner of the world? The only thing we know for sure is that the study of knowledge is an exciting journey. The lowest hanging fruit may have already been picked, but the tree is still filled with fruits and flavors. Let’s climb it and explore….(More)”

Beyond GDP: Measuring What Counts for Economic and Social Performance


OECD Book: “Metrics matter for policy and policy matters for well-being. In this report, the co-chairs of the OECD-hosted High Level Expert Group on the Measurement of Economic Performance and Social Progress, Joseph E. Stiglitz, Jean-Paul Fitoussi and Martine Durand, show how over-reliance on GDP as the yardstick of economic performance misled policy makers who did not see the 2008 crisis coming. When the crisis did hit, concentrating on the wrong indicators meant that governments made inadequate policy choices, with severe and long-lasting consequences for many people.

While GDP is the most well-known, and most powerful economic indicator, it can’t tell us everything we need to know about the health of countries and societies. In fact, it can’t even tell us everything we need to know about economic performance. We need to develop dashboards of indicators that reveal who is benefitting from growth, whether that growth is environmentally sustainable, how people feel about their lives, what factors contribute to an individual’s or a country’s success. This book looks at progress made over the past 10 years in collecting well-being data, and in using them to inform policies. An accompanying volume, For Good Measure: Advancing Research on Well-being Metrics Beyond GDP, presents the latest findings from leading economists and statisticians on selected issues within the broader agenda on defining and measuring well-being….(More)”

Artificial Intelligence: Public-Private Partnerships join forces to boost AI progress in Europe


European Commission Press Release: “…the Big Data Value Association and euRobotics agreed to cooperate more in order to boost the advancement of artificial intelligence’s (AI) in Europe. Both associations want to strengthen their collaboration on AI in the future. Specifically by:

  • Working together to boost European AI, building on existing industrial and research communities and on results of the Big Data Value PPP and SPARC PPP. This to contribute to the European Commission’s ambitious approach to AI, backed up with a drastic increase investment, reaching €20 billion total public and private funding in Europe until 2020.
  • Enabling joint-pilots, for example, to accelerate the use and integration of big data, robotics and AI technologies in different sectors and society as a whole
  • Exchanging best practices and approaches from existing and future projects of the Big Data PPP and the SPARC PPP
  • Contributing to the European Digital Single Market, developing strategic roadmaps and  position papers

This Memorandum of Understanding between the PPPs follows the European Commission’s approach to AI presented in April 2018 and the Declaration of Cooperation on Artificial Intelligence signed by all 28 Member States and Norway. This Friday 7 December the Commission will present its EU coordinated plan….(More)”.