Stefaan Verhulst

Kevin Kelly at Technium: “…The procession of technological discoveries is inevitable. When the conditions are right — when the necessary web of supporting technology needed for every invention is established — then the next adjacent technological step will emerge as if on cue. If inventor X does not produce it, inventor Y will. The invention of the microphone, the laser, the transistor, the steam turbine, the waterwheel, and the discoveries of oxygen, DNA, and Boolean logic, were all inevitable in roughly the period they appeared. However the particular form of the microphone, its exact circuit, or the specific design of the laser, or the particular materials of the transistor, or the dimensions of the steam turbine, or the peculiar notation of the formula, or the specifics of any invention are not inevitable. Rather they will vary quite widely due to the personality of their finder, the resources at hand, the culture of society they are born into, the economics funding the discovery, and the influence of luck and chance. An incandescent light bulb based on a coil of carbonized bamboo filament heated within a vacuum bulb is not inevitable, but “the electric incandescent light bulb” is. The concept of “the electric incandescent light bulb” abstracted from all the details that can vary while still producing the result — luminance from electricity, for instance  —  is ordained by the technium’s trajectory. We know this because “the electric incandescent light bulb” was invented, re-invented, co-invented, or “first invented” dozens of times. In their book “Edison’s Electric Light: Biography of an Invention”, Robert Friedel and Paul Israel list 23 inventors of incandescent bulbs prior to Edison. It might be fairer to say that Edison was the very last “first” inventor of the electric light.

Three independently invented electric light bulbs: Edison’s, Swan’s, and Maxim’s.

Any claim of inevitability is difficult to prove. Convincing proof requires re-running a progression more than once and showing that the outcome is the same each time. That no matter what perturbations thrown at the system, it yields an identical result. To claim that the large-scale trajectory of the technium is inevitable would mean demonstrating that if we re-ran history, the same abstracted inventions would arise again, and in roughly the same relative order.  Without a time machine, there’ll be no indisputable proof, but we do have three types of evidence that suggest that the paths of technologies are inevitable. They are 1) that quantifiable trajectories of progress don’t waver despite attempts to shift them (see my Moore’s Law); 2) that in ancient times when transcontinental communication was slow or null, we find independent timelines of technology in different continents converging upon a set order; and 3) the fact that most inventions and discoveries have been made independently by more than one person….(More)”.

Report by Christina Nemr and William Gangware: “The proliferation of social media platforms has democratized the dissemination and consumption of information, thereby eroding traditional media hierarchies and undercutting claims of authority. In this environment, states and individuals can easily spread disinformation at lightning speed and with serious impact.

Today’s information ecosystem presents significant vulnerabilities that foreign states can exploit, and they revolve around three primary, interconnected elements:

1. The medium – the platforms on which disinformation flourishes;
2. the message – what is being conveyed through disinformation; and,
3. the audience – the consumers of such content.

The problem of disinformation is therefore not one that can be solved through any single solution, whether psychological or technological. An effective response to this challenge requires understanding the converging factors of technology, media, and human behavior.

This interdisciplinary review, commissioned by the United States Department of State’s Global Engagement Center, presents a holistic overview of the disinformation landscape by examining 1) psychological vulnerabilities to disinformation, 2) current foreign state-sponsored disinformation and propaganda efforts both abroad and in the United States, 3) social media companies’ efforts to counter disinformation, and 4) knowledge and technology gaps that remain….(More)”.

Joseph Brean at National Post: “Nova Scotia’s decision to presume people’s consent to donating their organs after death is not just a North American first. It is also the latest example of how deeply behavioural psychology has changed policy debates.

That is a rare achievement for science. Governments used to appeal to people’s sense of reason, religion, civic duty, or fear of consequences. Today, when they want to change how their citizens behave, they use psychological tricks to hack their minds.

Nudge politics, as it came to be known, has been an intellectual hit among wonks and technocrats ever since Daniel Kahneman won the Nobel Prize in 2002 for destroying the belief people make decisions based on good information and reasonable expectations. Not so, he showed. Not even close. Human decision-making is an organic process, all but immune to reason, but strangely susceptible to simple environmental cues, just waiting to be exploited by a clever policymaker….

Organ donation is a natural fit. Nova Scotia’s experiment aims to solve a policy problem by getting people to do what they always tend to do about government requests — nothing.

The cleverness is evident in the N.S. government’s own words, which play on the meaning of “opportunity”: “Every Nova Scotian will have the opportunity to be an organ and tissue donor unless they opt out.” The policy applies to kidneys, pancreas, heart, liver, lungs, small bowel, cornea, sclera, skin, bones, tendons and heart valves.

It is so clever it aims to make progress as people ignore it. The default position is a positive for the policy. It assumes poor pickup. You can opt out of organ donation if you want. Nova Scotia is simply taking the informed gamble that you probably won’t. That is the goal, and it will make for a revealing case study.

Organ donation is an important question, and chronically low donation rates can reasonably be called a crisis. But most people make their personal choice “thoughtlessly,” as Kahneman wrote in the 2011 book Thinking, Fast and Slow.

He referred to European statistics which showed vast differences in organ donation rights between neighbouring and culturally similar countries, such as Sweden and Denmark, or Germany and Austria. The key difference, he noted, was what he called “framing effects,” or how the question was asked….(More)”.

Essay by Guru Madhavan and Charles Phelps: “…Some experimental studies have begun to offer insights into the benefits of making voting methods—and the very goals of voting—more expressive. In the 2007 French presidential election, for instance, people were offered the chance to participate in an experimental ballot that allowed them to use letter grades to evaluate the candidates just as professors evaluate students. This approach, called the “majority judgment,” provides a clear method to combine those grades into rankings or a final winner. But instead of merely selecting a winner, majority judgment conveys—with a greater degree of expressivity—the voters’ evaluations of their choices. In this experiment, people completed their ballots in about a minute, thus allaying potential concerns that a letter grading system was too complicated to use. What’s more, they seemed more enthusiastic about this method. Scholars Michel Balinski and Rida Laraki, who led this study, point out: “Indeed, one of the most effective arguments for persuading reluctant voters to participate was that the majority judgment allows fuller expression of opinion.”

Additional experiments with more expressive ballots have now been repeated across different countries and elections. According to a 2018 summary of these experiments by social choice theorist Annick Laruelle,  “While ranking all candidates appears to be difficult … participants enjoy the possibility of choosing a grade for each candidate … [and] ballots with three grades are preferred to those … with two grades.” Some participant comments are revealing, stating, “With this ballot we can at last vote with the heart,” or, “Voting with this ballot is a relief.” Voters, according to Laruelle, “Enjoyed the option of voting in favor of several candidates and were especially satisfied of being offered the opportunity to vote against candidates.”…

These opportunities for expression might increase public interest in (and engagement with) democratic decision making, encouraging more thoughtful candidate debates, more substantive election campaigns and advertisements, and richer use of opinion polling to help candidates shape their position statements (once they are aware that the public’s selection process has changed). One could even envision that the basis for funding election campaigns might evolve if funders focused on policy ideas rather than political allegiances and specific candidates. Changes such as these would ideally put the power back in the hands of the people, where it actually belongs in a democracy. These conjectures need to be tested and retested across contexts, ideally through field experiments that leverage research and expertise in engineering, social choice, and political and behavioral sciences.

Standard left-to-right political scales and the way we currently vote do not capture the true complexity of our evolving political identities and preferences. If voting is indeed the true instrument of democracy and much more than a repeated political ritual, it must allow for richer expression. Current methods seem to discourage public participation, the very nucleus of civic life. The essence of civility and democracy is not merely about providing issues and options to vote on but in enabling people to fully express their preferences. For a country founded on choice as its tenet, is it too much to ask for a little bit more choice in how we select our leaders? …(More)”.

Report by Geoff Mulgan and Kirsten Bound: “Featured in this compendium are just some of the innovation methods we have explored over the last decade. Some, like seed accelerator programmes, we have invested in and studied. Others, like challenge prizes, standards of evidence or public sector labs, we have developed and helped to spread around the world.

Each section gives a simple introduction to the method and describes Nesta’s work in relation to it. In each case, we have also provided links to further relevant resources and inspiration on our website and beyond.

The 13 methods featured are:

1. Accelerator programmes
2. Anticipatory regulation
3. Challenge prizes
4. Crowdfunding
5. Experimentation
6. Futures
7. Impact investment
8. Innovation mapping
9. People Powered Results: the 100 day challenge
10. Prototyping
11. Public and social innovation labs
12. Scaling grants for social innovations
13. Standards of Evidence…(More)”.

Study by Claude Castelluccia and Daniel Le Métayer for the European Parliament: “While algorithms are hardly a recent invention, they are nevertheless increasingly involved in systems used to support decision-making. These systems, known as ‘ADS’ (algorithmic decision systems), often rely on the analysis of large amounts of personal data to infer correlations or, more generally, to derive information deemed useful to make decisions. Human intervention in the decision-making may vary, and may even be completely out of the loop in entirely automated systems. In many situations, the impact of the decision on people can be significant, such as access to credit, employment, medical treatment, or judicial sentences, among other things.

Entrusting ADS to make or to influence such decisions raises a variety of ethical, political, legal, or technical issues, where great care must be taken to analyse and address them correctly. If they are neglected, the expected benefits of these systems may be negated by a variety of different risks for individuals (discrimination, unfair practices, loss of autonomy, etc.), the economy (unfair practices, limited access to markets, etc.), and society as a whole (manipulation, threat to democracy, etc.).

This study reviews the opportunities and risks related to the use of ADS. It presents policy options to reduce the risks and explain their limitations. We sketch some options to overcome these limitations to be able to benefit from the tremendous possibilities of ADS while limiting the risks related to their use. Beyond providing an up-to date and systematic review of the situation, the study gives a precise definition of a number of key terms and an analysis of their differences to help clarify the debate. The main focus of the study is the technical aspects of ADS. However, to broaden the discussion, other legal, ethical and social dimensions are considered….(More)”.

Paper by the Bipartisan Policy Center: “Emerging privacy-preserving technologies and approaches hold considerable promise for improving data privacy and confidentiality in the 21st century. At the same time, more information is becoming accessible to support evidence-based policymaking.

In 2017, the U.S. Commission on Evidence-Based Policymaking unanimously recommended that further attention be given to the deployment of privacy-preserving data-sharing applications. If these types of applications can be tested and scaled in the near-term, they could vastly improve insights about important policy problems by using disparate datasets. At the same time, the approaches could promote substantial gains in privacy for the American public.

There are numerous ways to engage in privacy-preserving data sharing. This paper primarily focuses on secure computation, which allows information to be accessed securely, guarantees privacy, and permits analysis without making private information available. Three key issues motivated the launch of a domestic secure computation demonstration project using real government-collected data:

• Using new privacy-preserving approaches addresses pressing needs in society. Current widely accepted approaches to managing privacy risks—like preventing the identification of individuals or organizations in public datasets—will become less effective over time. While there are many practices currently in use to keep government-collected data confidential, they do not often incorporate modern developments in computer science, mathematics, and statistics in a timely way. New approaches can enable researchers to combine datasets to improve the capability for insights, without being impeded by traditional concerns about bringing large, identifiable datasets together. In fact, if successful, traditional approaches to combining data for analysis may not be as necessary.
• There are emerging technical applications to deploy certain privacy-preserving approaches in targeted settings. These emerging procedures are increasingly enabling larger-scale testing of privacy-preserving approaches across a variety of policy domains, governmental jurisdictions, and agency settings to demonstrate the privacy guarantees that accompany data access and use.
• Widespread adoption and use by public administrators will only follow meaningful and successful demonstration projects. For example, secure computation approaches are complex and can be difficult to understand for those unfamiliar with their potential. Implementing new privacy-preserving approaches will require thoughtful attention to public policy implications, public opinions, legal restrictions, and other administrative limitations that vary by agency and governmental entity.

This project used real-world government data to illustrate the applicability of secure computation compared to the classic data infrastructure available to some local governments. The project took place in a domestic, non-intelligence setting to increase the salience of potential lessons for public agencies….(More)”.

Essay by Tim Rogan: “Machine learning – a kind of sub-field of artificial intelligence (AI) – is a means of training algorithms to discern empirical relationships within immense reams of data. Run a purpose-built algorithm by a pile of images of moles that might or might not be cancerous. Then show it images of diagnosed melanoma. Using analytical protocols modelled on the neurons of the human brain, in an iterative process of trial and error, the algorithm figures out how to discriminate between cancers and freckles. It can approximate its answers with a specified and steadily increasing degree of certainty, reaching levels of accuracy that surpass human specialists. Similar processes that refine algorithms to recognise or discover patterns in reams of data are now running right across the global economy: medicine, law, tax collection, marketing and research science are among the domains affected. Welcome to the future, say the economist Erik Brynjolfsson and the computer scientist Tom Mitchell: machine learning is about to transform our lives in something like the way that steam engines and then electricity did in the 19th and 20th centuries. 

Signs of this impending change can still be hard to see. Productivity statistics, for instance, remain worryingly unaffected. This lag is consistent with earlier episodes of the advent of new ‘general purpose technologies’. In past cases, technological innovation took decades to prove transformative. But ideas often move ahead of social and political change. Some of the ways in which machine learning might upend the status quo are already becoming apparent in political economy debates.

The discipline of political economy was created to make sense of a world set spinning by steam-powered and then electric industrialisation. Its central question became how best to regulate economic activity. Centralised control by government or industry, or market freedoms – which optimised outcomes? By the end of the 20th century, the answer seemed, emphatically, to be market-based order. But the advent of machine learning is reopening the state vs market debate. Which between state, firm or market is the better means of coordinating supply and demand? Old answers to that question are coming under new scrutiny. In an eye-catching paper in 2017, the economists Binbin Wang and Xiaoyan Li at Sichuan University in China argued that big data and machine learning give centralised planning a new lease of life. The notion that market coordination of supply and demand encompassed more information than any single intelligence could handle would soon be proved false by 21st-century AI.

How seriously should we take such speculations? Might machine learning bring us full-circle in the history of economic thought, to where measures of economic centralisation and control – condemned long ago as dangerous utopian schemes – return, boasting new levels of efficiency, to constitute a new orthodoxy?

A great deal turns on the status of tacit knowledge….(More)”.

Book by Christoph Lütge: “Countering the claims that competition contradicts and undermines ethical thought processes and actions, Christoph Lütge successfully argues that competition and ethics do not necessarily have to oppose one another. He highlights how intensified competition can in fact work in favour of ethical goals, and that many criticisms of competition stem from an outdated understanding of how modern societies and economies function. 

Illustrating this view with examples from ecology, healthcare and education, the author calls for a more entrepreneurial spirit in analysing the relationship between competition and ethics. This book delivers important arguments for the ethics of innovation, using a combination of theoretical and practical evidence to support it.

Researchers and scholars of economics, business, philosophy and politics will greatly benefit from the fresh interdisciplinary perspectives and thorough exploration of the complex relationship between modern competition and ethics….(More)”.

Blog Post by Juan Murillo Arias: “…But in order for data to truly become a lever that foments innovation in benefit of society as a whole, we must understand and address the following factors:

1. Disconnected, disperse sources. As users of digital services (transportation, finance, telecommunications, news or entertainment) we leave a different digital footprint for each service that we use. These footprints, which are different facets of the same polyhedron, can even be contradictory on occasion. For this reason, they must be seen as complementary. Analysts should be aware that they must cross data sources from different origins in order to create a reliable picture of our preferences, otherwise we will be basing decisions on partial or biased information. How many times do we receive advertising for items we have already purchased, or tourist destinations where we have already been? And this is just one example of digital marketing. When scoring financial solvency, or monitoring health, the more complete the digital picture is of the person, the more accurate the diagnosis will be.

Furthermore, from the user’s standpoint, proper management of their entire, disperse digital footprint is a challenge. Perhaps centralized consent would be very beneficial. In the financial world, the PSD2 regulations have already forced banks to open this information to other banks if customers so desire. Fostering competition and facilitating portability is the purpose, but this opening up has also enabled the development of new services of information aggregation that are very useful to financial services users. It would be ideal if this step of breaking down barriers and moving toward a more transparent market took place simultaneously in all sectors in order to avoid possible distortions to competition and by extension, consumer harm. Therefore, customer consent would open the door to building a more accurate picture of our preferences.

2. The public and private sectors’ asymmetric capacity to gather data.This is related to citizens using public services less frequently than private services in the new digital channels. However, governments could benefit from the information possessed by private companies. These anonymous, aggregated data can help to ensure a more dynamic public management. Even personal data could open the door to customized education or healthcare on an individual level. In order to analyze all of this, the European Commissionhas created a working group including 23 experts. The purpose is to come up with a series of recommendations regarding the best legal, technical and economic framework to encourage this information transfer across sectors.

3. The lack of incentives for companies and citizens to encourage the reuse of their data.The reality today is that most companies solely use the sources internally. Only a few have decided to explore data sharing through different models (for academic research or for the development of commercial services). As a result of this and other factors, the public sector largely continues using the survey method to gather information instead of reading the digital footprint citizens produce. Multiple studies have demonstrated that this digital footprint would be useful to describe socioeconomic dynamics and monitor the evolution of official statistical indicators. However, these studies have rarely gone on to become pilot projects due to the lack of incentives for a private company to open up to the public sector, or to society in general, making this new activity sustainable.

4. Limited commitment to the diversification of services.Another barrier is the fact that information based product development is somewhat removed from the type of services that the main data generators (telecommunications, banks, commerce, electricity, transportation, etc.) traditionally provide. Therefore, these data based initiatives are not part of their main business and are more closely tied to companies’ innovation areas where exploratory proofs of concept are often not consolidated as a new line of business.

5. Bidirectionality. Data should also flow from the public sector to the rest of society. The first regulatory framework was created for this purpose. Although it is still very recent (the PSI Directive on the re-use of public sector data was passed in 2013), it is currently being revised, in attempt to foster the consolidation of an open data ecosystem that emanates from the public sector as well. On the one hand it would enable greater transparency, and on the other, the development of solutions to improve multiple fields in which public actors are key, such as the environment, transportation and mobility, health, education, justice and the planning and execution of public works. Special emphasis will be placed on high value data sets, such as statistical or geospatial data — data with tremendous potential to accelerate the emergence of a wide variety of information based data products and services that add value.The Commission will begin working with the Member States to identify these data sets.

In its report, Creating Data through Open Data, the European open data portal estimates that government agencies making their data accessible will inject an extra €65 billion in the EU economy this year.

6. The commitment to analytical training and financial incentives for innovation.They are the key factors that have given rise to the digital unicorns that have emerged, more so in the U.S. and China than in Europe….(More)”