Why Coming Up With Effective Interventions To Address COVID-19 Is So Hard


Article by Neil Lewis Jr.: “It has been hard to measure the effects of the novel coronavirus. Not only is COVID-19 far-reaching — it’s touched nearly every corner of the globe at this point — but its toll on society has also been devastating. It is responsible for the deaths of over 905,000 people around the world, and more than 190,000 people in the United States alone. The associated economic fallout has been crippling. In the U.S., more people lost their jobs in the first three months of the pandemic than in the first two years of the Great Recession. Yes, there are some signs the economy might be recovering, but the truth is, we’re just beginning to understand the pandemic’s full impact, and we don’t yet know what the virus has in store for us.

This is all complicated by the fact that we’re still figuring out how best to combat the pandemic. Without a vaccine readily available, it has been challenging to get people to engage in enough of the behaviors that can help slow the virus. Some policy makers have turned to social and behavioral scientists for guidance, which is encouraging because this doesn’t always happen. We’ve seen many universities ignore the warnings of behavioral scientists and reopen their campuses, only to have to quickly shut them back down.

But this has also meant that there are a lot of new studies to wade through. In the field of psychology alone, between Feb. 10 and Aug. 30, 541 papers about COVID-19 were uploaded to the field’s primary preprint server, PsyArXiv. With so much research to wade through, it’s hard to know what to trust — and I say that as someone who makes a living researching what types of interventions motivate people to change their behaviors.

As I tell my students, if you want to use behavioral science research to address real-world problems, you have to look very closely at the details. Often, a simple question like, “What research should policy makers and practitioners use to help combat the pandemic?” is surprisingly difficult to answer.

For starters, there are often key differences between the lab (or the people and situations some social scientists typically study as part of our day-to-day research) and the real world (or the people and situations policy-makers and practitioners have in mind when crafting interventions).

Take, for example, the fact that social scientists tend to study people from richer countries that are generally highly educated, industrialized, democratic and in the Western hemisphere. And some social scientific fields (e.g., psychologyfocus overwhelmingly on whiter, wealthier and more highly educated groups of people within those nations.

This is a major issue in the social sciences and something that researchers have been talking about for decades. But it’s important to mention now, too, as Black and brown people have been disproportionately affected by the coronavirus — they are dying at much higher rates than white people and working more of the lower-paying “essential” jobs that expose them to greater risks. Here you can start to see very real research limitations creep in: The people whose lives have been most adversely affected by the virus have largely been excluded from the studies that are supposed to help them. When samples and the methods used are not representative of the real world, it becomes very difficult to reach accurate and actionable conclusions….(More)”.

Emerging models of data governance in the age of datafication


Paper by Marina Micheli et al: “The article examines four models of data governance emerging in the current platform society. While major attention is currently given to the dominant model of corporate platforms collecting and economically exploiting massive amounts of personal data, other actors, such as small businesses, public bodies and civic society, take also part in data governance. The article sheds light on four models emerging from the practices of these actors: data sharing pools, data cooperatives, public data trusts and personal data sovereignty. We propose a social science-informed conceptualisation of data governance. Drawing from the notion of data infrastructure we identify the models as a function of the stakeholders’ roles, their interrelationships, articulations of value, and governance principles. Addressing the politics of data, we considered the actors’ competitive struggles for governing data. This conceptualisation brings to the forefront the power relations and multifaceted economic and social interactions within data governance models emerging in an environment mainly dominated by corporate actors. These models highlight that civic society and public bodies are key actors for democratising data governance and redistributing value produced through data. Through the discussion of the models, their underpinning principles and limitations, the article wishes to inform future investigations of socio-technical imaginaries for the governance of data, particularly now that the policy debate around data governance is very active in Europe….(More)”.

Enhancing Digital Equity


Book by Massimo Ragnedda on “Connecting the Digital Underclass…This book highlights how, in principle, digital technologies present an opportunity to reduce social disparities, tackle social exclusion, enhance social and civil rights, and promote equity. However, to achieve these goals, it is necessary to promote digital equity and connect the digital underclass.

The book focuses on how the advent of technologies may become a barrier to social mobility and how, by concentrating resources and wealth in few hands, the digital revolution is giving rise to the digital oligarchy, further penalizing the digital underclass. Socially-disadvantaged people, living at the margins of digital society, are penalized both in terms of accessing-using-benefits (three levels of digital divide) but also in understanding-programming-treatment of new digital technologies (three levels of algorithms divide). The advent and implementation of tools that rely on algorithms to make decisions has further penalized specific social categories by normalizing inequalities in the name of efficiency and rationalization….(More)”.

Coding Democracy


Book by Maureen Webb: “Hackers have a bad reputation, as shady deployers of bots and destroyers of infrastructure. In Coding Democracy, Maureen Webb offers another view. Hackers, she argues, can be vital disruptors. Hacking is becoming a practice, an ethos, and a metaphor for a new wave of activism in which ordinary citizens are inventing new forms of distributed, decentralized democracy for a digital era. Confronted with concentrations of power, mass surveillance, and authoritarianism enabled by new technology, the hacking movement is trying to “build out” democracy into cyberspace.

Webb travels to Berlin, where she visits the Chaos Communication Camp, a flagship event in the hacker world; to Silicon Valley, where she reports on the Apple-FBI case, the significance of Russian troll farms, and the hacking of tractor software by desperate farmers; to Barcelona, to meet the hacker group XNet, which has helped bring nearly 100 prominent Spanish bankers and politicians to justice for their role in the 2008 financial crisis; and to Harvard and MIT, to investigate the institutionalization of hacking. Webb describes an amazing array of hacker experiments that could dramatically change the current political economy. These ambitious hacks aim to displace such tech monoliths as Facebook and Amazon; enable worker cooperatives to kill platforms like Ubergive people control over their data; automate trust; and provide citizens a real say in governance, along with capacity to reach consensus. Coding Democracy is not just another optimistic declaration of technological utopianism; instead, it provides the tools for an urgently needed upgrade of democracy in the digital era….(More)”.

Models and Modeling in the Sciences: A Philosophical Introduction


Book by Stephen M. Downes: “Biologists, climate scientists, and economists all rely on models to move their work forward. In this book, Stephen M. Downes explores the use of models in these and other fields to introduce readers to the various philosophical issues that arise in scientific modeling. Readers learn that paying attention to models plays a crucial role in appraising scientific work. 

This book first presents a wide range of models from a number of different scientific disciplines. After assembling some illustrative examples, Downes demonstrates how models shed light on many perennial issues in philosophy of science and in philosophy in general. Reviewing the range of views on how models represent their targets introduces readers to the key issues in debates on representation, not only in science but in the arts as well. Also, standard epistemological questions are cast in new and interesting ways when readers confront the question, “What makes for a good (or bad) model?”…(More)’.

How Algorithms Can Fight Bias Instead of Entrench It


Essay by Tobias Baer: “…How can we build algorithms that correct for biased data and that live up to the promise of equitable decision-making?

When we consider changing an algorithm to eliminate bias, it is helpful to distinguish what we can change at three different levels (from least to most technical): the decision algorithm, formula inputs, and the formula itself.

In discussing the levels, I will use a fictional example, involving Martians and Zeta Reticulans. I do this because picking a real-life example would, in fact, be stereotyping—I would perpetuate the very biases I try to fight by reiterating a simplified version of the world, and every time I state that a particular group of people is disadvantaged, I also can negatively affect the self-perception of people who consider themselves members of these groups. I do apologize if I unintentionally insult any Martians reading this article!

On the simplest and least technical level, we would adjust only the overall decision algorithm that takes one or more statistical formulas (typically to predict unknown outcomes such as academic success, recidivation, or marital bliss) as an input and applies rules to translate the predictions of these formulas into decisions (e.g., by comparing predictions with externally chosen cutoff values or contextually picking one prediction over another). Such rules can be adjusted without touching the statistical formulas themselves.

An example of such an intervention is called boxing. Imagine you have a score of astrological ability. The astrological ability score is a key criterion for shortlisting candidates for the Interplanetary Economic Forecasting Institute. You would have no objective reason to believe that Martians are any less apt at prognosticating white noise than Zeta Reticulans; however, due to racial prejudice in our galaxy, Martian children tend to get asked a lot less for their opinion and therefore have a lot less practice in gabbing than Zeta Reticulans, and as a result only one percent of Martian applicants achieve the minimum score required to be hired for the Interplanetary Economic Forecasting Institute as compared to three percent of Zeta Reticulans.

Boxing would posit that for hiring decisions to be neutral of race, for each race two percent of applicants should be eligible, and boxing would achieve it by calibrating different cut-off scores (i.e., different implied probabilities of astrological success) for Martians and Zeta Reticulans.

Another example of a level-one adjustment would be to use multiple rank-ordering scores and to admit everyone who achieves a high score on any one of them. This approach is particularly well suited if you have different methods of assessment at your disposal, but each method implies a particular bias against one or more subsegments. An example for a crude version of this approach is admissions to medical school in Germany, where routes include college grades, a qualitative assessment through an interview, and a waitlist….(More)”.

Global citizen deliberation on genome editing


Essay by John S. Dryzek et al at Science: “Genome editing technologies provide vast possibilities for societal benefit, but also substantial risks and ethical challenges. Governance and regulation of such technologies have not kept pace in a systematic or internationally consistent manner, leaving a complex, uneven, and incomplete web of national and international regulation (1). How countries choose to regulate these emergent technologies matters not just locally, but globally, because the implications of technological developments do not stop at national boundaries. Practices deemed unacceptable in one country may find a more permissive home in another: not necessarily through national policy choice, but owing to a persistent national legal and regulatory void that enables “ethics dumping” (2)—for example, if those wanting to edit genes to “perfect” humans seek countries with little governance capacity. Just as human rights are generally recognized as a matter of global concern, so too should technologies that may impinge on the question of what it means to be human. Here we show how, as the global governance vacuum is filled, deliberation by a global citizens’ assembly should play a role, for legitimate and effective governance….(More)”.

Politicians should take citizens’ assemblies seriously


The Economist: “In 403bc Athens decided to overhaul its institutions. A disastrous war with Sparta had shown that direct democracy, whereby adult male citizens voted on laws, was not enough to stop eloquent demagogues from getting what they wanted, and indeed from subverting democracy altogether. So a new body, chosen by lot, was set up to scrutinise the decisions of voters. It was called the nomothetai or “layers down of law” and it would be given the time to ponder difficult decisions, unmolested by silver-tongued orators and the schemes of ambitious politicians.

This ancient idea is back in vogue, and not before time. Around the world “citizens’ assemblies” and other deliberative groups are being created to consider questions that politicians have struggled to answer (see article). Over weeks or months, 100 or so citizens—picked at random, but with a view to creating a body reflective of the population as a whole in terms of gender, age, income and education—meet to discuss a divisive topic in a considered, careful way. Often they are paid for their time, to ensure that it is not just political wonks who sign up. At the end they present their recommendations to politicians. Before covid-19 these citizens met in conference centres in large cities where, by mingling over lunch-breaks, they discovered that the monsters who disagree with them turned out to be human after all. Now, as a result of the pandemic, they mostly gather on Zoom.

Citizens’ assemblies are often promoted as a way to reverse the decline in trust in democracy, which has been precipitous in most of the developed world over the past decade or so. Last year the majority of people polled in America, Britain, France and Australia—along with many other rich countries—felt that, regardless of which party wins an election, nothing really changes. Politicians, a common complaint runs, have no understanding of, or interest in, the lives and concerns of ordinary people.

Citizens’ assemblies can help remedy that. They are not a substitute for the everyday business of legislating, but a way to break the deadlock when politicians have tried to deal with important issues and failed. Ordinary people, it turns out, are quite reasonable. A large four-day deliberative experiment in America softened Republicans’ views on immigration; Democrats became less eager to raise the minimum wage. Even more strikingly, two 18-month-long citizens’ assemblies in Ireland showed that the country, despite its deep Catholic roots, was far more socially liberal than politicians had realised. Assemblies overwhelmingly recommended the legalisation of both same-sex marriage and abortion….(More)”.

The forecasting fallacy


Essay by Alex Murrell: “Marketers are prone to a prediction.

You’ll find them in the annual tirade of trend decks. In the PowerPoint projections of self-proclaimed prophets. In the feeds of forecasters and futurists. They crop up on every conference stage. They make their mark on every marketing magazine. And they work their way into every white paper.

To understand the extent of our forecasting fascination, I analysed the websites of three management consultancies looking for predictions with time frames ranging from 2025 to 2050. Whilst one prediction may be published multiple times, the size of the numbers still shocked me. Deloitte’s site makes 6904 predictions. McKinsey & Company make 4296. And Boston Consulting Group, 3679.

In total, these three companies’ websites include just shy of 15,000 predictions stretching out over the next 30 years.

But it doesn’t stop there.

My analysis finished in the year 2050 not because the predictions came to an end but because my enthusiasm did.

Search the sites and you’ll find forecasts stretching all the way to the year 2100. We’re still finding our feet in this century but some, it seems, already understand the next.

I believe the vast majority of these to be not forecasts but fantasies. Snake oil dressed up as science. Fiction masquerading as fact.

This article assesses how predictions have performed in five fields. It argues that poor projections have propagated throughout our society and proliferated throughout our industry. It argues that our fixation with forecasts is fundamentally flawed.

So instead of focussing on the future, let’s take a moment to look at the predictions of the past. Let’s see how our projections panned out….

Viewed through the lens of Tetlock, it becomes clear that the 15,000 predictions with which I began this article are not forecasts but fantasies.

The projections look precise. They sound scientific. But these forecasts are nothing more than delusions with decimal places. Snake oil dressed up as statistics. Fiction masquerading as fact. They provide a feeling of certainty but they deliver anything but.

In his 1998 book The Fortune Sellers, the business writer William A. Sherden quantified our consensual hallucination: 

“Each year the prediction industry showers us with $200 billion in (mostly erroneous) information. The forecasting track records for all types of experts are universally poor, whether we consider scientifically oriented professionals, such as economists, demographers, meteorologists, and seismologists, or psychic and astrological forecasters whose names are household words.” 

The comparison between professional predictors and fortune tellers is apt.

From tarot cards to tea leaves, palmistry to pyromancy, clear visions of cloudy futures have always been sold to susceptible audiences. 

Today, marketers are one such audience.

It’s time we opened our eyes….(More)”.

Guide to Responsible Tech: How to Get Involved & Build a Better Tech Future


Resource by All Tech Is Human: “How do you get involved in the growing Responsible Tech field? This guide is a comprehensive look at the vibrant Responsible Tech ecosystem. Aimed at college students, grad students, and young professionals, the “Responsible Tech Guide” is a mix of advice, career profiles, education journeys, and organizations in the space. Developed by All Tech Is Human, an organization committed to informing & inspiring the next generation of responsible technologists & changemakers….(More)”.