An exploration of Augmented Collective Intelligence

Curated on Posted on by Stefaan Verhulst

Dark Matter Laboratories: “…As with all so-called wicked problems, the climate crisis occurs at the intersection of human and natural systems, where interdependent components interact at multiple scales causing uncertainty and emergent, erratic fluctuations. Interventions in such systems can trigger disproportionate impacts in other areas due to feedback effects. On top of this, collective action problems, such as identifying and implementing climate crisis adaptation or mitigation strategies, involve trade-offs and conflicting motivations between the different decision-makers. All of this presents challenges when identifying solutions, or even agreeing on a shared definition of the problem.

As is often the case in times of crisis, collective community-led actions have been a vital part of the response to the COVID-19 pandemic. Communities have demonstrated their capacity to mobilise efficiently in areas where the public sector has been either too slow, unable, or unwilling to intervene. Yet, the pandemic has also put into perspective the scale of response required to address the climate crisis. Despite a near-total shutdown of the global economy, annual CO2 emissions are only expected to fall by 5.6% this year, falling short of the 7.6% target required to ensure a temperature rise of no more than 1.5°C. Can AI help amplify and coordinate collective action to the scale necessary for effective climate crisis response? In this post, we explore alternative futures that leverage the significant potential of citizen groups to act at a local level in order to achieve global impact.

Applying AI to climate problems

There are various research collaborations, open challenges, and corporate-led initiatives that already exist in the field of AI and climate crisis. Climate Change AI, for instance, has identified a range of opportunity domains for a selection of machine learning (ML) methods. These applications range from electrical systems and transportation to collective decisions and education. Google.org’s Impact Challenge supports initiatives applying AI for social good, while the AI for Good platform aims to identify practical applications of AI that can be scaled for global impact. These initiatives and many others, such as Project Drawdown, have informed our research into opportunity areas for AI to augment Collective Intelligence.

Throughout the project, we have been wary that attempts to apply AI to complex problems can suffer from technological solutionism, which loses sight of the underlying issues. To try to avoid this, with Civic AI, we have focused on understanding community challenges before identifying which parts of the problem are most suited to AI’s strengths, especially as this is just one of the many tools available. Below, we explore how AI could be used to complement and enhance community-led efforts as part of inclusive civic infrastructures.

We define civic assets as the essential shared infrastructure that benefits communities such as an urban forest or a community library. We will explore their role in climate crisis mitigation and adaptation. What does a future look like in which these assets are semi-autonomous and highly participatory, fostering collaboration between people and machines?…(More) –

See also: Where and when AI and CI meet: exploring the intersection of artificial and collective intelligence towards the goal of innovating how we govern

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Amsterdam and Helsinki launch algorithm registries to bring transparency to public deployments of AI

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Khari Johnson at Venture Beat: “Amsterdam and Helsinki today launched AI registries to detail how each city government uses algorithms to deliver services, some of the first major cities in the world to do so. An AI Register for each city was introduced in beta today as part of the Next Generation Internet Policy Summit, organized in part by the European Commission and the city of Amsterdam. The Amsterdam registry currently features a handful of algorithms, but it will be extended to include all algorithms following the collection of feedback at the virtual conference to lay out a European vision of the future of the internet, according to a city official.

Each algorithm cited in the registry lists datasets used to train a model, a description of how an algorithm is used, how humans utilize the prediction, and how algorithms were assessed for potential bias or risks. The registry also provides citizens a way to give feedback on algorithms their local government uses and the name, city department, and contact information for the person responsible for the responsible deployment of a particular algorithm. A complete algorithmic registry can empower citizens and give them a way to evaluate, examine, or question governments’ applications of AI.

In a previous development in the U.S., New York City created an automated decision systems task force in 2017 to document and assess city use of algorithms. At the time it was the first city in the U.S. to do so. However, following the release of a report last year, commissioners on the task force complained about a lack of transparency and inability to access information about algorithms used by city government agencies….

In a statement accompanying the announcement, Helsinki City Data project manager Pasi Rautio said the registry is also aimed at increasing public trust in the kinds of artificial intelligence “with the greatest possible openness.”…(More)”.

Private Sector Data for Humanitarian Response: Closing the Gaps

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Jos Berens at Bloomberg New Economy Forum: “…Despite these and other examples, data sharing between the private sector and humanitarian agencies is still limited. Out of 281 contributing organizations on HDX, only a handful come from the private sector. 

So why don’t we see more use of private sector data in humanitarian response? One obvious set of challenges concerns privacy, data protection and ethics. Companies and their customers are often wary of data being used in ways not related to the original purpose of data collection. Such concerns are understandable, especially given the potential legal and reputational consequences of personal data breaches and leaks.

Figuring out how to use this type of sensitive data in an already volatile setting seems problematic, and it is — negotiations between public and private partners in the middle of a crisis often get hung up on a lack of mutual understanding. Data sharing partnerships negotiated during emergencies often fail to mature beyond the design phase. This dynamic creates a loop of inaction due to a lack of urgency in between crises, followed by slow and halfway efforts when action is needed most.

To ensure that private sector data is accessible in an emergency, humanitarian organizations and private sector companies need to work together to build partnerships before a crisis. They can do this by taking the following actions: 

  • Invest in relationships and build trust. Both humanitarian organizations and private sector organizations should designate focal points who can quickly identify potentially useful data during a humanitarian emergency. A data stewards network which identifies and connects data responsibility leaders across organizations, as proposed by the NYU Govlab, is a great example of how such relations could look. Efforts to build trust with the general public regarding private sector data use for humanitarian response should also be strengthened, primarily through transparency about the means and purpose of such collaborations. This is particularly important in the context of COVID-19, as noted in the UN Comprehensive Response to COVID-19 and the World Economic Forum’s ‘Great Reset’ initiative…(More)”.

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

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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., psychology) focus 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)”.

How Algorithms Can Fight Bias Instead of Entrench It

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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)”.

The forecasting fallacy

Curated on Posted on by Stefaan Verhulst

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)”.

The Post-pandemic Future of Trust in Digital Governance

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Essay by Teresa Scassa: “Even prior to the COVID-19 pandemic, “trust” was a key concept for governments as they asked citizens to make a leap of faith into an increasingly digital and data-driven society. Canada’s Digital Charter was billed as a tool for “building a foundation of trust.” Australia’s Data & Digital Council issued Trust Principles. Trust was a key theme in “Strengthening Digital Government,” a statement from the Organisation for Economic Co-operation and Development. Yet, in spite of this focus on trust, a 2017 study suggested disturbingly low levels of citizen trust in government’s handling of their data in the United Kingdom, the United States and Australia.

The COVID-19 pandemic has further laid bare this lack of trust in government. In the debates around contact-tracing apps it became clear that Western governments did not enjoy public trust when it came to data and technology. When they sought to use technology to support public health contact tracing during a pandemic, governments found that a lack of trust seriously constrained their options. Privacy advocates resisted contact-tracing technologies, raising concerns about surveillance and function creep. They had only to refer to the post-9/11 surveillance legacy to remind the public that “emergency” measures can easily become the new normal.

Working with privacy advocates, Google and Apple developed a fully decentralized model for contact tracing that largely left public health authorities out of the loop. Not trusting governments to set their own parameters for apps, Google and Apple dictated the rules. The Google-Apple Exposure Notification system is limited to only one app per country (creating challenges for Canada’s complicated federalism). It relies on Bluetooth only and does not collect location data. It requires full decentralization of data storage, demands that any app built on the protocol be used voluntarily and ensures post-pandemic decommissioning. Governments that saw value in collecting some centralized data — and possibly some GPS data — to support their data analyses and modelling found themselves with apps that operated less than optimally on Android or iOS platforms or that faced interoperability challenges with other apps in the “return to normal” phase….(More)”.

Using behavioral insights to make the most of emergency social protection cash transfers

Curated on Posted on by Stefaan Verhulst

Article by Laura Rawlings, Jessica Jean-Francois and Catherine MacLeod: “In response to the COVID-19 pandemic, countries across the globe have been adapting social assistance policies to support their populations. In fact, since March 2020, 139 countries and territories have planned, implemented, or adapted cash transfers to support their citizens. Cash transfers specifically make up about half of the social protection programs implemented to address the pandemic. Now more than ever, it’s crucial that such programs are designed to maximize impacts. Behavioral insights can be mobilized as a cost-effective way to help beneficiaries make the most out of the available support. The World Bank and ideas42 partnership on behavioral designs for cash transfer programs is helping countries achieve this goal.

Cash transfers are a key response instrument in the social protection toolkit—and for good reason. Cash transfers have been shown to generate a wide variety of positive benefits, from helping families invest in their children to promoting gender equality. However, we know from our previous work that in order to make the most out of cash transfers, recipients of any program (already facing challenging circumstances that compete for their attention) must undertake complex decisions and actions with their cash. These challenges are only magnified by the global pandemic. COVID-19 has wrought increased uncertainty around future employment and income, which makes calculations and planning to use cash transfer benefits all the more complex.

To help practitioners design programs that account for the complex thought processes and potential barriers recipients face, we mapped out their journey to effectively spend emergency social protection cash transfers. We also created simple, actionable guidance for program designers to put to use in maximizing their programs to help recipients use their cash transfer benefit to most effectively support families and reduce mid- to long-term financial volatility. 

For example, the first step is helping recipients understand what the transfer is for. For recipients who have not yet been impacted by financial instability, or indeed have never encountered a cash transfer before, such funds might seem like a gift or bonus, and recipients may spend it accordingly. Providing clear, simple framing or labelling the transfer may signal to recipients that they should use the cash not only for immediate needs, but also in ways that can help them protect investments in their family members’ human capital and jumpstart their livelihood after the crisis wanes….(More)”.

Winter is coming. Can cities use innovation to save ‘streateries’?

Curated on Posted on by Stefaan Verhulst

Bloomberg Cities: “Outdoor dining has been a summer savior in these COVID times, keeping restaurants and the people they employ afloat while bringing sidewalks and streets once hushed by stay-at-home orders back to life.

But with Labor Day now behind us, many city leaders and residents alike are asking, “What’s next?” “What becomes of the vibrant ‘streateries’ once winter comes rolling in?”

Perhaps it’s no surprise that Chicago, notorious for its frigid winters and whipping lakefront winds, is at the forefront of the hunt for an answer. The city recently launched the City of Chicago Winter Dining Challenge to get everyone from designers to dishwashers thinking up new ideas for how to do outdoor eating in the cold in a way that is both appealing and safe for customers and restaurant workers.

More intriguing is just how much interest the competition has generated, including nearly 650 entries from all over the world. There are dozens of takes on warming large patios and small dining pods, including approaches likened to greenhouses, igloos, and yurts; ideas for repurposing parking garages and city buses; furniture-based concepts with heated tables, seats and umbrellas, and even a Swiss-style fondue chalet.

The goal, said Samir Mayekar, Chicago’s Deputy Mayor for Economic and Neighborhood Development, is to surface ideas city leaders would never have thought of. Three winners will get $5,000 each and see their ideas piloted in neighborhoods across the city in October….(More)”.

The Road Back to College Is Paved with Barriers, but Behavioral Science Can Help Smooth the Way

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Blog by Katherine Flaschen and Ben Castleman: “In order to create the most effective solutions, policymakers and educators need to better understand a fundamental question: Why do so many of these students, many of whom have already made substantial progress toward their degree, fail to return to college and graduate? …

With a better understanding of the barriers preventing people who intend to finish their degree from following through, policymakers and colleges can create solutions that meaningfully meet students’ needs and help them re-enroll. As states across the country face rising unemployment rates, it’s critical to design and test interventions that address these behavioral barriers and help thousands of citizens who are out of work due to the COVID-19 crisis consider their options for going back to school.

For example, colleges could provide monetary incentives to students for taking actions related to re-enrollment that overcome these barriers, such as speaking with an advisor, reviewing upcoming recommended courses and developing a course plan, and making an active choice about when to return to college. In addition, SCND students could be paired with current students to serve as peer mentors, both to provide support with the re-enrollment process and to hold them accountable for degree completion (especially if faced with difficult remaining classes). Community colleges could also encourage major employers of the SCND population in high-demand fields, like health care, to provide options for employees to finish their degree while working (e.g., via tuition reimbursement programs), translate degree attainment into concrete career returns, and identify representatives within the company, such as recent graduates, to promote re-enrollment and make it a more salient opportunity….(More)”.