Stefaan Verhulst

Lessons from Survey Research by National Academies of Sciences, Engineering, and Medicine: “Contact tracing shares important features with the collection of survey data, as well as the taking of the U.S. Census. This rapid expert consultation suggests proven strategies from survey research that decision makers can use to encourage participation in and cooperation with contact tracing efforts along two fronts: encouraging individuals to respond to outreach from health department officials regarding participation in contact tracing and case investigation, and encouraging those who do participate to share information about people whom they may have exposed to COVID-19.

Encouraging Participation and Cooperation in Contact Tracing is intended to help decision makers in local public health departments and local governments increase participation and cooperation in contact tracing related to COVID-19. This publication focuses on contact tracing methods that involve phone, text, or email interviews with people who have tested positive and with others they may have exposed to the virus…(More)”.

Aaron Gordon at Vice: “…The Louisville highway project is hardly the first time travel demand models have missed the mark. Despite them being a legally required portion of any transportation infrastructure project that gets federal dollars, it is one of urban planning’s worst kept secrets that these models are error-prone at best and fundamentally flawed at worst.

Recently, I asked Renn how important those initial, rosy traffic forecasts of double-digit growth were to the boondoggle actually getting built.

“I think it was very important,” Renn said. “Because I don’t believe they could have gotten approval to build the project if they had not had traffic forecasts that said traffic across the river is going to increase substantially. If there isn’t going to be an increase in traffic, how do you justify building two bridges?”

ravel demand models come in different shapes and sizes. They can cover entire metro regions spanning across state lines or tackle a small stretch of a suburban roadway. And they have gotten more complicated over time. But they are rooted in what’s called the Four Step process, a rough approximation of how humans make decisions about getting from A to B. At the end, the model spits out numbers estimating how many trips there will be along certain routes.

As befits its name, the model goes through four steps in order to arrive at that number. First, it generates a kind of algorithmic map based on expected land use patterns (businesses will generate more trips than homes) and socio-economic factors (for example, high rates of employment will generate more trips than lower ones). Then it will estimate where people will generally be coming from and going to. The third step is to guess how they will get there, and the fourth is to then plot their actual routes, based mostly on travel time. The end result is a number of how many trips there will be in the project area and how long it will take to get around. Engineers and planners will then add a new highway, transit line, bridge, or other travel infrastructure to the model and see how things change. Or they will change the numbers in the first step to account for expected population or employment growth into the future. Often, these numbers are then used by policymakers to justify a given project, whether it’s a highway expansion or a light rail line…(More)”.

Report by the Open Data Institute: “The outbreak of the coronavirus (Covid-19) has amplified and accelerated the need for an effective technology ecosystem that benefits everyone’s health. This report explores models of ‘data stewardship’ (the collection, maintenance and sharing of data) required to enable better evaluation

The pandemic has been accompanied by a marked increase in the use of digital technology, including introduction of remote consultation in general practice, new data flows to support the distribution of food and other essentials, and applications to support digital contact tracing.

This report explores models of ‘data stewardship’ (the collection, maintenance and sharing of data) required to enable better evaluation. It argues everybody involved in technology has a shared responsibility to enable evaluation, whether that means innovators sharing data for evaluation purposes, or healthcare providers being clearer, from the outset, about what data is needed to support effective evaluation.

This report re-envisages the role of evaluators as data stewards, who could use their positions as intermediaries to encourage stakeholders to share data, and help increase access to data for public benefit…(More)”.

Paper by Khaled Moustafa in Cities: “The ongoing COVID-19 pandemic should teach us some lessons at health, environmental and human levels toward more fairness, human cohesion and environmental sustainability. At a health level, the pandemic raises the importance of housing for everyone particularly vulnerable and homeless people to protect them from the disease and against other similar airborne pandemics. Here, I propose to make good use of big data along with 3D construction printers to construct houses and solve some major and pressing housing needs worldwide. Big data can be used to determine how many people do need accommodation and 3D construction printers to build houses accordingly and swiftly. The combination of such facilities- big data and 3D printers- can help solve global housing crises more efficiently than traditional and unguided construction plans, particularly under environmental and major health crises where health and housing are tightly interrelated….(More)”.

Paper by Susanne Beck et al: “Openness and collaboration in scientific research are attracting increasing attention from scholars and practitioners alike. However, a common understanding of these phenomena is hindered by disciplinary boundaries and disconnected research streams. We link dispersed knowledge on Open Innovation, Open Science, and related concepts such as Responsible Research and Innovation by proposing a unifying Open Innovation in Science (OIS) Research Framework. This framework captures the antecedents, contingencies, and consequences of open and collaborative practices along the entire process of generating and disseminating scientific insights and translating them into innovation. Moreover, it elucidates individual-, team-, organisation-, field-, and society‐level factors shaping OIS practices. To conceptualise the framework, we employed a collaborative approach involving 47 scholars from multiple disciplines, highlighting both tensions and commonalities between existing approaches. The OIS Research Framework thus serves as a basis for future research, informs policy discussions, and provides guidance to scientists and practitioners….(More)”.

Simon Roberts and Jason Bell at the Conversation: “The COVID-19 pandemic and consequent lockdown measures have had a huge negative impact on producers and consumers. Food production has been disrupted, and incomes have been lost. But a far more devastating welfare consequence of the pandemic could be reduced access to food.

A potential rise in food insecurity is a key policy point for many countries. The World Economic Forum has stated this pandemic is set to “radically exacerbate food insecurity in Africa”. This, and other supplier shocks, such as locust swarms in East Africa, have made many African economies more dependent on externally sourced food.

As the pandemic continues to spread, the continued functioning of regional and national food supply chains is vital to avoid a food security crisis in countries dependent on agriculture. This is true in terms of both nutrition and livelihoods. Many countries in Southern and East African economies are in this situation.

The integration of regional economies is one vehicle for alleviating pervasive food security issues. But regional integration can’t be achieved without the appropriate support for investment in production, infrastructure and capabilities.

And, crucially, there must be more accurate and timely information about food markets. Data on food prices are crucial for political and economic stability. Yet they are not easily accessible.

A study by the Centre for Competition, Regulation and Economic Development highlights how poor and inconsistent pricing data severely affects the quality of any assessment of agricultural markets in the Southern and East African region….(More)”

Book by George Dyson: “In 1716, the philosopher and mathematician Gottfried Wilhelm Leibniz spent eight days taking the cure with Peter the Great at Bad Pyrmont in Saxony, seeking to initiate a digitally-computed takeover of the world. In his classic books, Darwin Among the Machines and Turing’s Cathedral, Dyson chronicled the realization of Leibniz’s dream at the hands of a series of iconoclasts who brought his ideas to life. Now, in his pathbreaking new book, Analogia, he offers a chronicle of people who fought for the other side—the Native American leader Geronimo and physicist Leo Szilard, among them—a series of stories that will change our view not only of the past but also of the future.

The convergence of a startling historical archaeology with Dyson’s unusual personal story—set alternately in the rarified world of cutting-edge physics and computer science, in Princeton, and in the rainforest of the Northwest Coast—leads to a prophetic vision of an analog revolution already under way. We are, Dyson reveals, on the cusp of a new moment in human history, driven by a generation of machines whose powers are beyond programmable control…(More)”.

Essay by Hannah Kerner: “Any researcher who’s focused on applying machine learning to real-world problems has likely received a response like this one: “The authors present a solution for an original and highly motivating problem, but it is an application and the significance seems limited for the machine-learning community.”

These words are straight from a review I received for a paper I submitted to the NeurIPS (Neural Information Processing Systems) conference, a top venue for machine-learning research. I’ve seen the refrain time and again in reviews of papers where my coauthors and I presented a method motivated by an application, and I’ve heard similar stories from countless others.

This makes me wonder: If the community feels that aiming to solve high-impact real-world problems with machine learning is of limited significance, then what are we trying to achieve?

The goal of artificial intelligence (pdf) is to push forward the frontier of machine intelligence. In the field of machine learning, a novel development usually means a new algorithm or procedure, or—in the case of deep learning—a new network architecture. As others have pointed out, this hyperfocus on novel methods leads to a scourge of papers that report marginal or incremental improvements on benchmark data sets and exhibit flawed scholarship (pdf) as researchers race to top the leaderboard.

Meanwhile, many papers that describe new applications present both novel concepts and high-impact results. But even a hint of the word “application” seems to spoil the paper for reviewers. As a result, such research is marginalized at major conferences. Their authors’ only real hope is to have their papers accepted in workshops, which rarely get the same attention from the community.

This is a problem because machine learning holds great promise for advancing health, agriculture, scientific discovery, and more. The first image of a black hole was produced using machine learning. The most accurate predictions of protein structures, an important step for drug discovery, are made using machine learning. If others in the field had prioritized real-world applications, what other groundbreaking discoveries would we have made by now?

This is not a new revelation. To quote a classic paper titled “Machine Learning that Matters” (pdf), by NASA computer scientist Kiri Wagstaff: “Much of current machine learning research has lost its connection to problems of import to the larger world of science and society.” The same year that Wagstaff published her paper, a convolutional neural network called AlexNet won a high-profile competition for image recognition centered on the popular ImageNet data set, leading to an explosion of interest in deep learning. Unfortunately, the disconnect she described appears to have grown even worse since then….(More)”.

Jane Bailey et al at The Conversation: “…In his game-changing 1993 book, The Panoptic Sort, scholar Oscar Gandy warned that “complex technology [that] involves the collection, processing and sharing of information about individuals and groups that is generated through their daily lives … is used to coordinate and control their access to the goods and services that define life in the modern capitalist economy.” Law enforcement uses it to pluck suspects from the general public, and private organizations use it to determine whether we have access to things like banking and employment.

Gandy prophetically warned that, if left unchecked, this form of “cybernetic triage” would exponentially disadvantage members of equality-seeking communities — for example, groups that are racialized or socio-economically disadvantaged — both in terms of what would be allocated to them and how they might come to understand themselves.

Some 25 years later, we’re now living with the panoptic sort on steroids. And examples of its negative effects on equality-seeking communities abound, such as the false identification of Williams.

Pre-existing bias

This sorting using algorithms infiltrates the most fundamental aspects of everyday life, occasioning both direct and structural violence in its wake.

The direct violence experienced by Williams is immediately evident in the events surrounding his arrest and detention, and the individual harms he experienced are obvious and can be traced to the actions of police who chose to rely on the technology’s “match” to make an arrest. More insidious is the structural violence perpetrated through facial recognition technology and other digital technologies that rate, match, categorize and sort individuals in ways that magnify pre-existing discriminatory patterns.

Structural violence harms are less obvious and less direct, and cause injury to equality-seeking groups through systematic denial to power, resources and opportunity. Simultaneously, it increases direct risk and harm to individual members of those groups.

Predictive policing uses algorithmic processing of historical data to predict when and where new crimes are likely to occur, assigns police resources accordingly and embeds enhanced police surveillance into communities, usually in lower-income and racialized neighbourhoods. This increases the chances that any criminal activity — including less serious criminal activity that might otherwise prompt no police response — will be detected and punished, ultimately limiting the life chances of the people who live within that environment….(More)”.

Data2X: “This report builds on our 2019 technical report, Bridging the Gap: Mapping Gender Data Availability in Africa, but shifts the geographic focus to Latin America and the Caribbean (LAC).

It reports on the availability of gender data in Colombia, Costa Rica, the Dominican Republic, Jamaica, and Paraguay at the international, national, and microdata levels, and it assesses the availability of 93 gender indicators, their disaggregations, and their frequency of observation in international and national databases and publications.

Additionally, with the assistance of the UN Economic Commission for Latin America (ECLAC), the report documents the availability of statistical indicators to support gender development plans in the five countries.

Through this report, we hope to help move the development community one step closer to producing high-quality and policy-relevant gender indicators to inform better decisions….Read the report.“