Stefaan Verhulst
Blog by Actionable Intelligence for Social Policy (AISP): “…State and local leaders are called upon to respond to the immediate harms of COVID-19. Yet with a looming recession threatening to undo gains among marginalized groups — particularly the Black middle class — tools to understand and disrupt long-term impacts on economic mobility and well-being are also urgently needed.
Administrative data[3] — the information collected during the course of routine service delivery, program administration, and business operations — provide an essential tool to help policymakers, community leaders, and researchers understand short- and long-term impacts of the pandemic. Several jurisdictions now have the capacity to link administrative data across programs in order to better understand how individuals interact with multiple systems, study longitudinal outcomes, and identify vulnerable subpopulations. As the COVID-19 crisis reveals weaknesses in the U.S. social safety net, states and localities with integrated administrative data infrastructure can use their capacity to identify populations and needs otherwise overlooked. Youth who “age out” of the child welfare system or individuals experiencing chronic homelessness often remain invisible when using traditional methods, aggregate data, or administrative records from a single source.
This blogpost demonstrates how nimble state and local data integration efforts have leveraged their capacity to quickly respond to and understand the impacts of COVID-19, while also reflecting on what can be done to mitigate harm and shift thinking about social welfare and the safety net….(More)”.
Book by Cory Doctorow: “…Today, there is a widespread belief that machine learning and commercial surveillance can turn even the most fumble-tongued conspiracy theorist into a svengali who can warp your perceptions and win your belief by locating vulnerable people and then pitching them with A.I.-refined arguments that bypass their rational faculties and turn everyday people into flat Earthers, anti-vaxxers, or even Nazis. When the RAND Corporation blames Facebook for “radicalization” and when Facebook’s role in spreading coronavirus misinformation is blamed on its algorithm, the implicit message is that machine learning and surveillance are causing the changes in our consensus about what’s true.
After all, in a world where sprawling and incoherent conspiracy theories like Pizzagate and its successor, QAnon, have widespread followings, something must be afoot.
But what if there’s another explanation? What if it’s the material circumstances, and not the arguments, that are making the difference for these conspiracy pitchmen? What if the trauma of living through real conspiracies all around us — conspiracies among wealthy people, their lobbyists, and lawmakers to bury inconvenient facts and evidence of wrongdoing (these conspiracies are commonly known as “corruption”) — is making people vulnerable to conspiracy theories?
If it’s trauma and not contagion — material conditions and not ideology — that is making the difference today and enabling a rise of repulsive misinformation in the face of easily observed facts, that doesn’t mean our computer networks are blameless. They’re still doing the heavy work of locating vulnerable people and guiding them through a series of ever-more-extreme ideas and communities.
Belief in conspiracy is a raging fire that has done real damage and poses real danger to our planet and species, from epidemics kicked off by vaccine denial to genocides kicked off by racist conspiracies to planetary meltdown caused by denial-inspired climate inaction. Our world is on fire, and so we have to put the fires out — to figure out how to help people see the truth of the world through the conspiracies they’ve been confused by.
But firefighting is reactive. We need fire prevention. We need to strike at the traumatic material conditions that make people vulnerable to the contagion of conspiracy. Here, too, tech has a role to play.
There’s no shortage of proposals to address this. From the EU’s Terrorist Content Regulation, which requires platforms to police and remove “extremist” content, to the U.S. proposals to force tech companies to spy on their users and hold them liable for their users’ bad speech, there’s a lot of energy to force tech companies to solve the problems they created.
There’s a critical piece missing from the debate, though. All these solutions assume that tech companies are a fixture, that their dominance over the internet is a permanent fact. Proposals to replace Big Tech with a more diffused, pluralistic internet are nowhere to be found. Worse: The “solutions” on the table today require Big Tech to stay big because only the very largest companies can afford to implement the systems these laws demand….(More)”.
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)”.