Fighting poverty with synthetic data

Curated on Posted on by Stefaan Verhulst

Article by Jack Gisby, Anna Kiknadze, Thomas Mitterling, and Isabell Roitner-Fransecky: “If you have ever used a smartwatch or other wearable tech to track your steps, heart rate, or sleep, you are part of the “quantified self” movement. You are voluntarily submitting millions of intimate data points for collection and analysis. The Economist highlighted the benefits of good quality personal health and wellness data—increased physical activity, more efficient healthcare, and constant monitoring of chronic conditions. However, not everyone is enthusiastic about this trend. Many fear corporations will use the data to discriminate against the poor and vulnerable. For example, insurance firms could exclude patients based on preconditions obtained from personal data sharing.

Can we strike a balance between protecting the privacy of individuals and gathering valuable information? This blog explores applying a synthetic populations approach in New York City,  a city with an established reputation for using big data approaches to support urban management, including for welfare provisions and targeted policy interventions.

To better understand poverty rates at the census tract level, World Data Lab, with the support of the Sloan Foundation, generated a synthetic population based on the borough of Brooklyn. Synthetic populations rely on a combination of microdata and summary statistics:

  • Microdata consists of personal information at the individual level. In the U.S., such data is available at the Public Use Microdata Area (PUMA) level. PUMA are geographic areas partitioning the state, containing no fewer than 100,000 people each. However, due to privacy concerns, microdata is unavailable at the more granular census tract level. Microdata consists of both household and individual-level information, including last year’s household income, the household size, the number of rooms, and the age, sex, and educational attainment of each individual living in the household.
  • Summary statistics are based on populations rather than individuals and are available at the census tract level, given that there are fewer privacy concerns. Census tracts are small statistical subdivisions of a county, averaging about 4,000 inhabitants. In New York City, a census tract roughly equals a building block. Similar to microdata, summary statistics are available for individuals and households. On the census tract level, we know the total population, the corresponding demographic breakdown, the number of households within different income brackets, the number of households by number of rooms, and other similar variables…(More)”.

When What’s Right Is Also Wrong: The Pandemic As A Corporate Social Responsibility Paradox

Curated on Posted on by Stefaan Verhulst

Article by Heidi Reed: “When the COVID-19 pandemic first hit, businesses were faced with difficult decisions where making the ‘right choice’ just wasn’t possible. For example, if a business chose to shut down, it might protect employees from catching COVID, but at the same time, it would leave them without a paycheck. This was particularly true in the U.S. where the government played a more limited role in regulating business behavior, leaving managers and owners to make hard choices.

In this way, the pandemic is a societal paradox in which the social objectives of public health and economic prosperity are both interdependent and contradictory. How does the public judge businesses then when they make decisions favoring one social objective over another? To answer this question, I qualitatively surveyed the American public at the start of the COVID-19 crisis about what they considered to be responsible and irresponsible business behavior in response to the pandemic. Analyzing their answers led me to create the 4R Model of Moral Sensemaking of Competing Social Problems.

The 4R Model relies on two dimensions: the extent to which people prioritize one social problem over another and the extent to which they exhibit psychological discomfort (i.e. cognitive dissonance). In the first mode, Reconcile, people view the problems as compatible. There is no need to prioritize then and no resulting dissonance. These people think, “Businesses can just convert to making masks to help the cause and still make a profit.”

The second mode, Resign, similarly does not prioritize one problem over another; however, the problems are seen as competing, suggesting a high level of cognitive dissonance. These people might say, “It’s dangerous to stay open, but if the business closes, people will lose their jobs. Both decisions are bad.”

In the third mode, Ranking, people use prioritizing to reduce cognitive dissonance. These people say things like, “I understand people will be fired, but it’s more important to stop the virus.”

In the fourth and final mode, Rectify, people start by ranking but show signs of lingering dissonance as they acknowledge the harm created by prioritizing one problem over another. Unlike with the Resign mode, they try to find ways to reduce this harm. A common response in this mode would be, “Businesses should shut down, but they should also try to help employees file for unemployment.”

The 4R model has strong implications for other grand challenges where there may be competing social objectives such as in addressing climate change. To this end, the typology helps corporate social responsibility (CSR) decision-makers understand how they may be judged when businesses are forced to re- or de-prioritize CSR dimensions. In other words, it helps us understand how people make moral sense of business behavior when the right thing to do is paradoxically also the wrong thing…(More)”

A Snapshot of Artificial Intelligence Procurement Challenges

Curated on Posted on by Stefaan Verhulst

Press Release: “The GovLab has released a new report offering recommendations for government in procuring artificial intelligence (AI) tools. As the largest purchaser of technology, it is critical for the federal government to adapt its procurement practices to ensure that beneficial AI tools can be responsibly and rapidly acquired and that safeguards are in place to ensure that technology improves people’s lives while minimizing risks. 

Based on conversations with over 35 leaders in government technology, the report identifies key challenges impeding successful procurement of AI, and offers five urgent recommendations to ensure that government is leveraging the benefits of AI to serve residents:

  1. Training: Invest in training public sector professionals to understand and differentiate between high- and low-risk AI opportunities. This includes teaching individuals and government entities to define problems accurately and assess algorithm outcomes. Frequent training updates are necessary to adapt to the evolving AI landscape.
  2. Tools: Develop decision frameworks, contract templates, auditing tools, and pricing models that empower procurement officers to confidently acquire AI. Open data and simulated datasets can aid in testing algorithms and identifying discriminatory effects.
  3. Regulation and Guidance: Recognize the varying complexity of AI use cases and develop a system that guides acquisition professionals to allocate time appropriately. This approach ensures more problematic cases receive thorough consideration.
  4. Organizational Change: Foster collaboration, knowledge sharing, and coordination among procurement officials and policymakers. Including mechanisms for public input allows for a multidisciplinary approach to address AI challenges.
  5. Narrow the Expertise Gap: Integrate individuals with expertise in new technologies into various government departments, including procurement, legal, and policy teams. Strengthen connections with academia and expand fellowship programs to facilitate the acquisition of relevant talent capable of auditing AI outcomes. Implement these programs at federal, state, and local government levels…(More)”

How Does Data Access Shape Science?

Curated on Posted on by Stefaan Verhulst

Paper by Abhishek Nagaraj & Matteo Tranchero: “This study examines the impact of access to confidential administrative data on the rate, direction, and policy relevance of economics research. To study this question, we exploit the progressive geographic expansion of the U.S. Census Bureau’s Federal Statistical Research Data Centers (FSRDCs). FSRDCs boost data diffusion, help empirical researchers publish more articles in top outlets, and increase citation-weighted publications. Besides direct data usage, spillovers to non-adopters also drive this effect. Further, citations to exposed researchers in policy documents increase significantly. Our findings underscore the importance of data access for scientific progress and evidence-based policy formulation…(More)”.

Data for Environmentally Sustainable and Inclusive Urban Mobility

Curated on Posted on by Stefaan Verhulst

Report by Anusha Chitturi and Robert Puentes: “Data on passenger movements, vehicle fleets, fare payments, and transportation infrastructure has immense potential to inform cities to better plan, regulate, and enforce their urban mobility systems. This report specifically examines the opportunities that exist for U.S. cities to use mobility data – made available through adoption of new mobility services and data-based technologies – to improve transportation’s environmental sustainability, accessibility, and equity. Cities are advancing transportation sustainability in several ways, including making trips more efficient, minimizing the use of single-occupancy vehicles, prioritizing sustainable modes of transport, and enabling a transition to zero and low-emission fuels. They are improving accessibility and equity by planning for and offering a range of transportation services that serve all people, irrespective of their physical abilities, economic power, and geographic location.
Data sharing is an important instrument for furthering these mobility outcomes. Ridership data from ride-hailing companies, for example, can inform cities about whether they are replacing sustainable transport trips, resulting in an increase in congestion and emissions; such data can further be used for designing targeted emission-reduction programs such as a congestion fee program, or for planning high-quality sustainable transport services to reduce car trips. Similarly, mobility data can be used to plan on-demand services in certain transit-poor neighborhoods, where fixed transit services don’t make financial sense due to low urban densities. Sharing mobility data, however, often comes with certain risks,..(More)”.

Augmented Reality Is Coming for Cities

Curated on Posted on by Stefaan Verhulst

Article by Greg Lindsay: “It’s still early in the metaverse, however — no killer app has yet emerged, and the financial returns on disruption are falling as interest rates rise.

Already, a handful of companies have come forward to partner with cities instead of fighting them. For example, InCitu uses AR to visualize the building envelopes of planned projects in New York City, Buffalo, and beyond in hopes of winning over skeptical communities through seeing-is-believing. The startup recently partnered with Washington, DC’s Department of Buildings to aid its civic engagement efforts. Another of its partners is Snap, the Gen Z social media giant currently currying favor with cities and civic institutions as it pivots to AR for its next act…

For cities to gain the metaverse they want tomorrow, they will need to invest the scarce staff time and resources today. That means building a coalition of the willing among Apple, Google, Niantic, Snap and others; throwing their weight behind open standards through participation in umbrella groups such as the Metaverse Standards Forum; and becoming early, active participants in each of the major platforms in order to steer traffic toward designated testbeds and away from highly trafficked areas.

It’s a tall order for cities grappling with a pandemic crisis, drug-and-mental-health crisis, and climate crisis all at once, but a necessary one to prevent the metaverse (of all things!) from becoming the next one…(More)”.

How AI could take over elections – and undermine democracy

Curated on Posted on by Stefaan Verhulst

Article by Archon Fung and Lawrence Lessig: “Could organizations use artificial intelligence language models such as ChatGPT to induce voters to behave in specific ways?

Sen. Josh Hawley asked OpenAI CEO Sam Altman this question in a May 16, 2023, U.S. Senate hearing on artificial intelligence. Altman replied that he was indeed concerned that some people might use language models to manipulate, persuade and engage in one-on-one interactions with voters.

Altman did not elaborate, but he might have had something like this scenario in mind. Imagine that soon, political technologists develop a machine called Clogger – a political campaign in a black box. Clogger relentlessly pursues just one objective: to maximize the chances that its candidate – the campaign that buys the services of Clogger Inc. – prevails in an election.

While platforms like Facebook, Twitter and YouTube use forms of AI to get users to spend more time on their sites, Clogger’s AI would have a different objective: to change people’s voting behavior.

As a political scientist and a legal scholar who study the intersection of technology and democracy, we believe that something like Clogger could use automation to dramatically increase the scale and potentially the effectiveness of behavior manipulation and microtargeting techniques that political campaigns have used since the early 2000s. Just as advertisers use your browsing and social media history to individually target commercial and political ads now, Clogger would pay attention to you – and hundreds of millions of other voters – individually.

It would offer three advances over the current state-of-the-art algorithmic behavior manipulation. First, its language model would generate messages — texts, social media and email, perhaps including images and videos — tailored to you personally. Whereas advertisers strategically place a relatively small number of ads, language models such as ChatGPT can generate countless unique messages for you personally – and millions for others – over the course of a campaign.

Second, Clogger would use a technique called reinforcement learning to generate a succession of messages that become increasingly more likely to change your vote. Reinforcement learning is a machine-learning, trial-and-error approach in which the computer takes actions and gets feedback about which work better in order to learn how to accomplish an objective. Machines that can play Go, Chess and many video games better than any human have used reinforcement learning.How reinforcement learning works.

Third, over the course of a campaign, Clogger’s messages could evolve in order to take into account your responses to the machine’s prior dispatches and what it has learned about changing others’ minds. Clogger would be able to carry on dynamic “conversations” with you – and millions of other people – over time. Clogger’s messages would be similar to ads that follow you across different websites and social media…(More)”.

Recoding America: Why Government Is Failing in the Digital Age and How We Can Do Better

Curated on Posted on by Stefaan Verhulst

Book by Jennifer Pahlka: “Just when we most need our government to work—to decarbonize our infrastructure and economy, to help the vulnerable through a pandemic, to defend ourselves against global threats—it is faltering. Government at all levels has limped into the digital age, offering online services that can feel even more cumbersome than the paperwork that preceded them and widening the gap between the policy outcomes we intend and what we get.

But it’s not more money or more tech we need. Government is hamstrung by a rigid, industrial-era culture, in which elites dictate policy from on high, disconnected from and too often disdainful of the details of implementation. Lofty goals morph unrecognizably as they cascade through a complex hierarchy. But there is an approach taking hold that keeps pace with today’s world and reclaims government for the people it is supposed to serve. Jennifer Pahlka shows why we must stop trying to move the government we have today onto new technology and instead consider what it would mean to truly recode American government…(More)”.

How Differential Privacy Will Affect Estimates of Air Pollution Exposure and Disparities in the United States

Curated on Posted on by Stefaan Verhulst

Article by Madalsa Singh: “Census data is crucial to understand energy and environmental justice outcomes such as poor air quality which disproportionately impact people of color in the U.S. With the advent of sophisticated personal datasets and analysis, Census Bureau is considering adding top-down noise (differential privacy) and post-processing 2020 census data to reduce the risk of identification of individual respondents. Using 2010 demonstration census and pollution data, I find that compared to the original census, differentially private (DP) census significantly changes ambient pollution exposure in areas with sparse populations. White Americans have lowest variability, followed by Latinos, Asian, and Black Americans. DP underestimates pollution disparities for SO2 and PM2.5 while overestimates the pollution disparities for PM10…(More)”.

How civic capacity gets urban social innovations started

Curated on Posted on by Stefaan Verhulst

Article by Christof Brandtner: “After President Trump withdrew from the Paris Climate Accords, several hundred mayors signed national and global treaties announcing their commitments to “step up and do more,” as a senior official of the City of New York told me in a poorly lit room in 2017. Cities were rushing to the forefront of adopting practices and policies to address contemporary social and environmental problems, such as climate change.

What the general enthusiasm masked is significant variation in the extent and speed at which cities adopt these innovations…My study of the geographic dispersion of green buildings certified with the U.S. Green Building Council’s Leadership in Energy and Environmental Design (LEED) rating system, published in the American Journal of Sociology, suggests that the organizational communities within cities play a significant role in adopting urban innovations. Cities with a robust civic capacity, where values-oriented organizations actively address social problems, are more likely to adopt new practices quickly and extensively. Civic capacity matters not only through structural channels, as a sign of ample resources and community social capital, but also through organizational channels. Values-oriented organizations are often early adopters of new practices, such as green construction, solar panels, electric vehicles, or equitable hiring practices. By creating proofs of concepts, these early adopters can serve as catalysts of municipal policies and widespread adoption…(More)”.