How data helped Mexico City reduce high-impact crime by more than 50%

Curated on Posted on by Stefaan Verhulst

Article by Alfredo Molina Ledesma: “When Claudia Sheimbaum Pardo became Mayor of Mexico City 2018, she wanted a new approach to tackling the city’s most pressing problems. Crime was at the very top of the agenda – only 7% of the city’s inhabitants considered it a safe place. New policies were needed to turn this around.

Data became a central part of the city’s new strategy. The Digital Agency for Public Innovation was created in 2019 – tasked with using data to help transform the city. To put this into action, the city administration immediately implemented an open data policy and launched their official data platform, Portal de Datos Abiertos. The policy and platform aimed to make data that Mexico City collects accessible to anyone: municipal agencies, businesses, academics, and ordinary people.

“The main objective of the open data strategy of Mexico City is to enable more people to make use of the data generated by the government in a simple and interactive manner,” said Jose Merino, Head of the Digital Agency for Public Innovation. “In other words, what we aim for is to democratize the access and use of information.” To achieve this goal a new tool for interactive data visualization called Sistema Ajolote was developed in open source and integrated into the Open Data Portal…

Information that had never been made public before, such as street-level crime from the Attorney General’s Office, is now accessible to everyone. Academics, businesses and civil society organizations can access the data to create solutions and innovations that complement the city’s new policies. One example is the successful “Hoyo de Crimen” app, which proposes safe travel routes based on the latest street-level crime data, enabling people to avoid crime hotspots as they walk or cycle through the city.

Since the introduction of the open data policy – which has contributed to a comprehensive crime reduction and social support strategy – high-impact crime in the city has decreased by 53%, and 43% of Mexico City residents now consider the city to be a safe place…(More)”.

Use of AI in social sciences could mean humans will no longer be needed in data collection

Curated on Posted on by Stefaan Verhulst

Article by Michael Lee: A team of researchers from four Canadian and American universities say artificial intelligence could replace humans when it comes to collecting data for social science research.

Researchers from the University of Waterloo, University of Toronto, Yale University and the University of Pennsylvania published an article in the journal Science on June 15 about how AI, specifically large language models (LLMs), could affect their work.

“AI models can represent a vast array of human experiences and perspectives, possibly giving them a higher degree of freedom to generate diverse responses than conventional human participant methods, which can help to reduce generalizability concerns in research,” Igor Grossmann, professor of psychology at Waterloo and a co-author of the article, said in a news release.

Philip Tetlock, a psychology professor at UPenn and article co-author, goes so far as to say that LLMs will “revolutionize human-based forecasting” in just three years.

In their article, the authors pose the question: “How can social science research practices be adapted, even reinvented, to harness the power of foundational AI? And how can this be done while ensuring transparent and replicable research?”

The authors say the social sciences have traditionally relied on methods such as questionnaires and observational studies.

But with the ability of LLMs to pore over vast amounts of text data and generate human-like responses, the authors say this presents a “novel” opportunity for researchers to test theories about human behaviour at a faster rate and on a much larger scale.

Scientists could use LLMs to test theories in a simulated environment before applying them in the real world, the article says, or gather differing perspectives on a complex policy issue and generate potential solutions.

“It won’t make sense for humans unassisted by AIs to venture probabilistic judgments in serious policy debates. I put an 90 per cent chance on that,” Tetlock said. “Of course, how humans react to all of that is another matter.”

One issue the authors identified, however, is that LLMs often learn to exclude sociocultural biases, raising the question of whether models are correctly reflecting the populations they study…(More)”

Better Government Tech Is Possible

Curated on Posted on by Stefaan Verhulst

Article by Beth Noveck: “In the first four months of the Covid-19 pandemic, government leaders paid $100 million for management consultants at McKinsey to model the spread of the coronavirus and build online dashboards to project hospital capacity.

It’s unsurprising that leaders turned to McKinsey for help, given the notorious backwardness of government technology. Our everyday experience with online shopping and search only highlights the stark contrast between user-friendly interfaces and the frustrating inefficiencies of government websites—or worse yet, the ongoing need to visit a government office to submit forms in person. The 2016 animated movie Zootopia depicts literal sloths running the DMV, a scene that was guaranteed to get laughs given our low expectations of government responsiveness.

More seriously, these doubts are reflected in the plummeting levels of public trust in government. From early Healthcare.gov failures to the more recent implosions of state unemployment websites, policymaking without attention to the technology that puts the policy into practice has led to disastrous consequences.

The root of the problem is that the government, the largest employer in the US, does not keep its employees up-to-date on the latest tools and technologies. When I served in the Obama White House as the nation’s first deputy chief technology officer, I had to learn constitutional basics and watch annual training videos on sexual harassment and cybersecurity. But I was never required to take a course on how to use technology to serve citizens and solve problems. In fact, the last significant legislation about what public professionals need to know was the Government Employee Training Act, from 1958, well before the internet was invented.

In the United States, public sector awareness of how to use data or human-centered design is very low. Out of 400-plus public servants surveyed in 2020, less than 25 percent received training in these more tech-enabled ways of working, though 70 percent said they wanted such training…(More)”.

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)”.

Brazil launches participatory national planning process

Curated on Posted on by Stefaan Verhulst

Article by Tarson Núñez and Luiza Jardim: “At a time when signs of a crisis in democracy are prevalent around the world, the Brazilian government is seeking to expand and deepen the active participation of citizens in its decisions. The new administration of Luiz Inácio Lula da Silva believes that more democracy is needed to rebuild citizens’ trust in political processes. And it just launched one of its main initiatives, the Participatory Pluriannual Plan (PPA Participativo). The PPA sets the goals and objectives for Brazil over the following four years, and Lula is determined to not only allow but facilitate public participation in its development. 

On May 11, the federal government held the first state plenary for the Participatory PPA, an assembly open to all citizens, social movements and civil society organizations. Participants at the state plenaries are able to discuss proposals and deliberate on the government’s public policies. Over the next two months, government officials will travel to the capitals of the country’s 26 states as well as the federal district (the capital of Brazil) to listen to people present their priorities. If they prefer, people can also submit their suggestions through a digital platform (Decidim, accessible only to people in Brazil) or the Interconselhos Forum, which brings together various councils and civil society groups…(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)”.