Report by Evan D. Peet, Brian G. Vegetabile, Matthew Cefalu, Joseph D. Pane, Cheryl L. Damberg: “Machine learning (ML) can have a significant impact on public policy by modeling complex relationships and augmenting human decisionmaking. However, overconfidence in results and incorrectly interpreted algorithms can lead to peril, such as the perpetuation of structural inequities. In this Perspective, the authors give an overview of ML and discuss the importance of its interpretability. In addition, they offer the following recommendations, which will help policymakers develop trustworthy, transparent, and accountable information that leads to more-objective and more-equitable policy decisions: (1) improve data through coordinated investments; (2) approach ML expecting interpretability, and be critical; and (3) leverage interpretable ML to understand policy values and predict policy impacts…(More)”.
Institutions, Experts & the Loss of Trust
Essay by Henry E. Brady and Kay Lehman Schlozman: “Institutions are critical to our personal and societal well-being. They develop and disseminate knowledge, enforce the law, keep us healthy, shape labor relations, and uphold social and religious norms. But institutions and the people who lead them cannot fulfill their missions if they have lost legitimacy in the eyes of the people they are meant to serve.
Americans’ distrust of Congress is long-standing. What is less well-documented is how partisan polarization now aligns with the growing distrust of institutions once thought of as nonpolitical. Refusals to follow public health guidance about COVID-19, calls to defund the police, the rejection of election results, and disbelief of the press highlight the growing polarization of trust. But can these relationships be broken? And how does the polarization of trust affect institutions’ ability to confront shared problems, like climate change, epidemics, and economic collapse?…(More)”.
Humanizing Science and Engineering for the Twenty-First Century
Essay by Kaye Husbands Fealing, Aubrey Deveny Incorvaia and Richard Utz: “Solving complex problems is never a purely technical or scientific matter. When science or technology advances, insights and innovations must be carefully communicated to policymakers and the public. Moreover, scientists, engineers, and technologists must draw on subject matter expertise in other domains to understand the full magnitude of the problems they seek to solve. And interdisciplinary awareness is essential to ensure that taxpayer-funded policy and research are efficient and equitable and are accountable to citizens at large—including members of traditionally marginalized communities…(More)”.
Our Data, Ourselves
Book by Jacqueline D. Lipton: “Our Data, Ourselves addresses a common and crucial question: What can we as private individuals do to protect our personal information in a digital world? In this practical handbook, legal expert Jacqueline D. Lipton guides readers through important issues involving technology, data collection, and digital privacy as they apply to our daily lives.
Our Data, Ourselves covers a broad range of everyday privacy concerns with easily digestible, accessible overviews and real-world examples. Lipton explores the ways we can protect our personal data and monitor its use by corporations, the government, and others. She also explains our rights regarding sensitive personal data like health insurance records and credit scores, as well as what information retailers can legally gather, and how. Who actually owns our personal information? Can an employer legally access personal emails? What privacy rights do we have on social media? Answering these questions and more, Our Data, Ourselves provides a strategic approach to assuming control over, and ultimately protecting, our personal information…(More)”
Brain capital: A new vector for democracy strengthening
Report by the Brain Capital Alliance: “Democracies are increasingly under siege. Beyond direct external (e.g., warfare) and internal (e.g., populism, extremism) threats to democratic nations, multiple democracy-weakening factors are converging in our modern world. Brain health challenges, including mental, neurologic, and substance use disorders, social determinants of health, long COVID, undesired effects of technology, mis- and disinformation, and educational, health, and gender disparities, are associated with substantial economic and sociopolitical impediments. Herein, we argue that thriving democracies can distinguish themselves through provision of environments that enable each citizen to achieve their full brain health potential conducive to both personal and societal well-being. Gearing policymaking towards equitable and quality brain health may prove essential to combat brain challenges, promote societal cohesion, and boost economic productivity. We outline emerging policy innovations directed at building “pro-democratic brain health” across individual, communal, national, and international levels. While extensive research is warranted to further validate these approaches, brain health-directed policymaking harbors potential as a novel concept for democracy strengthening….(More)”.
Algorithms Quietly Run the City of DC—and Maybe Your Hometown
Article by Khari Johnson: “Washington, DC, IS the home base of the most powerful government on earth. It’s also home to 690,000 people—and 29 obscure algorithms that shape their lives. City agencies use automation to screen housing applicants, predict criminal recidivism, identify food assistance fraud, determine if a high schooler is likely to drop out, inform sentencing decisions for young people, and many other things.
That snapshot of semiautomated urban life comes from a new report from the Electronic Privacy Information Center (EPIC). The nonprofit spent 14 months investigating the city’s use of algorithms and found they were used across 20 agencies, with more than a third deployed in policing or criminal justice. For many systems, city agencies would not provide full details of how their technology worked or was used. The project team concluded that the city is likely using still more algorithms that they were not able to uncover.
The findings are notable beyond DC because they add to the evidence that many cities have quietly put bureaucratic algorithms to work across their departments, where they can contribute to decisions that affect citizens’ lives.
Government agencies often turn to automation in hopes of adding efficiency or objectivity to bureaucratic processes, but it’s often difficult for citizens to know they are at work, and some systems have been found to discriminate and lead to decisions that ruin human lives. In Michigan, an unemployment-fraud detection algorithm with a 93 percent error rate caused 40,000 false fraud allegations. A 2020 analysis by Stanford University and New York University found that nearly half of federal agencies are using some form of automated decisionmaking systems…(More)”.
Calls to “save democracy” won’t work if there is little agreement on what democracy is
Article by Nicholas T. Davis, Kirby Goidel and Keith Gaddie: “One of the most consistent findings in academic research is the existence of something called the principle-implementation gap. People can agree that an idea is perfectly reasonable but will largely reject any meaningful action designed to achieve it. It happens with government spending. People want government to create public goods such as law enforcement, healthcare, and national defense, but oppose new (or additional) taxes. It happens with climate change. The public largely accepts the idea that human-caused climate change is occurring but is unwilling to reduce their reliance on fossil fuels. And it happens with racial equality. People decry racism, but they reject policies that reduce inequality. It also happens, it turns out, with democracy. People claim to love democracy, but willingly sacrifice democratic norms in pursuit of partisan political ends.
A recent New York Times/Siena Poll illustrates this point. Most Americans (71 percent) said they believed American democracy was endangered, but there was little agreement on the nature of the threat or the appropriate corrective action. In response to an open-end question, the most frequently identified threat (mentioned by only 14 percent of respondents) was corruption, not the undermining of democratic norms or the rule of law by former President Donald Trump and the almost 300 Republican candidates running for public office in this year’s midterm elections who deny the legitimacy of the 2020 presidential election.…
Despite much weeping and gnashing of teeth about the “crisis of democracy,” a singular, widely shared understand of democracy is not on the ballot. Or, if it is on the ballot, it appears to be losing.
Why don’t right-wing populist threats against democracy inspire, mobilize, or persuade a public that professes to believe in democracy?
Our new book, Democracy’s Meanings: How the Public Thinks About Democracy and Why It Matters, suggests at least two reasons. First, according to open-ended responses, citizens mostly view democracy through the lens of “freedom” and “elections.” The United States is having an election this fall. It may be less free and less fair in some places than others, but, overall, the electoral apparatus in the United States hasn’t cracked apart – at least in the minds of ordinary voters who don’t pay much attention to the news or care much about the intricacies of electoral law….(More)”.
What Moneyball-for-Everything Has Done to American Culture
Article by Derek Thompson: “…The analytics revolution, which began with the movement known as Moneyball, led to a series of offensive and defensive adjustments that were, let’s say, catastrophically successful. Seeking strikeouts, managers increased the number of pitchers per game and pushed up the average velocity and spin rate per pitcher. Hitters responded by increasing the launch angles of their swings, raising the odds of a home run, but making strikeouts more likely as well. These decisions were all legal, and more important, they were all correct from an analytical and strategic standpoint….
When universal smarts lead to universal strategies, it can lead to a more homogenous product. Take the NBA. When every basketball team wakes up to the calculation that three points is 50 percent more than two points, you get a league-wide blitz of three-point shooting to take advantage of the discrepancy. Before the 2011–12 season, the league as a whole had never averaged more than 20 three-point-shot attempts per game. This year, no team is attempting fewer than 25 threes per game; four teams are attempting more than 40.
As I’ve written before, the quantitative revolution in culture is a living creature that consumes data and spits out homogeneity. Take the music industry. Before the ’90s, music labels routinely lied to Billboard about their sales figures to boost their preferred artists. In 1991, Billboard switched methodologies to use more objective data, including point-of-sale information and radio surveys that didn’t rely on input from the labels. The charts changed overnight. Rock-and-roll bands were toppled, and hip-hop and country surged. When the charts became more honest, they also became more static. Popular songs stick around longer than they used to. One analysis of the history of pop-music styles found that rap and hip-hop have dominated American pop music longer than any other musical genre. As the analytics revolution in music grew, radio playlists became more repetitive, and by some measures, the most popular songs became more similar to one another…(More)”.
Improving Access and Management of Public Transit ITS Data
Report by the National Academies: “With the proliferation of automated vehicle location (AVL), automated passenger counters (APCs), and automated fare collection (AFC), transit agencies are collecting increasingly granular data on service performance, ridership, customer behavior, and financial recovery. While granular intelligent transportation systems (ITS) data can meaningfully improve transit decision-making, transit agencies face many challenges in accessing, validating, storing, and analyzing these data sets. These challenges are made more difficult in that the tools for managing and analyzing transit ITS data generally cannot, at this point, be shared across transit agencies because of variation in data collection systems and data formats. Multiple vendors provide ITS hardware and software, and data formats vary by vendor. Moreover, agencies may employ a patchwork of ITS that has been acquired and modified over time, leading to further consistency challenges.
Standardization of data structures and tools can help address these challenges. Not only can standardization streamline data transfer, validation, and database structuring, it encourages the development of analysis tools that can be used across transit agencies, as has been the case with route and schedule data, standardized in the General Transit Feed Specification (GTFS) format..(More)”.
The Use of Data Science in a National Statistical Office
Paper by Sevgui Erman, Eric Rancourt, Yanick Beaucage, and Andre Loranger: “Objective statistical information is vital to an open and democratic society. It provides a solid foundation so that informed decisions can be made by our elected representatives, businesses, unions, and non-profit organizations, as well as individual citizens. There is a great shift towards a more virtual and digital economy and society. The traditional official statistical systems are centered on surveys, and must be adapted to this new digital reality. National statistical offices have been increasingly embracing non-survey data sources along with data science methods to better serve society.
This paper provides a blueprint for the application of data science in a government organization. It describes how data science enables innovation and the delivery of new high-value, high-quality, relevant, and trusted products that reflect the ever-evolving needs of our society and economy. We discuss practical operational considerations and impactful data science applications that supported the work of Statistics Canada’s analysts and front-line health agencies during the pandemic. We also discuss the innovative use of scanner data in lieu of survey data for large business respondents in the retail industry. We will describe computer vision methodologies, including machine learning models used to detect the start of buildings construction from satellite imagery, greenhouse area and greenhouse production, as well as crop types detection. Data science and machine learning methods have tremendous potential, and their ethical use is of primary importance. We conclude the paper with a forward-facing view of responsible data science use in statistical production.