Paper by Natalia Garbiras-Díaz and Mateo Montenegro: “Can information and communication technologies help citizens monitor their elections? We analyze a large-scale field experiment designed to answer this question in Colombia. We leveraged Facebook advertisements sent to over 4 million potential voters to encourage citizen reporting of electoral irregularities. We also cross-randomized whether candidates were informed about the campaign in a subset of municipalities. Total reports, and evidence-backed ones, experienced a large increase. Across a wide array of measures, electoral irregularities decreased. Finally, the reporting campaign reduced the vote share of candidates dependent on irregularities. This light-touch intervention is more cost-effective than monitoring efforts traditionally used by policymakers…(More)”.
Virtual Public Involvement: Lessons from the COVID-19 Pandemic
Report by the National Academies: “During the COVID-19 pandemic, transportation agencies’ most used public-engagement tools were virtual public meetings, social media, dedicated project websites or webpages, email blasts, and electronic surveys. As the pandemic subsides, virtual and hybrid models continue to provide opportunities and challenges.
The TRB National Cooperative Highway Research Program’s NCHRP Web-Only Document 349: Virtual Public Involvement: Lessons from the COVID-19 Pandemic discusses gaps that need to be addressed so that transportation agencies can better use virtual tools and techniques to facilitate two-way communication with the public…(More)”.
Smart OCR – Advancing the Use of Artificial Intelligence with Open Data
Article by Parth Jain, Abhinay Mannepalli, Raj Parikh, and Jim Samuel: “Optical character recognition (OCR) is growing at a projected compounded annual growth rate (CAGR) of 16%, and is expected to have a value of 39.7 billion USD by 2030, as estimated by Straits research. There has been a growing interest in OCR technologies over the past decade. Optical character recognition is the technological process for transforming images of typed, handwritten, scanned, or printed texts into machine-encoded and machine-readable texts (Tappert, et al., 1990). OCR can be used with a broad range of image or scan formats – for example, these could be in the form of a scanned document such as a .pdf file, a picture of a piece of paper in .png or .jpeg format, or images with embedded text, such as characters on a coffee cup, title on the cover page of a book, the license number on vehicular plates, and images of code on websites. OCR has proven to be a valuable technological process for tackling the important challenge of transforming non-machine-readable data into machine readable data. This enables the use of natural language processing and computational methods on information-rich data which were previously largely non-processable. Given the broad array of scanned and image documents in open government data and other open data sources, OCR holds tremendous promise for value generation with open data.
Open data has been defined as “being data that is made freely available for open consumption, at no direct cost to the public, which can be efficiently located, filtered, downloaded, processed, shared, and reused without any significant restrictions on associated derivatives, use, and reuse” (Chidipothu et al., 2022). Large segments of open data contain images, visuals, scans, and other non-machine-readable content. The size and complexity associated with the manual analysis of such content is prohibitive. The most efficient way would be to establish standardized processes for transforming documents into their OCR output versions. Such machine-readable text could then be analyzed using a range of NLP methods. Artificial Intelligence (AI) can be viewed as being a “set of technologies that mimic the functions and expressions of human intelligence, specifically cognition and logic” (Samuel, 2021). OCR was one of the earliest AI technologies implemented. The first ever optical reader to identify handwritten numerals was the advanced reading machine “IBM 1287,” presented at the World Fair in New York in 1965 (Mori, et al., 1990). The value of open data is well established – however, the extent of usefulness of open data is dependent on “accessibility, machine readability, quality” and the degree to which data can be processed by using analytical and NLP methods (data.gov, 2022; John, et al., 2022)…(More)”
Leveraging Data to Improve Racial Equity in Fair Housing
Report by Temilola Afolabi: “Residential segregation is related to inequalities in education, job opportunities, political power, access to credit, access to health care, and more. Steering, redlining, mortgage lending discrimination, and other historic policies have all played a role in creating this state of affairs.
Over time, federal efforts including the Fair Housing Act and Home Mortgage Disclosure Act have been designed to improve housing equity in the United States. While these laws have not been entirely effective, they have made new kinds of data available—data that can shed light on some of the historic drivers of housing inequity and help inform tailored solutions to their ongoing impact.
This report explores a number of current opportunities to strengthen longstanding data-driven tools to address housing equity. The report also shows how the effects of mortgage lending discrimination and other historic practices are still being felt today. At the same time, it outlines opportunities to apply data to increase equity in many areas related to the homeownership gap, including negative impacts on health and well-being, socioeconomic disparities, and housing insecurity….(More)”.
Closing the gap between user experience and policy design
Article by Cecilia Muñoz & Nikki Zeichner: “..Ask the average American to use a government system, whether it’s for a simple task like replacing a Social Security Card or a complicated process like filing taxes, and you’re likely to be met with groans of dismay. We all know that government processes are cumbersome and frustrating; we have grown used to the government struggling to deliver even basic services.
Unacceptable as the situation is, fixing government processes is a difficult task. Behind every exhausting government application form or eligibility screener lurks a complex policy that ultimately leads to what Atlantic staff writer Anne Lowrey calls the time tax, “a levy of paperwork, aggravation, and mental effort imposed on citizens in exchange for benefits that putatively exist to help them.”
Policies are complex, in part because they each represent many voices. The people who we call policymakers are key actors in governments and elected officials at every level from city councils to the U.S. Congress. As they seek to solve public problems like child poverty or improving economic mobility, they consult with experts at government agencies, researchers in academia, and advocates working directly with affected communities. They also hear from lobbyists from affected industries. They consider current events and public sentiments. All of these voices and variables, representing different and sometimes conflicting interests, contribute to the policies that become law. And as a result, laws reflect a complex mix of objectives. After a new law is in place, relevant government agencies are responsible for implementing them by creating new programs and services to carry them out. Complex policies then get translated into complex processes and experiences for members of the public. They become long application forms, unclear directions, and too often, barriers that keep people from accessing a benefit.
Policymakers and advocates typically declare victory when a new policy is signed into law; if they think about the implementation details at all, that work mostly happens after the ink is dry. While these policy actors may have deep expertise in a given issue area, or deep understanding of affected communities, they often lack experience designing services in a way that will be easy for the public to navigate…(More)”.
Data Analysis for Social Science: A Friendly and Practical Introduction
Book by Elena Llaudet and Kosuke Imai: “…provides a friendly introduction to the statistical concepts and programming skills needed to conduct and evaluate social scientific studies. Using plain language and assuming no prior knowledge of statistics and coding, the book provides a step-by-step guide to analyzing real-world data with the statistical program R for the purpose of answering a wide range of substantive social science questions. It teaches not only how to perform the analyses but also how to interpret results and identify strengths and limitations. This one-of-a-kind textbook includes supplemental materials to accommodate students with minimal knowledge of math and clearly identifies sections with more advanced material so that readers can skip them if they so choose…(More)”.
Machine Learning in Public Policy: The Perils and the Promise of Interpretability
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)”