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.
Public Access to Advance Equity
Essay by Alondra Nelson, Christopher Marcum and Jedidah Isler: “Open science began in the scientific community as a movement committed to making all aspects of research freely available to all members of society.
As a member of the Organisation for Economic Co-operation and Development (OECD), the United States is committed to promoting open science, which the OECD defines as “unhindered access to scientific articles, access to data from public research, and collaborative research enabled by information and communication technology tools and incentives.”
At the White House Office of Science and Technology Policy (OSTP), we have been inspired by the movement to push for openness in research by community activists, researchers, publishers, higher-education leaders, policymakers, patient advocates, scholarly associations, librarians, open-government proponents, philanthropic organizations, and the public.
Open science is an essential part of the Biden-Harris administration’s broader commitment to providing public access to data, publications, and the other important products of the nation’s taxpayer-supported research and innovation enterprise. We look to the lessons, methods, and products of open science to deliver on this commitment to policy that advances equity, accelerates discovery and innovation, provides opportunities for all to participate in research, promotes public trust, and is evidence-based. Here, we detail some of the ways OSTP is working to expand the American public’s access to the federal research and development ecosystem, and to ensure it is open, equitable, and secure…(More)”.
Hyperlocal: Place Governance in a Fragmented World
Book by By Jennifer S. Vey and Nate Storring: “Many of America’s downtowns, waterfronts, and innovation districts have experienced significant revitalization and reinvestment in recent years, but concentrated poverty and racial segregation remain persistent across thousands of urban, suburban, and rural neighborhoods. The coronavirus pandemic magnified this sustained and growing landscape of inequality.
Uneven patterns of economic growth and investment require a shift in how communities are governed and managed. This shift must take into account the changing socioeconomic realities of regions and the pressing need to bring inclusive economic growth and prosperity to more people and places.
In this context, place-based (“hyperlocal”) governance structures in the United States and around the globe have been both part of the problem and part of the solution. These organizations range from community land trusts to business improvement districts to neighborhood councils. However, very little systematic research has documented the full diversity and evolution of these organizations as part of one interrelated field. Hyperlocal helps fill that gap by describing the challenges and opportunities of “place governance.”
The chapters in Hyperlocal explore both the tensions and benefits associated with governing places in an increasingly fragmented—aneholders a structure through which to share ideas, voice concerns, advocate for investments, and co-design strategies with others both inside and outside their place. They also discuss how place governance can serve the interests of some stakeholders over others, in turn exacerbating wealth-based inequities within and across communities. Finally, they highlight innovative financing, organizing, and ownership models for creating and sustaining more effective and inclusive place governance structures…(More)”.
Evaluating Social Innovation Prototypes
Guide by Social Innovation Canada: “…practical resource for those involved with social research and development and who would like to create, test, and learn from prototypes. This how-to guide explores 12 principles to guide the testing process, five key steps for carrying out the process, and includes tables that summarize a variety of prototyping techniques, evaluation methods, and sampling strategies. Learn how to effectively use prototypes and test promising solutions to address social challenges….(More)”
Can Social Media Rhetoric Incite Hate Incidents? Evidence from Trump’s “Chinese Virus” Tweets
Paper by Andy Cao, Jason M. Lindo & Jiee Zhong: “We will investigate whether Donald Trump’s “Chinese Virus” tweets contributed to the rise of anti-Asian incidents. We find that the number of incidents spiked following Trump’s initial “Chinese Virus” tweets and the subsequent dramatic rise in internet search activity for the phrase. Difference-in-differences and event-study analyses leveraging spatial variation indicate that this spike in anti-Asian incidents was significantly more pronounced in counties that supported Donald Trump in the 2016 presidential election relative to those that supported Hillary Clinton. We estimate that anti-Asian incidents spiked by 4000 percent in Trump-supporting counties, over and above the spike observed in Clinton-supporting counties…(More)”.
Philanthropy to Protect US Democracy
Essay by Lukas Haynes: “…Given the threat of election subversion, philanthropists who care about democracy across the political spectrum must now deploy donations as effectively as they can. In their seminal book, Money Well Spent: A Strategic Plan for Smart Philanthropy, Paul Brest and Hal Harvey argue that generating “alternative solutions” to hard problems “requires creativity or innovation akin to that of a scientist or engineer—creativity that is goal-oriented, that aims to come up with pragmatic solutions to a problem.”
In seeking the most effective solutions, Brest and Harvey do not find that nonpartisan, charitable efforts are the only legitimate form of strategic giving. Instead, they encourage donors to identify clear problem-solving goals, sound strategy, and clarity about risk tolerance.
Given the concerted attack on democratic norms by political candidates, there is no more effective alternative at hand than using political donations to defeat those candidates. If it is not already part of donors’ philanthropic toolkit to protect democracy, it needs to be and soon.
Once Big Lie-promoting candidates win and take power over elections, it will be too late to repeal their authority, especially in states where Republicans control the state legislatures. Should they successfully subvert a national presidential election in a deeply polarized nation, the United States will have crossed an undemocratic Rubicon no well-intentioned American wants to witness. So what are the most effective ways for political donors to respond to this perilous moment?…(More)”.
Charting an Equity-Centered Public Health Data System
Introduction to Special Issue by Alonzo L. Plough: “…The articles in this special issue were written with that vision in mind; several of them even informed the commission’s deliberations. Each article addresses an issue essential to the challenge of building an equity-focused public health data system:
- Why Equity Matters in Public Health Data. Authors Anita Chandra, Laurie T. Martin, Joie D. Acosta, Christopher Nelson, Douglas Yeung, Nabeel Qureshi, and Tara Blagg explore where and how equity has been lacking in public health data and the implications of considering equity to the tech and data sectors.
- What is Public Health Data? As authors Joie D. Acosta, Anita Chandra, Douglas Yeung, Christopher Nelson, Nabeel Qureshi, Tara Blagg, and Laurie T. Martin explain, good public health data are more than just health data. We need to reimagine the types of data we collect and from where, as well data precision, granularity, timeliness, and more.
- Public Health Data and Special Populations. People of color, women, people with disabilities, and people who are lesbian, gay bisexual trans-gendered queer are among the populations that have been inconsistently represented in public health data over time. This article by authors Tina J. Kauh and Maryam Khojasteh reviews findings for each population, as well as commonalities across populations.
- Public health data interoperability and connectedness. What are challenges to connecting public health data swiftly yet accurately? What gaps need to be filled? How can the data and tech sector help address these issues? These are some of the questions explored in this article by authors Laurie T. Martin, Christopher Nelson, Douglas Yeung, Joie D. Acosta, Nabeel Qureshi, Tara Blagg, and Anita Chandra.
- Integrating Tech and Data Expertise into the Public Health Workforce. This article by authors Laurie T. Martin, Anita Chandra, Christopher Nelson, Douglas Yeung, Joie D. Acosta, Nabeel Qureshi, and Tara Blag envisions what a tech-savvy public health workforce will look like and how it can be achieved through new workforce models, opportunities to expand capacity, and training….(More)”.
The Exploited Labor Behind Artificial Intelligence
Essay by Adrienne Williams, Milagros Miceli, and Timnit Gebru: “The public’s understanding of artificial intelligence (AI) is largely shaped by pop culture — by blockbuster movies like “The Terminator” and their doomsday scenarios of machines going rogue and destroying humanity. This kind of AI narrative is also what grabs the attention of news outlets: a Google engineer claiming that its chatbot was sentient was among the most discussed AI-related news in recent months, even reaching Stephen Colbert’s millions of viewers. But the idea of superintelligent machines with their own agency and decision-making power is not only far from reality — it distracts us from the real risks to human lives surrounding the development and deployment of AI systems. While the public is distracted by the specter of nonexistent sentient machines, an army of precarized workers stands behind the supposed accomplishments of artificial intelligence systems today.
Many of these systems are developed by multinational corporations located in Silicon Valley, which have been consolidating power at a scale that, journalist Gideon Lewis-Kraus notes, is likely unprecedented in human history. They are striving to create autonomous systems that can one day perform all of the tasks that people can do and more, without the required salaries, benefits or other costs associated with employing humans. While this corporate executives’ utopia is far from reality, the march to attempt its realization has created a global underclass, performing what anthropologist Mary L. Gray and computational social scientist Siddharth Suri call ghost work: the downplayed human labor driving “AI”.
Tech companies that have branded themselves “AI first” depend on heavily surveilled gig workers like data labelers, delivery drivers and content moderators. Startups are even hiring people to impersonate AI systems like chatbots, due to the pressure by venture capitalists to incorporate so-called AI into their products. In fact, London-based venture capital firm MMC Ventures surveyed 2,830 AI startups in the EU and found that 40% of them didn’t use AI in a meaningful way…(More)”.
Innovative Data Science Approaches to Identify Individuals, Populations, and Communities at High Risk for Suicide
Report by the National Academies of Sciences, Engineering, and Medicine: “Emerging real-time data sources, together with innovative data science techniques and methods – including artificial intelligence and machine learning – can help inform upstream suicide prevention efforts. Select social media platforms have proactively deployed these methods to identify individual platform users at high risk for suicide, and in some cases may activate local law enforcement, if needed, to prevent imminent suicide. To explore the current scope of activities, benefits, and risks of leveraging innovative data science techniques to help inform upstream suicide prevention at the individual and population level, the Forum on Mental Health and Substance Use Disorders of the National Academies of Sciences, Engineering, and Medicine convened a virtual workshop series consisting of three webinars held on April 28, May 12, and June 30, 2022. This Proceedings highlights presentations and discussions from the workshop…(More)”