Library of Congress Launches Crowdsourcing Platform

Curated on Posted on by Stefaan Verhulst

Matt Enis at the Library Journal: “The Library of Congress (LC) last month launched crowd.loc.gov, a new crowdsourcing platform that will improve discovery and access to the Library’s digital collections with the help of volunteer transcription and tagging. The project kicked off with the “Letters to Lincoln Challenge,” a campaign encouraging volunteers to transcribe 10,000 digitized versions of documents written by or to Abraham Lincoln, which will make these materials full-text searchable for the first time….

The new project is the earliest example of LC’s new Digital Strategy, which complements the library’s new 2019–23 strategic plan. Announced in October, the strategic plan, “Enriching the User Experience,” outlines four high-level goals—expanding access, enhancing services, optimizing resources, and measuring results—while the digital strategy outlines how LC plans to accomplish these goals with its digital resources, described as “throwing open the treasure chest, connecting, and investing in our future”…

LC aims to use crowdsourcing to enrich the user experience in two key ways, Zwaard said.

“First, it helps with the legibility of our collections,” she explained. “The Library of Congress is home to so many historic treasures, but the handwriting can be hard to read…. For example, we have this amazing letter from Abraham Lincoln to his first fiancée. It’s really quite lovely, but at a glance, if you’re not familiar with historic handwriting, it’s hard to read.”…

Second, crowdsourcing “invites people into the collections,” she added. “The library is very optimized around answering specific research questions. One of the things we’re thinking about is how to serve users who don’t have a specific research question—who just want to see all of the cool stuff. We have so much cool stuff! But it can be hard for people to find purchase when they are just browsing and don’t have anything specific in mind. One of the ways we can [showcase interesting content] is by offering them a window into the collections by asking for their help.”…

To facilitate ongoing engagement with these varied projects, LC has set up an online forum on History Hub, a site hosted by the National Archives, to encourage crowd.loc.gov participants to ask questions, discuss projects, and meet other volunteers. …

Crowd.loc.gov is not LC’s first crowdsourcing project. Followers of the library’s official Flickr account have added tens of thousands of descriptive tags to digitized historical photos since the account debuted in 2007. And last year, the debut of labs.loc.gov—which aims to encourage creative use of LOC’s digital collections—included the Beyond Words crowdsourcing project developed by LC software developer Tong Wang….(More)”

Nudging compliance in government: A human-centered approach to public sector program design

Curated on Posted on by Stefaan Verhulst

Article by Michelle Cho, Joshua Schoop, Timothy Murphy: “What are the biggest challenges facing government? Bureaucracy? Gridlock? A shrinking pool of resources?

Chances are compliance—when people act in accordance with preset rules, policies, and/or expectations—doesn’t top the list for many. Yet maybe it should. Compliance touches nearly every aspect of public policy implementation. Over the past 10 years, US government spending on compliance reached US$7.5 billion.

Even the most sophisticated and well-planned policies often require cooperation and input from real humans to be successful. From voluntary tax filing at the Internal Revenue Service (IRS) to reducing greenhouse emissions at the Environmental Protection Agency (EPA), to achieving the public policy outcomes decision-makers intend, compliance is fundamental.

Consider these examples of noncompliance and their costs:

  • Taxes. By law, the IRS requires all income-earning, eligible constituents to file and pay their owed taxes. Tax evasion—the illegal nonpayment or underpayment of tax—cost the federal government an average of US$458 billion per year between 2008 and 2010.3 The IRS believes it will recover just 11 percent of the amount lost in that time frame.
  • The environment. The incorrect disposal of recyclable materials has cost more than US$744 million in the state of Washington since 2009.4 The city audit in San Diego found that 76 percent of materials disposed of citywide are recyclable and estimates that those recyclables could power 181,000 households for a year or conserve 3.4 million barrels of oil.5

Those who fail to comply with these rules could face direct and indirect consequences, including penalties and even jail time. Yet a significant subset of the population still behaves in a noncompliant manner. Why?

Behavioral sciences offer some clues. Through the combination of psychology, economics, and neuroscience, behavioral sciences demonstrate that people do not always do what is asked of them, even when it seems in their best interest to do so. Often, people choose a noncompliant path because of one of these reasons: They are unaware of their improper behavior, they find the “right” choice is too complex to decipher, or they simply are not intrinsically motivated to make the compliant choice.

For any of these reasons, when a cognitive hurdle emerges, some people resort to noncompliant behavior. But these hurdles can be overcome. Policymakers can use these same behavioral insights to understand why noncompliance occurs and alternatively, employ behavioral-inspired tools to encourage compliant behavior in a more agile and resource-efficient fashion.

In this spirit, leaders can take a more human-centered approach to program design by using behavioral science lessons to develop policies and programs in a manner that can make compliance easier and more appealing. In our article, we discuss three common reasons behind noncompliance and how better, more human-centered design can help policymakers achieve more positive results….(More)”.

Quantum Information Science: Applications, Global Research and Development, and Policy Considerations

Curated on Posted on by Stefaan Verhulst

Report from the Congressional Research Service: “Quantum information science (QIS) combines elements of mathematics, computer science, engineering, and physical sciences, and has the potential to provide capabilities far beyond what is possible with the most advanced technologies available today.
Although much of the press coverage of QIS has been devoted to quantum computing, there is more to QIS. Many experts divide QIS technologies into three application areas:

  • Sensing and metrology,
  • Communications, and
  • Computing and simulation.

… Today, QIS is a component of the National Strategic Computing Initiative (Presidential Executive Order 13702), which was established in 2015. Most recently, in September 2018, the National Science and Technology Council issued the National Strategic Overview for Quantum Information Science. The policy opportunities identified in this strategic overview include:

  • choosing a science-first approach to QIS,
  • creating a “quantum-smart” workforce,
  • deepening engagement with the quantum industry,
  • providing critical infrastructure,
  • maintaining national security and economic growth, and
  • advancing international cooperation.

This report provides an overview of QIS technologies: sensing and metrology, communications, and computing and simulation. It also includes examples of existing and potential future applications; brief summaries of funding and selected R&D initiatives in the United States and elsewhere around the world; a description of U.S. congressional activity; and a discussion of related policy considerations….(More)”.

What difference does data make? Data management and social change

Curated on Posted on by Stefaan Verhulst

Paper by Morgan E. Currie and Joan M. Donovan: “The purpose of this paper is to expand on emergent data activism literature to draw distinctions between different types of data management practices undertaken by groups of data activists.

The authors offer three case studies that illuminate the data management strategies of these groups. Each group discussed in the case studies is devoted to representing a contentious political issue through data, but their data management practices differ in meaningful ways. The project Making Sense produces their own data on pollution in Kosovo. Fatal Encounters collects “missing data” on police homicides in the USA. The Environmental Data Governance Initiative hopes to keep vulnerable US data on climate change and environmental injustices in the public domain.

In analysing our three case studies, the authors surface how temporal dimensions, geographic scale and sociotechnical politics influence their differing data management strategies….(More)”.

Learning Through Citizen Science: Enhancing Opportunities by Design

Curated on Posted on by Stefaan Verhulst

National Academies: “Scientific research that involves nonscientists contributing to research processes – also known as ‘citizen science’ – supports participants’ learning, engages the public in science, contributes to community scientific literacy, and can serve as a valuable tool to facilitate larger scale research, says a new report from the National Academies of Sciences, Engineering, and Medicine.  If one of the goals of a citizen science project is to advance learning, designers should plan for it by defining intended learning outcomes and using evidence-based strategies to reach those outcomes.

“This report affirms that citizen science projects can help participants learn scientific practices and content, but most likely only if the projects are designed to support learning,” says Rajul Pandya, chair of the committee that wrote the report and director, Thriving Earth Exchange, AGU.  

The term “citizen science” can be applied to a wide variety of projects that invite nonscientists to engage in doing science with the intended goal of advancing scientific knowledge or application. For example, a citizen science project might engage community members in collecting data to monitor the health of a local stream. As another example, among the oldest continuous organized datasets in the United States are records kept by farmers and agricultural organizations that document the timing of important events, such as sowing, harvests, and pest outbreaks.

Citizen science can support science learning in several ways, the report says. It offers people the opportunity to participate in authentic scientific endeavors, encourages learning through projects conducted in real-world contexts, supports rich social interaction that deepens learning, and engages participants with real data. Citizen science also includes projects that grow out of a community’s desire to address an inequity or advance a priority. For example, the West-Oakland Indicators Project, a community group in Oakland, Calif., self-organizes to collect and analyze air quality data and uses that data to address trucking in and around schools to reduce local children’s exposure to air pollution. When communities can work alongside scientists to advance their priorities, enhanced community science literacy is one possible outcome….

In order to maximize learning outcomes, the report recommends that designers and practitioners of citizen science projects should intentionally build them for learning. This involves knowing the audience; intentionally designing for diversity; engaging stakeholders in the design; supporting multiple kinds of participant engagement; encouraging social interaction; building learning supports into the project; and iteratively improving projects through evaluation and refinement.  Engaging stakeholders and participants in design and implementation results in more learning for all participants, which can support other project goals. 

The report also lays out a research agenda that can help to build the field of citizen science by filling gaps in the current understanding of how citizen science can support science learning and enhance science education. Researchers should consider three important factors: citizen science extends beyond academia and therefore, evidence for practices that advance learning can be found outside of peer-reviewed literature; research should include attention to practice and link theory to application; and attention must be given to diversity in all research, including ensuring broad participation in the design and implementation of the research. Pursuing new lines of inquiry can help add value to the existing research, make future research more productive, and provide support for effective project implementation….(More)”.


Waze-fed AI platform helps Las Vegas cut car crashes by almost 20%

Curated on Posted on by Stefaan Verhulst

Liam Tung at ZDNet: “An AI-led, road-safety pilot program between analytics firm Waycare and Nevada transportation agencies has helped reduce crashes along the busy I-15 in Las Vegas.

The Silicon Valley Waycare system uses data from connected cars, road cameras and apps like Waze to build an overview of a city’s roads and then shares that data with local authorities to improve road safety.

Waycare struck a deal with Google-owned Waze earlier this year to “enable cities to communicate back with drivers and warn of dangerous roads, hazards, and incidents ahead”. Waze’s crowdsourced data also feeds into Waycare’s traffic management system, offering more data for cities to manage traffic.

Waycare has now wrapped up a year-long pilot with the Regional Transportation Commission of Southern Nevada (RTC), Nevada Highway Patrol (NHP), and the Nevada Department of Transportation (NDOT).

RTC reports that Waycare helped the city reduce the number of primary crashes by 17 percent along the Interstate 15 Las Vegas.

Waycare’s data, as well as its predictive analytics, gave the city’s safety and traffic management agencies the ability to take preventative measures in high risk areas….(More)”.

Using Artificial Intelligence to Promote Diversity

Curated on Posted on by Stefaan Verhulst

Paul R. Daugherty, H. James Wilson, and Rumman Chowdhury at MIT Sloan Management Review:  “Artificial intelligence has had some justifiably bad press recently. Some of the worst stories have been about systems that exhibit racial or gender bias in facial recognition applications or in evaluating people for jobs, loans, or other considerations. One program was routinely recommending longer prison sentences for blacks than for whites on the basis of the flawed use of recidivism data.

But what if instead of perpetuating harmful biases, AI helped us overcome them and make fairer decisions? That could eventually result in a more diverse and inclusive world. What if, for instance, intelligent machines could help organizations recognize all worthy job candidates by avoiding the usual hidden prejudices that derail applicants who don’t look or sound like those in power or who don’t have the “right” institutions listed on their résumés? What if software programs were able to account for the inequities that have limited the access of minorities to mortgages and other loans? In other words, what if our systems were taught to ignore data about race, gender, sexual orientation, and other characteristics that aren’t relevant to the decisions at hand?

AI can do all of this — with guidance from the human experts who create, train, and refine its systems. Specifically, the people working with the technology must do a much better job of building inclusion and diversity into AI design by using the right data to train AI systems to be inclusive and thinking about gender roles and diversity when developing bots and other applications that engage with the public.

Design for Inclusion

Software development remains the province of males — only about one-quarter of computer scientists in the United States are women— and minority racial groups, including blacks and Hispanics, are underrepresented in tech work, too.  Groups like Girls Who Code and AI4ALL have been founded to help close those gaps. Girls Who Code has reached almost 90,000 girls from various backgrounds in all 50 states,5 and AI4ALL specifically targets girls in minority communities….(More)”.

Recalculating GDP for the Facebook age

Curated on Posted on by Stefaan Verhulst

Gillian Tett at the Financial Times: How big is the impact of Facebook on our lives? That question has caused plenty of hand-wringing this year, as revelations have tumbled out about the political influence of Big Tech companies.

Economists are attempting to look at this question too — but in a different way. They have been quietly trying to calculate the impact of Facebook on gross domestic product data, ie to measure what our social-media addiction is doing to economic output….

Kevin Fox, an Australian economist, thinks there is. Working with four other economists, including Erik Brynjolfsson, a professor at MIT, he recently surveyed consumers to see what they would “pay” for Facebook in monetary terms, concluding conservatively that this was about $42 a month. Extrapolating this to the wider economy, he then calculated that the “value” of the social-media platform is equivalent to 0.11 per cent of US GDP. That might not sound transformational. But this week Fox presented the group’s findings at an IMF conference on the digital economy in Washington DC and argued that if Facebook activity had been counted as output in the GDP data, it would have raised the annual average US growth rate from 1.83 per cent to 1.91 per cent between 2003 and 2017. The number would rise further if you included other platforms – researchers believe that “maps” and WhatsApp are particularly important – or other services.  Take photographs.

Back in 2000, as the group points out, about 80 billion photos were taken each year at a cost of 50 cents a picture in camera and processing fees. This was recorded in GDP. Today, 1.6 trillion photos are taken each year, mostly on smartphones, for “free”, and excluded from that GDP data. What would happen if that was measured too, along with other types of digital services?

The bad news is that there is no consensus among economists on this point, and the debate is still at a very early stage. … A separate paper from Charles Hulten and Leonard Nakamura, economists at the University of Maryland and Philadelphia Fed respectively, explained another idea: a measurement known as “EGDP” or “Expanded GDP”, which incorporates “welfare” contributions from digital services. “The changes wrought by the digital revolution require changes to official statistics,” they said.

Yet another paper from Nakamura, co-written with Diane Coyle of Cambridge University, argued that we should also reconfigure the data to measure how we “spend” our time, rather than “just” how we spend our money. “To recapture welfare in the age of digitalisation, we need shadow prices, particularly of time,” they said. Meanwhile, US government number-crunchers have been trying to measure the value of “free” open-source software, such as R, Python, Julia and Java Script, concluding that if captured in statistics these would be worth about $3bn a year. Another team of government statisticians has been trying to value the data held by companies – this estimates, using one method, that Amazon’s data is currently worth $125bn, with a 35 per cent annual growth rate, while Google’s is worth $48bn, growing at 22 per cent each year. It is unlikely that these numbers – and methodologies – will become mainstream any time soon….(More)”.

Using Data to Raise the Voices of Working Americans

Curated on Posted on by Stefaan Verhulst

Ida Rademacher at the Aspen Institute: “…At the Aspen Institute Financial Security Program, we sense a growing need to ground these numbers in what people experience day-to-day. We’re inspired by projects like the Financial Diaries that helped create empathy for what the statistics mean. …the Diaries was a time-delimited project, and the insights we can gain from major banking institutions are somewhat limited in their ability to show the challenges of economically marginalized populations. That’s why we’ve recently launched a consumer insights initiative to develop and translate a more broadly sourced set of data that lifts the curtain on the financial lives of low- and moderate-income US consumers. What does it really mean to lack $400 when you need it? How do people cope? What are the aspirations and anxieties that fuel choices? Which strategies work and which fall flat? Our work exists to focus the dialogue about financial insecurity by keeping an ear to the ground and amplifying what we hear. Our ultimate goal: Inspire new solutions that react to reality, ones that can genuinely improve the financial well-being of many.

Our consumer insights initiative sees power in partnerships and collaboration. We’re building a big tent for a range of actors to query and share what their data says: private sector companies, public programs, and others who see unique angles into the financial lives of low- and moderate-income households. We are creating a new forum to lift up these firms serving consumers – and in doing so, we’re raising the voices of consumers themselves.

One example of this work is our Consumer Insights Collaborative (CIC), a group of nine leading non-profits from across the country. Each has a strong sense of challenges and opportunities on the ground because every day their work brings them face-to-face with a wide array of consumers, many of whom are low- and moderate-income families. And most already work independently to learn from their data. Take EARN and its Big Data on Small Savings project; the Financial Clinic’s insights series called Change Matters; Mission Asset Fund’s R&D Lab focused on human-centered design; and FII which uses data collection as part of its main service.

Through the CIC, they join forces to see more than any one nonprofit can on their own. Together CIC members articulate common questions and synthesize collective answers. In the coming months we will publish a first-of-its-kind report on a jointly posed question: What are the dimensions and drivers of short term financial stability?

An added bonus of partnerships like the CIC is the community of practice that naturally emerges. We believe that data scientists from all walks can, and indeed must, learn from each other to have the greatest impact. Our initiative especially encourages cooperative capacity-building around data security and privacy. We acknowledge that as access to information grows, so does the risk to consumers themselves. We endorse collaborative projects that value ethics, respect, and integrity as much as they value cross-organizational learning.

As our portfolio grows, we will invite an even broader network to engage. We’re already working with NEST Insights to draw on NEST’s extensive administrative data on retirement savings, with an aim to understand more about the long-term implications of non-traditional work and unstable household balance sheets on financial security….(More)”.

Democracy is an information system

Curated on Posted on by Stefaan Verhulst

Bruce Shneier on Security: “That’s the starting place of our new paper: “Common-Knowledge Attacks on Democracy.” In it, we look at democracy through the lens of information security, trying to understand the current waves of Internet disinformation attacks. Specifically, we wanted to explain why the same disinformation campaigns that act as a stabilizing influence in Russia are destabilizing in the United States.

The answer revolves around the different ways autocracies and democracies work as information systems. We start by differentiating between two types of knowledge that societies use in their political systems. The first is common political knowledge, which is the body of information that people in a society broadly agree on. People agree on who the rulers are and what their claim to legitimacy is. People agree broadly on how their government works, even if they don’t like it. In a democracy, people agree about how elections work: how districts are created and defined, how candidates are chosen, and that their votes count­ — even if only roughly and imperfectly.

We contrast this with a very different form of knowledge that we call contested political knowledge,which is, broadly, things that people in society disagree about. Examples are easy to bring to mind: how much of a role the government should play in the economy, what the tax rules should be, what sorts of regulations are beneficial and what sorts are harmful, and so on.

This seems basic, but it gets interesting when we contrast both of these forms of knowledge across autocracies and democracies. These two forms of government have incompatible needs for common and contested political knowledge.

For example, democracies draw upon the disagreements within their population to solve problems. Different political groups have different ideas of how to govern, and those groups vie for political influence by persuading voters. There is also long-term uncertainty about who will be in charge and able to set policy goals. Ideally, this is the mechanism through which a polity can harness the diversity of perspectives of its members to better solve complex policy problems. When no-one knows who is going to be in charge after the next election, different parties and candidates will vie to persuade voters of the benefits of different policy proposals.

But in order for this to work, there needs to be common knowledge both of how government functions and how political leaders are chosen. There also needs to be common knowledge of who the political actors are, what they and their parties stand for, and how they clash with each other. Furthermore, this knowledge is decentralized across a wide variety of actors­ — an essential element, since ordinary citizens play a significant role in political decision making.

Contrast this with an autocracy….(More)”.