Building and Sustaining State Data Integration Efforts: Legislation, Funding, and Strategies

Curated on Posted on by Stefaan Verhulst

Policy Report by AISP: “The economic and social impacts of the COVID-19 pandemic have heightened demand for cross-agency data capacity, as policymakers are forced to reconcile the need for expanded services with extreme fiscal constraints. In this context, integrated data systems (IDS) – also commonly referred to as data hubs, data collaboratives, or state longitudinal data systems – are a valuable resource for data-informed decision making across agencies. IDS utilize standard governance processes and legal agreements to grant authority for routine, responsible use of linked data, and institutionalize roles across partners with shared priorities.

Despite these benefits, creating and sustaining IDS remains a challenge for many states. Legislation and executive action can be powerful mechanisms to overcome this challenge and promote the use of cross-agency data for public good. Legislative and/or executive actions on data sharing can:
– Require data sharing to address a specific state policy priority
– Mandate oversight and planning activities to promote a state data sharing strategy
– Grant authority to a particular office or agency to lead cross-agency data sharing

This brief is organized in three parts. First, we offer examples of these three approaches from states that have used legislation and/or executive orders to enable data integration, as well as key considerations related to each. Second, we discuss state and federal funding opportunities that can help in implementing legislative or executive actions on data sharing and enhancing long-term sustainability of data sharing efforts. Third, we offer five foundational strategies to ensure that legislative or executive action is both ethical and effective…(More)”.

We Need to Reimagine the Modern Think Tank

Curated on Posted on by Stefaan Verhulst

Article by Emma Vadehra: “We are in the midst of a great realignment in policymaking. After an era-defining pandemic, which itself served as backdrop to a generations-in-the-making reckoning on racial injustice, the era of policy incrementalism is giving way to broad, grassroots demands for structural change. But elected officials are not the only ones who need to evolve. As the broader policy ecosystem adjusts to a post-2020 world, think tanks that aim to provide the intellectual backbone to policy movements—through research, data analysis, and evidence-based recommendation—need to change their approach as well.

Think tanks may be slower to adapt because of long-standing biases around what qualifies someone to be a policy “expert.” Traditionally, think tanks assess qualifications based on educational attainment and advanced degrees, which has often meant prioritizing academic credentials over lived or professional experience on the ground. These hiring preferences alone leave many people out of the debates that shape their lives: if think tanks expect a master’s degree for mid-level and senior research and policy positions, their pool of candidates will be limited to the 4 percent of Latinos and 7 percent of Black people with those degrees (lower than the rates among white people (10.5 percent) or Asian/Pacific Islanders (17 percent)). And in specific fields like Economics, from which many think tanks draw their experts, just 0.5 percent of doctoral degrees go to Black women each year.

Think tanks alone cannot change the larger cultural and societal forces that have historically limited access to certain fields. But they can change their own practices: namely, they can change how they assess expertise and who they recruit and cultivate as policy experts. In doing so, they can push the broader policy sector—including government and philanthropic donors—to do the same. Because while the next generation marches in the streets and runs for office, the public policy sector is not doing enough to diversify and support who develops, researches, enacts, and implements policy. And excluding impacted communities from the decision-making table makes our democracy less inclusive, responsive, and effective.

Two years ago, my colleagues and I at The Century Foundation, a 100-year-old think tank that has weathered many paradigm shifts in policymaking, launched an organization, Next100, to experiment with a new model for think tanks. Our mission was simple: policy by those with the most at stake, for those with the most at stake. We believed that proximity to the communities that policy looks to serve will make policy stronger, and we put muscle and resources behind the theory that those with lived experience are as much policy experts as anyone with a PhD from an Ivy League university. The pandemic and heightened calls for racial justice in the last year have only strengthened our belief in the need to thoughtfully democratize policy development. While it’s common understanding now that COVID-19 has surfaced and exacerbated profound historical inequities, not enough has been done to question why those inequities exist, or why they run so deep. How we make policy—and who makes it—is a big reason why….(More)”

What Robots Can — And Can’t — Do For the Old and Lonely

Curated on Posted on by Stefaan Verhulst

Katie Engelhart at The New Yorker: “…In 2017, the Surgeon General, Vivek Murthy, declared loneliness an “epidemic” among Americans of all ages. This warning was partly inspired by new medical research that has revealed the damage that social isolation and loneliness can inflict on a body. The two conditions are often linked, but they are not the same: isolation is an objective state (not having much contact with the world); loneliness is a subjective one (feeling that the contact you have is not enough). Both are thought to prompt a heightened inflammatory response, which can increase a person’s risk for a vast range of pathologies, including dementia, depression, high blood pressure, and stroke. Older people are more susceptible to loneliness; forty-three per cent of Americans over sixty identify as lonely. Their individual suffering is often described by medical researchers as especially perilous, and their collective suffering is seen as an especially awful societal failing….

So what’s a well-meaning social worker to do? In 2018, New York State’s Office for the Aging launched a pilot project, distributing Joy for All robots to sixty state residents and then tracking them over time. Researchers used a six-point loneliness scale, which asks respondents to agree or disagree with statements like “I experience a general sense of emptiness.” They concluded that seventy per cent of participants felt less lonely after one year. The pets were not as sophisticated as other social robots being designed for the so-called silver market or loneliness economy, but they were cheaper, at about a hundred dollars apiece.

In April, 2020, a few weeks after New York aging departments shut down their adult day programs and communal dining sites, the state placed a bulk order for more than a thousand robot cats and dogs. The pets went quickly, and caseworkers started asking for more: “Can I get five cats?” A few clients with cognitive impairments were disoriented by the machines. One called her local department, distraught, to say that her kitty wasn’t eating. But, more commonly, people liked the pets so much that the batteries ran out. Caseworkers joked that their clients had loved them to death….(More)”.

How a largely untested AI algorithm crept into hundreds of hospitals

Curated on Posted on by Stefaan Verhulst

Vishal Khetpal and Nishant Shah at FastCompany: “Last spring, physicians like us were confused. COVID-19 was just starting its deadly journey around the world, afflicting our patients with severe lung infections, strokes, skin rashes, debilitating fatigue, and numerous other acute and chronic symptoms. Armed with outdated clinical intuitions, we were left disoriented by a disease shrouded in ambiguity.

In the midst of the uncertainty, Epic, a private electronic health record giant and a key purveyor of American health data, accelerated the deployment of a clinical prediction tool called the Deterioration Index. Built with a type of artificial intelligence called machine learning and in use at some hospitals prior to the pandemic, the index is designed to help physicians decide when to move a patient into or out of intensive care, and is influenced by factors like breathing rate and blood potassium level. Epic had been tinkering with the index for years but expanded its use during the pandemic. At hundreds of hospitals, including those in which we both work, a Deterioration Index score is prominently displayed on the chart of every patient admitted to the hospital.

The Deterioration Index is poised to upend a key cultural practice in medicine: triage. Loosely speaking, triage is an act of determining how sick a patient is at any given moment to prioritize treatment and limited resources. In the past, physicians have performed this task by rapidly interpreting a patient’s vital signs, physical exam findings, test results, and other data points, using heuristics learned through years of on-the-job medical training.

Ostensibly, the core assumption of the Deterioration Index is that traditional triage can be augmented, or perhaps replaced entirely, by machine learning and big data. Indeed, a study of 392 COVID-19 patients admitted to Michigan Medicine that the index was moderately successful at discriminating between low-risk patients and those who were at high-risk of being transferred to an ICU, getting placed on a ventilator, or dying while admitted to the hospital. But last year’s hurried rollout of the Deterioration Index also sets a worrisome precedent, and it illustrates the potential for such decision-support tools to propagate biases in medicine and change the ways in which doctors think about their patients….(More)”.

Deepfake Maps Could Really Mess With Your Sense of the World

Curated on Posted on by Stefaan Verhulst

Will Knight at Wired: “Satellite images showing the expansion of large detention camps in Xinjiang, China, between 2016 and 2018 provided some of the strongest evidence of a government crackdown on more than a million Muslims, triggering international condemnation and sanctions.

Other aerial images—of nuclear installations in Iran and missile sites in North Korea, for example—have had a similar impact on world events. Now, image-manipulation tools made possible by artificial intelligence may make it harder to accept such images at face value.

In a paper published online last month, University of Washington professor Bo Zhao employed AI techniques similar to those used to create so-called deepfakes to alter satellite images of several cities. Zhao and colleagues swapped features between images of Seattle and Beijing to show buildings where there are none in Seattle and to remove structures and replace them with greenery in Beijing.

Zhao used an algorithm called CycleGAN to manipulate satellite photos. The algorithm, developed by researchers at UC Berkeley, has been widely used for all sorts of image trickery. It trains an artificial neural network to recognize the key characteristics of certain images, such as a style of painting or the features on a particular type of map. Another algorithm then helps refine the performance of the first by trying to detect when an image has been manipulated….(More)”.

Quantitative Description of Digital Media

Curated on Posted on by Stefaan Verhulst

Introduction by Kevin Munger, Andrew M. Guess and Eszter Hargittai: “We introduce the rationale for a new peer-reviewed scholarly journal, the Journal of Quantitative Description: Digital Media. The journal is intended to create a new venue for research on digital media and address several deficiencies in the current social science publishing landscape. First, descriptive research is undersupplied and undervalued. Second, research questions too often only reflect dominant theories and received wisdom. Third, journals are constrained by unnecessary boundaries defined by discipline, geography, and length. Fourth, peer review is inefficient and unnecessarily burdensome for both referees and authors. We outline the journal’s scope and structure, which is open access, fee-free and relies on a Letter of Inquiry (LOI) model. Quantitative description can appeal to social scientists of all stripes and is a crucial methodology for understanding the continuing evolution of digital media and its relationship to important questions of interest to social scientists….(More)”.

Creating Public Value using the AI-Driven Internet of Things

Curated on Posted on by Stefaan Verhulst

Report by Gwanhoo Lee: “Government agencies seek to deliver quality services in increasingly dynamic and complex environments. However, outdated infrastructures—and a shortage of systems that collect and use massive real-time data—make it challenging for the agencies to fulfill their missions. Governments have a tremendous opportunity to transform public services using the “Internet of Things” (IoT) to provide situationspecific and real-time data, which can improve decision-making and optimize operational effectiveness.

In this report, Professor Lee describes IoT as a network of physical “things” equipped with sensors and devices that enable data transmission and operational control with no or little human intervention. Organizations have recently begun to embrace artificial intelligence (AI) and machine learning (ML) technologies to drive even greater value from IoT applications. AI/ML enhances the data analytics capabilities of IoT by enabling accurate predictions and optimal decisions in new ways. Professor Lee calls this AI/ML-powered IoT the “AI-Driven Internet of Things” (AIoT for short hereafter). AIoT is a natural evolution of IoT as computing, networking, and AI/ML technologies are increasingly converging, enabling organizations to develop as “cognitive enterprises” that capitalize on the synergy across these emerging technologies.

Strategic application of IoT in government is in an early phase. Few U.S. federal agencies have explicitly incorporated IoT in their strategic plan, or connected the potential of AI to their evolving IoT activities. The diversity and scale of public services combined with various needs and demands from citizens provide an opportunity to deliver value from implementing AI-driven IoT applications.

Still, IoT is already making the delivery of some public services smarter and more efficient, including public parking, water management, public facility management, safety alerts for the elderly, traffic control, and air quality monitoring. For example, the City of Chicago has deployed a citywide network of air quality sensors mounted on lampposts. These sensors track the presence of several air pollutants, helping the city develop environmental responses that improve the quality of life at a community level. As the cost of sensors decreases while computing power and machine learning capabilities grow, IoT will become more feasible and pervasive across the public sector—with some estimates of a market approaching $5 trillion in the next few years.

Professor Lee’s research aims to develop a framework of alternative models for creating public value with AIoT, validating the framework with five use cases in the public domain. Specifically, this research identifies three essential building blocks to AIoT: sensing through IoT devices, controlling through the systems that support these devices, and analytics capabilities that leverage AI to understand and act on the information accessed across these applications. By combining the building blocks in different ways, the report identifies four models for creating public value:

  • Model 1 utilizes only sensing capability.
  • Model 2 uses sensing capability and controlling capability.
  • Model 3 leverages sensing capability and analytics capability.
  • Model 4 combines all three capabilities.

The analysis of five AIoT use cases in the public transport sector from Germany, Singapore, the U.K., and the United States identifies 10 critical success factors, such as creating public value, using public-private partnerships, engaging with the global technology ecosystem, implementing incrementally, quantifying the outcome, and using strong cybersecurity measures….(More)”.

The Switch: How the Telegraph, Telephone, and Radio Created the Computer

Curated on Posted on by Stefaan Verhulst

Book by Chris McDonald: “Digital technology has transformed our world almost beyond recognition over the past four decades. We spend our lives surrounded by laptops, phones, tablets, and video game consoles — not to mention the digital processors that are jam-packed into our appliances and automobiles. We use computers to work, to play, to learn, and to socialize. The Switch tells the story of the humble components that made all of this possible — the transistor and its antecedents, the relay, and the vacuum tube.

All three of these devices were originally developed without any thought for their application to computers or computing. Instead, they were created for communication, in order to amplify or control signals sent over a wire or over the air. By repurposing these amplifiers as simple switches, flipped on and off by the presence or absence of an electric signal, later scientists and engineers constructed our digital universe. Yet none of it would have been possible without the telegraph, telephone, and radio. In these pages you’ll find a story of the interplay between science and technology, and the surprising ways in which inventions created for one purpose can be adapted to another. The tale is enlivened by the colorful cast of scientists and innovators, from Luigi Galvani to William Shockley, who, whether through brilliant insight or sheer obstinate determination, contributed to the evolution of the digital switch….(More)”.

Diverse Sources Database

Curated on Posted on by Stefaan Verhulst

About: “The Diverse Sources Database is NPR’s resource for journalists who believe in the value of diversity and share our goal to make public radio look and sound like America.

Originally called Source of the Week, the database launched in 2013 as a way help journalists at NPR and member stations expand the racial/ethnic diversity of the experts they tap for stories…(More)”.

‘Belonging Is Stronger Than Facts’: The Age of Misinformation

Curated on Posted on by Stefaan Verhulst


Max Fisher at the New York Times: “There’s a decent chance you’ve had at least one of these rumors, all false, relayed to you as fact recently: that President Biden plans to force Americans to eat less meat; that Virginia is eliminating advanced math in schools to advance racial equality; and that border officials are mass-purchasing copies of Vice President Kamala Harris’s book to hand out to refugee children.

All were amplified by partisan actors. But you’re just as likely, if not more so, to have heard it relayed from someone you know. And you may have noticed that these cycles of falsehood-fueled outrage keep recurring.

We are in an era of endemic misinformation — and outright disinformation. Plenty of bad actors are helping the trend along. But the real drivers, some experts believe, are social and psychological forces that make people prone to sharing and believing misinformation in the first place. And those forces are on the rise.

“Why are misperceptions about contentious issues in politics and science seemingly so persistent and difficult to correct?” Brendan Nyhan, a Dartmouth College political scientist, posed in a new paper in Proceedings of the National Academy of Sciences.

It’s not for want of good information, which is ubiquitous. Exposure to good information does not reliably instill accurate beliefs anyway. Rather, Dr. Nyhan writes, a growing body of evidence suggests that the ultimate culprits are “cognitive and memory limitations, directional motivations to defend or support some group identity or existing belief, and messages from other people and political elites.”

Put more simply, people become more prone to misinformation when three things happen. First, and perhaps most important, is when conditions in society make people feel a greater need for what social scientists call ingrouping — a belief that their social identity is a source of strength and superiority, and that other groups can be blamed for their problems….(More)”.