Promoting Sustainable Data Use in State Programs

Curated on Posted on by Stefaan Verhulst

Toolkit by Chapin Hall:”…helps public sector agencies build the culture and infrastructure to apply data analysis routinely, effectively, and accurately—what we call “sustainable data use.”  It is meant to serve as a hands-on resource, containing strategies and tools for agencies seeking to grow their analytic capacity. 

Administrative data can be a rich source of information for human services agencies seeking to improve programs. But too often, data use in state agencies is temporary, dependent on funds and training from short-term resources such as pilot projects and grants. How can agencies instead move from data to knowledge to action routinely, creating a reinforcing cycle of evidence-building and program improvement?

Chapin Hall experts and experts at partner organizations set out to determine who achieves sustainable data use and how they go about doing so. Building on previous work and the results of a literature review, we identified domains that can significantly influence an agency’s ability to establish sustainable data practices. We then focused on eight state TANF agencies and three partner organizations with demonstrated successes in one or more of these domains, and we interviewed staff who work directly with data to learn more about what strategies they used to achieve success. We focused on what worked rather than what didn’t. From those interviews, we identified common themes, developed case studies, and generated tools to help agencies develop sustainable data practices…(More)”.

Unleashing possibilities, ignoring risks: Why we need tools to manage AI’s impact on jobs

Curated on Posted on by Stefaan Verhulst

Article by Katya Klinova and Anton Korinek: “…Predicting the effects of a new technology on labor demand is difficult and involves significant uncertainty. Some would argue that, given the uncertainty, we should let the “invisible hand” of the market decide our technological destiny. But we believe that the difficulty of answering the question “Who is going to benefit and who is going to lose out?” should not serve as an excuse for never posing the question in the first place. As we emphasized, the incentives for cutting labor costs are artificially inflated. Moreover, the invisible hand theorem does not hold for technological change. Therefore, a failure to investigate the distribution of benefits and costs of AI risks invites a future with too many “so-so” uses of AI—uses that concentrate gains while distributing the costs. Although predictions about the downstream impacts of AI systems will always involve some uncertainty, they are nonetheless useful to spot applications of AI that pose the greatest risks to labor early on and to channel the potential of AI where society needs it the most.

In today’s society, the labor market serves as a primary mechanism for distributing income as well as for providing people with a sense of meaning, community, and purpose. It has been documented that job loss can lead to regional decline, a rise in “deaths of despair,” addiction and mental health problems. The path that we lay out aims to prevent abrupt job losses or declines in job quality on the national and global scale, providing an additional tool for managing the pace and shape of AI-driven labor market transformation.

Nonetheless, we do not want to rule out the possibility that humanity may eventually be much happier in a world where machines do a lot more economically valuable work. Even despite our best efforts to manage the pace and shape of AI labor market disruption through regulation and worker-centric practices, we may still face a future with significantly reduced human labor demand. Should the demand for human labor decrease permanently with the advancement of AI, timely policy responses will be needed to address both the lost incomes as well as the lost sense of meaning and purpose. In the absence of significant efforts to distribute the gains from advanced AI more broadly, the possible devaluation of human labor would deeply impact income distribution and democratic institutions’ sustainability. While a jobless future is not guaranteed, its mere possibility and the resulting potential societal repercussions demand serious consideration. One promising proposal to consider is to create an insurance policy against a dramatic decrease in the demand for human labor that automatically kicks in if the share of income received by workers declines, for example a “seed” Universal Basic Income that starts at a very small level and remains unchanged if workers continue to prosper but automatically rises if there is large scale worker displacement…(More)”.

Reimagining Our High-Tech World

Curated on Posted on by Stefaan Verhulst

Essay by Mike Kubzansky: “…Channeling the power of technology for the good of society requires a shared vision of an ideal society. Despite the country’s increasing polarization, most Americans agree on the principles of a representative democracy and embrace the three quintessential rights inscribed in the Declaration of Independence—life, liberty, and the pursuit of happiness. Freedom and individual liberty, including freedom of speech, religion, and assembly and the right to privacy, are fundamental to most people’s expectations for this country, as are equality for all citizens, a just legal system, and a strong economy. Widespread consensus also exists around giving children a strong start in life; ensuring access to basic necessities like health care, food, and housing; and taking care of the planet.

By deliberately building a digital tech system guided by these values, society has an opportunity to advance its interests and future-proof the digital tech system for better outcomes.

Such collective action requires a broad conversation about what kind of society Americans want and how digital technology fits into that vision. To initiate this discussion, I suggest five questions philanthropists, technologists, entrepreneurs, policy makers, academics, advocates, movement leaders, students, consumers, investors, and everyone else who has a stake in the nation’s future need to start asking—now….(More)”.

It’s like jury duty, but for getting things done

Curated on Posted on by Stefaan Verhulst

Article by Hollie Russon Gilman and Amy Eisenstein: “Citizens’ assemblies have the potential to repair our broken politics…Imagine a democracy where people come together and their voices are heard and are translated directly into policy. Frontline workers, doctors, teachers, friends, and neighbors — young and old — are brought together in a random, representative sample to deliberate the most pressing issues facing our society. And they are compensated for their time.

The concept may sound radical. But we already use this method for jury duty. Why not try this widely accepted practice to tackle the deepest, most crucial, and most divisive issues facing our democracy?

The idea — known today as citizens’ assemblies — originated in ancient Athens. Instead of a top-down government, Athens used sortition — a system that was horizontal and distributive. The kleroterion, an allotment machine, randomly selected citizens to hold civic office, ensuring that the people had a direct say in their government’s dealings….(More)”.

The Worst People Run for Office. It’s Time for a Better Way.

Curated on Posted on by Stefaan Verhulst

Article by Adam Grant: “On the eve of the first debate of the 2024 presidential race, trust in government is rivaling historic lows. Officials have been working hard to safeguard elections and assure citizens of their integrity. But if we want public office to have integrity, we might be better off eliminating elections altogether.

If you think that sounds anti-democratic, think again. The ancient Greeks invented democracy, and in Athens many government officials were selected through sortition — a random lottery from a pool of candidates. In the United States, we already use a version of a lottery to select jurors. What if we did the same with mayors, governors, legislators, justices and even presidents?

People expect leaders chosen at random to be less effective than those picked systematically. But in multiple experiments led by the psychologist Alexander Haslam, the opposite held true. Groups actually made smarter decisions when leaders were chosen at random than when they were elected by a group or chosen based on leadership skill.

Why were randomly chosen leaders more effective? They led more democratically. “Systematically selected leaders can undermine group goals,” Dr. Haslam and his colleagues suggest, because they have a tendency to “assert their personal superiority.” When you’re anointed by the group, it can quickly go to your head: I’m the chosen one.

When you know you’re picked at random, you don’t experience enough power to be corrupted by it. Instead, you feel a heightened sense of responsibility: I did nothing to earn this, so I need to make sure I represent the group well. And in one of the Haslam experiments, when a leader was picked at random, members were more likely to stand by the group’s decisions.

Over the past year I’ve floated the idea of sortition with a number of current members of Congress. Their immediate concern is ability: How do we make sure that citizens chosen randomly are capable of governing?

In ancient Athens, people had a choice about whether to participate in the lottery. They also had to pass an examination of their capacity to exercise public rights and duties. In America, imagine that anyone who wants to enter the pool has to pass a civics test — the same standard as immigrants applying for citizenship. We might wind up with leaders who understand the Constitution…(More)”.

Health Data Sharing to Support Better Outcomes: Building a Foundation of Stakeholder Trust

Curated on Posted on by Stefaan Verhulst

A Special Publication from the National Academy of Medicine: “The effective use of data is foundational to the concept of a learning health system—one that leverages and shares data to learn from every patient experience, and feeds the results back to clinicians, patients and families, and health care executives to transform health, health care, and health equity. More than ever, the American health care system is in a position to harness new technologies and new data sources to improve individual and population health.

Learning health systems are driven by multiple stakeholders—patients, clinicians and clinical teams, health care organizations, academic institutions, government, industry, and payers. Each stakeholder group has its own sources of data, its own priorities, and its own goals and needs with respect to sharing that data. However, in America’s current health system, these stakeholders operate in silos without a clear understanding of the motivations and priorities of other groups. The three stakeholder working groups that served as the authors of this Special Publication identified many cultural, ethical, regulatory, and financial barriers to greater data sharing, linkage, and use. What emerged was the foundational role of trust in achieving the full vision of a learning health system.

This Special Publication outlines a number of potentially valuable policy changes and actions that will help drive toward effective, efficient, and ethical data sharing, including more compelling and widespread communication efforts to improve awareness, understanding, and participation in data sharing. Achieving the vision of a learning health system will require eliminating the artificial boundaries that exist today among patient care, health system improvement, and research. Breaking down these barriers will require an unrelenting commitment across multiple stakeholders toward a shared goal of better, more equitable health.

We can improve together by sharing and using data in ways that produce trust and respect. Patients and families deserve nothing less…(More)”.

Tyranny of the Minority

Curated on Posted on by Stefaan Verhulst

Book by Steven Levitsky and Daniel Ziblatt: “America is undergoing a massive experiment: It is moving, in fits and starts, toward a multiracial democracy, something few societies have ever done. But the prospect of change has sparked an authoritarian backlash that threatens the very foundations of our political system. Why is democracy under assault here, and not in other wealthy, diversifying nations? And what can we do to save it?

With the clarity and brilliance that made their first book, How Democracies Die, a global bestseller, Harvard professors Steven Levitsky and Daniel Ziblatt offer a coherent framework for understanding these volatile times. They draw on a wealth of examples—from 1930s France to present-day Thailand—to explain why and how political parties turn against democracy. They then show how our Constitution makes us uniquely vulnerable to attacks from within: It is a pernicious enabler of minority rule, allowing partisan minorities to consistently thwart and even rule over popular majorities. Most modern democracies—from Germany and Sweden to Argentina and New Zealand—have eliminated outdated institutions like elite upper chambers, indirect elections, and lifetime tenure for judges. The United States lags dangerously behind.

In this revelatory book, Levitsky and Ziblatt issue an urgent call to reform our politics. It’s a daunting task, but we have remade our country before—most notably, after the Civil War and during the Progressive Era. And now we are at a crossroads: America will either become a multiracial democracy or cease to be a democracy at all…(More)”.

Public Sector Use of Private Sector Personal Data: Towards Best Practices

Curated on Posted on by Stefaan Verhulst

Paper by Teresa Scassa: “Governments increasingly turn to the private sector as a source of data for various purposes. In some cases, the data that they seek to use is personal data. The public sector use of private sector personal data raises several important law and public policy concerns. These include the legal authority for such uses; privacy and data protection; ethics; transparency; and human rights. Governments that use private sector personal data without attending to the issues that such use raises may breach existing laws, which in some cases may not be well-adapted to evolving data practices. They also risk undermining public trust.

This paper uses two quite different recent examples from Canada where the use of private sector personal data by public sector actors caused considerable backlash and led to public hearings and complaints to the Privacy Commissioner. The examples are used to tease out the complex and interwoven law and policy issues. In some cases, the examples reveal issues that are particular to the evolving data society and that are not well addressed by current law or practice. The paper identifies key issues and important gaps and makes recommendations to address these. Although the examples discussed are Canadian and depend to some extent on Canadian law and institutions, the practices at issue are ones that are increasingly used around the world, and many of the issues raised are broadly relevant…(More)”.

Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril

Curated on Posted on by Stefaan Verhulst

Special Publication by the National Academy of Medicine (NAM): “The emergence of artificial intelligence (AI) in health care offers unprecedented opportunities to improve patient and clinical team outcomes, reduce costs, and impact population health. While there have been a number of promising examples of AI applications in health care, it is imperative to proceed with caution or risk the potential of user disillusionment, another AI winter, or further exacerbation of existing health- and technology-driven disparities.

This Special Publication synthesizes current knowledge to offer a reference document for relevant health care stakeholders. It outlines the current and near-term AI solutions; highlights the challenges, limitations, and best practices for AI development, adoption, and maintenance; offers an overview of the legal and regulatory landscape for AI tools designed for health care application; prioritizes the need for equity, inclusion, and a human rights lens for this work; and outlines key considerations for moving forward.

AI is poised to make transformative and disruptive advances in health care, but it is prudent to balance the need for thoughtful, inclusive health care AI that plans for and actively manages and reduces potential unintended consequences, while not yielding to marketing hype and profit motives…(More)”

Changing Facebook’s algorithm won’t fix polarization, new study finds

Curated on Posted on by Stefaan Verhulst

Article by Naomi Nix, Carolyn Y. Johnson, and Cat Zakrzewski: “For years, regulators and activists have worried that social media companies’ algorithms were dividing the United States with politically toxic posts and conspiracies. The concern was so widespread that in 2020, Meta flung open troves of internal data for university academics to study how Facebook and Instagram would affect the upcoming presidential election.

The first results of that research show that the company’s platforms play a critical role in funneling users to partisan information with which they are likely to agree. But the results cast doubt on assumptions that the strategies Meta could use to discourage virality and engagement on its social networks would substantially affect people’s political beliefs.

“Algorithms are extremely influential in terms of what people see on the platform, and in terms of shaping their on-platform experience,” Joshua Tucker, co-director of the Center for Social Media and Politics at New York University and one of the leaders on the research project, said in an interview.

“Despite the fact that we find this big impact in people’s on-platform experience, we find very little impact in changes to people’s attitudes about politics and even people’s self-reported participation around politics.”

The first four studies, which were released on Thursday in the journals Science and Nature, are the result of a unique partnership between university researchers and Meta’s own analysts to study how social media affects political polarization and people’s understanding and opinions about news, government and democracy. The researchers, who relied on Meta for data and the ability to run experiments, analyzed those issues during the run-up to the 2020 election. The studies were peer-reviewed before publication, a standard procedure in science in which papers are sent out to other experts in the field who assess the work’s merit.

As part of the project, researchers altered the feeds of thousands of people using Facebook and Instagram in fall of 2020 to see if that could change political beliefs, knowledge or polarization by exposing them to different information than they might normally have received. The researchers generally concluded that such changes had little impact.

The collaboration, which is expected to be released over a dozen studies, also will examine data collected after the Jan. 6, 2021, attack on the U.S. Capitol, Tucker said…(More)”.