How Americans View Data Privacy

Curated on Posted on by Stefaan Verhulst

Pew Research: “…Americans – particularly Republicans – have grown more concerned about how the government uses their data. The share who say they are worried about government use of people’s data has increased from 64% in 2019 to 71% today. That reflects rising concern among Republicans (from 63% to 77%), while Democrats’ concern has held steady. (Each group includes those who lean toward the respective party.)

The public increasingly says they don’t understand what companies are doing with their data. Some 67% say they understand little to nothing about what companies are doing with their personal data, up from 59%.

Most believe they have little to no control over what companies or the government do with their data. While these shares have ticked down compared with 2019, vast majorities feel this way about data collected by companies (73%) and the government (79%).

We’ve studied Americans’ views on data privacy for years. The topic remains in the national spotlight today, and it’s particularly relevant given the policy debates ranging from regulating AI to protecting kids on social media. But these are far from abstract concepts. They play out in the day-to-day lives of Americans in the passwords they choose, the privacy policies they agree to and the tactics they take – or not – to secure their personal information. We surveyed 5,101 U.S. adults using Pew Research Center’s American Trends Panel to give voice to people’s views and experiences on these topics.

In addition to the key findings covered on this page, the three chapters of this report provide more detail on:

How to share data — not just equally, but equitably

Curated on Posted on by Stefaan Verhulst

Editorial in Nature: “Two decades ago, scientists asked more than 150,000 people living in Mexico City to provide medical data for research. Each participant gave time, blood and details of their medical history. For the researchers, who were based at the National Autonomous University of Mexico in Mexico City and the University of Oxford, UK, this was an opportunity to study a Latin American population for clues about factors contributing to disease and health. For the participants, it was a chance to contribute to science so that future generations might one day benefit from access to improved health care. Ultimately, the Mexico City Prospective Study was an exercise in trust — scientists were trusted with some of people’s most private information because they promised to use it responsibly.

Over the years, the researchers have repaid the communities through studies investigating the effects of tobacco and other risk factors on participants’ health. They have used the data to learn about the impact of diabetes on mortality rates, and they have found that rare forms of a gene called GPR75 lower the risk of obesity. And on 11 October, researchers added to the body of knowledge on the population’s ancestry.

But this project also has broader relevance — it can be seen as a model of trust and of how the power structures of science can be changed to benefit the communities closest to it.

Mexico’s population is genetically wealthy. With a complex history of migration and mixing of several populations, the country’s diverse genetic resources are valuable to the study of the genetic roots of diseases. Most genetic databases are stocked with data from people with European ancestry. If genomics is to genuinely benefit the global community — and especially under-represented groups — appropriately diverse data sets are needed. These will improve the accuracy of genetic tests, such as those for disease risk, and will make it easier to unearth potential drug targets by finding new genetic links to medical conditions…(More)”.

Generative AI is set to transform crisis management

Curated on Posted on by Stefaan Verhulst

Article by Ben Ellencweig, Mihir Mysore, Jon Spaner: “…Generative AI presents transformative potential, especially in disaster preparedness and response, and recovery. As billion-dollar disasters become more frequent – “billion-dollar disasters” typically costing the U.S. roughly $120 billion each – and “polycrises”, or multiple crises at once proliferate (e.g. hurricanes combined with cyber disruptions), the significant impact that Generative AI can have, especially with proper leadership focus, is a focal point of interest.

Generative AI’s speed is crucial in emergencies, as it enhances information access, decision-making capabilities, and early warning systems. Beyond organizational benefits for those who adopt Generative AI, its applications include real-time data analysis, scenario simulations, sentiment analysis, and simplifying complex information access. Generative AI’s versatility offers a wide variety of promising applications in disaster relief, and opens up facing real time analyses with tangible applications in the real world. 

Early warning systems and sentiment analysis: Generative AI excels in early warning systems and sentiment analysis, by scanning accurate real-time data and response clusters. By enabling connections between disparate systems, Generative AI holds the potential to provide more accurate early warnings. Integrated with traditional and social media, Generative AI can also offer precise sentiment analysis, empowering leaders to understand public sentiment, detect bad actors, identify misinformation, and tailor communications for accurate information dissemination.

Scenario simulations: Generative AI holds the potential to enhance catastrophe modeling for better crisis assessment and resource allocation. It creates simulations for emergency planners, improving modeling for various disasters (e.g., hurricanes, floods, wildfires) using historical data such as location, community impact, and financial consequence. Often, simulators perform work “so large that it exceeds human capacity (for example, finding flooded or unusable roads across a large area after a hurricane).” …(More)”

Evidence-Based Government Is Alive and Well

Curated on Posted on by Stefaan Verhulst

Article by Zina Hutton: “A desire to discipline the whimsical rule of despots.” That’s what Gary Banks, a former chairman of Australia’s Productivity Commission, attributed the birth of evidence-based policy to back in the 14th century in a speech from 2009. Evidence-based policymaking isn’t a new style of government, but it’s one with well-known roadblocks that elected officials have been working around in order to implement it more widely.

Evidence-based policymaking relies on evidence — facts, data, expert analysis — to shape aspects of long- and short-term policy decisions. It’s not just about collecting data, but also applying it and experts’ analysis to shape future policy. Whether it’s using school enrollment numbers to justify building a new park in a neighborhood or scientists collaborating on analysis of wastewater to try to “catch” illness spread in a community before it becomes unmanageable, evidence-based policy uses facts to help elected and appointed officials decide what funds and other resources to allocate in their communities.

Problems with evidence-based governing have been around for years. They range from a lack of communication between the people designing the policy and its related programs and the people implementing them, to the way that local government struggles to recruit and maintain employees. Resource allocation also shapes the decisions some cities make when it comes to seeking out and using data. This can be seen in the way larger cities, with access to proportionately larger budgets, research from state universities within city limits and a larger workforce, have had more success with evidence-based policymaking.
“The largest cities have more personnel, more expertise, more capacity, whether that’s for collecting administrative data and monitoring it, whether that’s doing open data portals, or dashboards, or whether that’s doing things like policy analysis or program evaluation,” says Karen Mossberger, the Frank and June Sackton Professor in the School of Public Affairs at Arizona State University. “It takes expert personnel, it takes people within government with the skills and the capacity, it takes time.”

Roadblocks aside, state and local governments are finding innovative ways to collaborate with one another on data-focused projects and policy, seeking ways to make up for the problems that impacted early efforts at evidence-based governance. More state and local governments now recruit data experts at every level to collect, analyze and explain the data generated by residents, aided by advances in technology and increased access to researchers…(More)”.

Who owns data about you?

Curated on Posted on by Stefaan Verhulst

Article by Wendy Wong: “The ascendancy of artificial intelligence hinges on vast data accrued from our daily activities. In turn, data train advanced algorithms, fuelled by massive amounts of computing power. Together, they form the critical trio driving AI’s capabilities. Because of its human sources, data raise an important question: who owns data, and how do the data add up when they’re about our mundane, routine choices?

It often helps to think through modern problems with historical anecdotes. The case of Henrietta Lacks, a Black woman living in Baltimore stricken with cervical cancer, and her everlasting cells, has become well-known because of Rebecca Skloot’s book, The Immortal Life of Henrietta Lacks,and a movie starring Oprah Winfrey. Unbeknownst to her, Lacks’s medical team removed her cancer cells and sent them to a lab to see if they would grow. While Lacks died of cancer in 1951, her cells didn’t. They kept going, in petri dishes in labs, all the way through to the present day.

The unprecedented persistence of Lacks’s cells led to the creation of the HeLa cell line. Her cells underpin various medical technologies, from in-vitro fertilization to polio and COVID-19 vaccines, generating immense wealth for pharmaceutical companies. HeLa is a co-creation. Without Lacks or scientific motivation, there would be no HeLa.

The case raises questions about consent and ownership. That her descendants recently settled a lawsuit against Thermo Fisher Scientific, a pharmaceutical company that monetized products made from HeLa cells, echoes the continuing discourse surrounding data ownership and rights. Until the settlement, just one co-creator was reaping all the financial benefits of that creation.

The Lacks family’s legal battle centred on a human-rights claim. Their situation was rooted in the impact of Lacks’s cells on medical science and the intertwined racial inequalities that lead to disparate medical outcomes. Since Lacks’s death, the family had struggled while biotech companies profited.

These “tissue issues” often don’t favour the individuals providing the cells or body parts. The U.S. Supreme Court case Moore v. Regents of the University of California deemed body parts as “garbage” once separated from the individual. The ruling highlights a harsh legal reality: Individuals don’t necessarily retain rights of parts of their body, financial or otherwise. Another federal case, Washington University v. Catalona, invalidated ownership claims based upon the “feeling” it belongs to the person it came from.

We can liken this characterization of body parts to how we often think about data taken from people. When we call data “detritus” or “exhaust,” we dehumanize the thoughts, behaviours and choices that generate those data. Do we really want to say that data, once created, is a resource for others’ exploitation?…(More)”.

When is a Decision Automated? A Taxonomy for a Fundamental Rights Analysis

Curated on Posted on by Stefaan Verhulst

Paper by Francesca Palmiotto: “This paper addresses the pressing issues surrounding the use of automated systems in public decision-making, with a specific focus on the field of migration, asylum, and mobility. Drawing on empirical research conducted for the AFAR project, the paper examines the potential and limitations of the General Data Protection Regulation and the proposed Artificial Intelligence Act in effectively addressing the challenges posed by automated decision making (ADM). The paper argues that the current legal definitions and categorizations of ADM fail to capture the complexity and diversity of real-life applications, where automated systems assist human decision-makers rather than replace them entirely. This discrepancy between the legal framework and practical implementation highlights the need for a fundamental rights approach to legal protection in the automation age. To bridge the gap between ADM in law and practice, the paper proposes a taxonomy that provides theoretical clarity and enables a comprehensive understanding of ADM in public decision-making. This taxonomy not only enhances our understanding of ADM but also identifies the fundamental rights at stake for individuals and the sector-specific legislation applicable to ADM. The paper finally calls for empirical observations and input from experts in other areas of public law to enrich and refine the proposed taxonomy, thus ensuring clearer conceptual frameworks to safeguard individuals in our increasingly algorithmic society…(More)”.

NYC Releases Plan to Embrace AI, and Regulate It

Curated on Posted on by Stefaan Verhulst

Article by Sarah Holder: “New York City Mayor Eric Adams unveiled a plan for adopting and regulating artificial intelligence on Monday, highlighting the technology’s potential to “improve services and processes across our government” while acknowledging the risks.

The city also announced it is piloting an AI chatbot to answer questions about opening or operating a business through its website MyCity Business.

NYC agencies have reported using more than 30 tools that fit the city’s definition of algorithmic technology, including to match students with public schools, to track foodborne illness outbreaks and to analyze crime patterns. As the technology gets more advanced, and the implications of algorithmic bias, misinformation and privacy concerns become more apparent, the city plans to set policy around new and existing applications…

New York’s strategy, developed by the Office of Technology and Innovation with the input of city agency representatives and outside technology policy experts, doesn’t itself establish any rules and regulations around AI, but lays out a timeline and blueprint for creating them. It emphasizes the need for education and buy-in both from New York constituents and city employees. Within the next year, the city plans to start to hold listening sessions with the public, and brief city agencies on how and why to use AI in their daily operations. The city has also given itself a year to start work on piloting new AI tools, and two to create standards for AI contracts….

Stefaan Verhulst, a research professor at New York University and the co-founder of The GovLab, says that especially during a budget crunch, leaning on AI offers cities opportunities to make evidence-based decisions quickly and with fewer resources. Among the potential use cases he cited are identifying areas most in need of affordable housing, and responding to public health emergencies with data…(More) (Full plan)”.

How a billionaire-backed network of AI advisers took over Washington

Curated on Posted on by Stefaan Verhulst

Article by Brendan Bordelon: “An organization backed by Silicon Valley billionaires and tied to leading artificial intelligence firms is funding the salaries of more than a dozen AI fellows in key congressional offices, across federal agencies and at influential think tanks.

The fellows funded by Open Philanthropy, which is financed primarily by billionaire Facebook co-founder and Asana CEO Dustin Moskovitz and his wife Cari Tuna, are already involved in negotiations that will shape Capitol Hill’s accelerating plans to regulate AI. And they’re closely tied to a powerful influence network that’s pushing Washington to focus on the technology’s long-term risks — a focus critics fear will divert Congress from more immediate rules that would tie the hands of tech firms.

Acting through the little-known Horizon Institute for Public Service, a nonprofit that Open Philanthropy effectively created in 2022, the group is funding the salaries of tech fellows in key Senate offices, according to documents and interviews…Current and former Horizon AI fellows with salaries funded by Open Philanthropy are now working at the Department of Defense, the Department of Homeland Security and the State Department, as well as in the House Science Committee and Senate Commerce Committee, two crucial bodies in the development of AI rules. They also populate key think tanks shaping AI policy, including the RAND Corporation and Georgetown University’s Center for Security and Emerging Technology, according to the Horizon web site…

In the high-stakes Washington debate over AI rules, Open Philanthropy has long been focused on one slice of the problem — the long-term threats that future AI systems might pose to human survival. Many AI thinkers see those as science-fiction concerns far removed from the current AI harms that Washington should address. And they worry that Open Philanthropy, in concert with its web of affiliated organizations and experts, is shifting the policy conversation away from more pressing issues — including topics some leading AI firms might prefer to keep off the policy agenda…(More)”.

Gender Reboot: Reprogramming Gender Rights in the Age of AI

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Book by Eleonore Fournier-Tombs: “This book explores gender norms and women’s rights in the age of AI. The author examines how gender dynamics have evolved in the spheres of work, self-image and safety, and education, and how these might be reflected in current challenges in AI development. The book also explores opportunities in AI to address issues facing women, and how we might harness current technological developments for gender equality. Taking a narrative tone, the book is interwoven with stories and a reflection on the raising young children during the COVID-19 pandemic. It includes both expert and personal interviews to create a nuanced and multidimensional perspective on the state of women’s rights and what might be done to move forward…(More)”.

The Good and Bad of Anticipating Migration

Curated on Posted on by Stefaan Verhulst

Article by Sara Marcucci, Stefaan Verhulst, María Esther Cervantes, Elena Wüllhorst: “This blog is the first in a series that will be published weekly, dedicated to exploring innovative anticipatory methods for migration policy. Over the coming weeks, we will delve into various aspects of these methods, delving into their value, challenges, taxonomy, and practical applications. 

This first blog serves as an exploration of the value proposition and challenges inherent in innovative anticipatory methods for migration policy. We delve into the various reasons why these methods hold promise for informing more resilient, and proactive migration policies. These reasons include evidence-based policy development, enabling policymakers to ground their decisions in empirical evidence and future projections. Decision-takers, users, and practitioners can benefit from anticipatory methods for policy evaluation and adaptation, resource allocation, the identification of root causes, and the facilitation of humanitarian aid through early warning systems. However, it’s vital to acknowledge the challenges associated with the adoption and implementation of these methods, ranging from conceptual concerns such as fossilization, unfalsifiability, and the legitimacy of preemptive intervention, to practical issues like interdisciplinary collaboration, data availability and quality, capacity building, and stakeholder engagement. As we navigate through these complexities, we aim to shed light on the potential and limitations of anticipatory methods in the context of migration policy, setting the stage for deeper explorations in the coming blogs of this series…(More)”.