Report by Danielle Goldfarb: “From collecting millions of online price data to measure inflation, to assessing the economic impact of the COVID-19 pandemic on low-income workers, digital data sets can be used to benefit the public interest. Using these and other examples, this special report explores how digital data sets and advances in artificial intelligence (AI) can provide timely, transparent and detailed insights into global challenges. These experiments illustrate how governments and civil society analysts can reuse digital data to spot emerging problems, analyze specific group impacts, complement traditional metrics or verify data that may be manipulated. AI and data governance should extend beyond addressing harms. International institutions and governments need to actively steward digital data and AI tools to support a step change in our understanding of society’s biggest challenges…(More)”
Cities, health, and the big data revolution
Blog by Harvard Public Health: “Cities influence our health in unexpected ways. From sidewalks to crosswalks, the built environment affects how much we move, impacting our risk for diseases like obesity and diabetes. A recent New York City study underscores that focusing solely on infrastructure, without understanding how people use it, can lead to ineffective interventions. Researchers analyzed over two million Google Street View images, combining them with health and demographic data to reveal these dynamics. Harvard Public Health spoke with Rumi Chunara, director of New York University’s Center for Health Data Science and lead author of the study.
Why study this topic?
We’re seeing an explosion of new data sources, like street-view imagery, being used to make decisions. But there’s often a disconnect—people using these tools don’t always have the public health knowledge to interpret the data correctly. We wanted to highlight the importance of combining data science and domain expertise to ensure interventions are accurate and impactful.
What did you find?
We discovered that the relationship between built environment features and health outcomes isn’t straightforward. It’s not just about having sidewalks; it’s about how often people are using them. Improving physical activity levels in a community could have a far greater impact on health outcomes than simply adding more infrastructure.
It also revealed the importance of understanding the local context. For instance, Google Street View data sometimes misclassifies sidewalks, particularly near highways or bridges, leading to inaccurate conclusions. Relying solely on this data, without accounting for these nuances, could result in less effective interventions…(More)”.
Will big data lift the veil of ignorance?
Blog by Lisa Herzog: “Imagine that you have a toothache, and a visit at the dentist reveals that a major operation is needed. You phone your health insurance. You listen to the voice of the chatbot, press the buttons to go through the menu. And then you hear: “We have evaluated your profile based on the data you have agreed to share with us. Your dental health behavior scores 6 out of 10. The suggested treatment plan therefore requires a co-payment of [insert some large sum of money here].”
This may sound like science fiction. But many other insurances, e.g. car insurances, already build on automated data being shared with them. If they were allowed, health insurers would certainly like to access our data as well – not only those from smart toothbrushes, but also credit card data, behavioral data (e.g. from step counting apps), or genetic data. If they were allowed to use them, they could move towards segmented insurance plans for specific target groups. As two commentators, on whose research I come back below, recently wrote about health insurance: “Today, public plans and nondiscrimination clauses, not lack of information, are what stands between integration and segmentation.”
If, like me, you’re interested in the relation between knowledge and institutional design, insurance is a fascinating topic. The basic idea of insurance is centuries old – here is a brief summary (skip a few paragraphs if you know this stuff). Because we cannot know what might happen to us in the future, but we can know that on an aggregate level, things will happen to people, it can make sense to enter an insurance contract, creating a pool that a group jointly contributes to. Those for whom the risks in question materialize get support from the pool. Those for whom it does not materialize may go through life without receiving any money, but they still know that they could get support if something happened to them. As such, insurance combines solidarity within a group with individual pre-caution…(More)”.
Flipping data on its head: Differing conceptualisations of data and the implications for actioning Indigenous data sovereignty principles
Paper by Stephanie Cunningham-Reimann et al: “Indigenous data sovereignty is of global concern. The power of data through its multitude of uses can cause harm to Indigenous Peoples, communities, organisations and Nations in Canada and globally. Indigenous research principles play a vital role in guiding researchers, scholars and policy makers in their careers and roles. We define data, data sovereignty principles, ways of practicing Indigenous research principles, and recommendations for applying and actioning Indigenous data sovereignty through culturally safe self-reflection, interpersonal and reciprocal relationships built upon respect, reciprocity, relevance, responsibility and accountability. Research should be co-developed, co-led, and co-disseminated in partnership with Indigenous Peoples, communities, organisations and/or nations to build capacity, support self-determination, and reduce harms produced through the analysis and dissemination of research findings. OCAP® (Ownership, Control, Access & Possession), OCAS (Ownership, Control, Access & Stewardship), Inuit Qaujimajatuqangit principles in conjunction the 4Rs (respect, relevance, reciprocity & responsibility) and cultural competency including self-examination of the 3Ps (power, privilege, and positionality) of researchers, scholars and policy makers can be challenging, but will amplify the voices and understandings of Indigenous research by implementing Indigenous data sovereignty in Canada…(More)”
Thousands of U.S. Government Web Pages Have Been Taken Down Since Friday
Article by Ethan Singer: “More than 8,000 web pages across more than a dozen U.S. government websites have been taken down since Friday afternoon, a New York Times analysis has found, as federal agencies rush to heed President Trump’s orders targeting diversity initiatives and “gender ideology.”
The purges have removed information about vaccines, veterans’ care, hate crimes and scientific research, among many other topics. Doctors, researchers and other professionals often rely on such government data and advisories. Some government agencies appear to have removed entire sections of their websites, while others are missing only a handful of pages.
Among the pages that have been taken down:
(The links are to archived versions.)
- More than 3,000 pages from the Centers for Disease Control and Prevention, including a thousand research articles filed under preventing chronic disease, S.T.D. treatment guidelines, information about Alzheimer’s warning signs, overdose prevention training and vaccine guidelines for pregnant people (the use of the phrase “pregnant people” could have contributed to its removal).
- More than 3,000 pages from the Census Bureau, the vast majority of which are articles filed under research and methodology. Other missing pages include data stewardship policies and documentation for several data sets and surveys.
- More than 1,000 pages from the Office of Justice Programs, including a feature on teenage dating violence and a blog post about grants that have gone toward combating hate crimes.
- More than 200 pages from Head Start, a program for low-income children, including advice on helping families establish routines and videos about preventing postpartum depression.
- …(More)”
Establish data collaboratives to foster meaningful public involvement
Article by Gwen Ottinger: “…Data Collaboratives would move public participation and community engagement upstream in the policy process by creating opportunities for community members to contribute their lived experience to the assessment of data and the framing of policy problems. This would in turn foster two-way communication and trusting relationships between government and the public. Data Collaboratives would also help ensure that data and their uses in federal government are equitable, by inviting a broader range of perspectives on how data analysis can promote equity and where relevant data are missing. Finally, Data Collaboratives would be one vehicle for enabling individuals to participate in science, technology, engineering, math, and medicine activities throughout their lives, increasing the quality of American science and the competitiveness of American industry…(More)”.
Developing a public-interest training commons of books
Article by Authors Alliance: “…is pleased to announce a new project, supported by the Mellon Foundation, to develop an actionable plan for a public-interest book training commons for artificial intelligence. Northeastern University Library will be supporting this project and helping to coordinate its progress.
Access to books will play an essential role in how artificial intelligence develops. AI’s Large Language Models (LLMs) have a voracious appetite for text, and there are good reasons to think that these data sets should include books and lots of them. Over the last 500 years, human authors have written over 129 million books. These volumes, preserved for future generations in some of our most treasured research libraries, are perhaps the best and most sophisticated reflection of all human thinking. Their high editorial quality, breadth, and diversity of content, as well as the unique way they employ long-form narratives to communicate sophisticated and nuanced arguments and ideas make them ideal training data sources for AI.
These collections and the text embedded in them should be made available under ethical and fair rules as the raw material that will enable the computationally intense analysis needed to inform new AI models, algorithms, and applications imagined by a wide range of organizations and individuals for the benefit of humanity…(More)”
Experts warn about the ‘crumbling infrastructure’ of federal government data
Article by Hansi Lo Wang: “The stability of the federal government’s system for producing statistics, which the U.S. relies on to understand its population and economy, is under threat because of budget concerns, officials and data users warn.
And that’s before any follow-through on the new Trump administration and Republican lawmakers‘ pledges to slash government spending, which could further affect data production.
In recent months, budget shortfalls and the restrictions of short-term funding have led to the end of some datasets by the Bureau of Economic Analysis, known for its tracking of the gross domestic product, and to proposals by the Bureau of Labor Statistics to reduce the number of participants surveyed to produce the monthly jobs report. A “lack of multiyear funding” has also hurt efforts to modernize the software and other technology the BLS needs to put out its data properly, concluded a report by an expert panel tasked with examining multiple botched data releases last year.
Long-term funding questions are also dogging the Census Bureau, which carries out many of the federal government’s surveys and is preparing for the 2030 head count that’s set to be used to redistribute political representation and trillions in public funding across the country. Some census watchers are concerned budget issues may force the bureau to cancel some of its field tests for the upcoming tally, as it did with 2020 census tests for improving the counts in Spanish-speaking communities, rural areas and on Indigenous reservations.
While the statistical agencies have not been named specifically, some advocates are worried that calls to reduce the federal government’s workforce by President Trump and the new Republican-controlled Congress could put the integrity of the country’s data at greater risk…(More)”
Impact Curious?
Excerpt of book by Priya Parrish: “My journey to impact investing began when I was an undergraduate studying economics and entrepreneurship and couldn’t find any examples of people harnessing the power of business to improve the world. That was 20 years ago, before impact investing was a recognized strategy. Back then, just about everyone in the field was an entrepreneur experimenting with investment tools, trying to figure out how to do well financially while also making positive change. I joined right in.
The term “impact investing” has been around since 2007 but hasn’t taken hold the way I thought (and hoped) it might. There are still a lot of myths about what impact investing truly is and does, the most prevalent of which is that doing good won’t generate returns. This couldn’t be more false, yet it persists. This book is my attempt to debunk this myth and others like it, as well as make sense of the confusion, as it’s difficult for a newcomer to understand the jargon, sort through the many false or exaggerated claims, and follow the heated debates about this topic. This book is for the “impact curious,” or anyone who wants more than just financial returns from their investments. It is for anyone interested in finding out what their investments can do when aligned with purpose. It is for anyone who wishes to live in alignment with their values—in every aspect of their lives.
This particular excerpt from my book, The Little Book of Impact Investing, provides a history of the term and activity in the space. It addresses why now is a particularly good time to make business do more and do better—so that the world can and will too…(More)”.
Silencing Science Tracker
About: “The Silencing Science Tracker is a joint initiative of the Sabin Center for Climate Change Law and the Climate Science Legal Defense Fund. It is intended to record reports of federal, state, and local government attempts to “silence science” since the November 2016 election.
We define “silencing science” to include any action that has the effect of restricting or prohibiting scientific research, education, or discussion, or the publication or use of scientific information. We divide such actions into 7 categories as follows…(More)”
| Category | Examples | |
|---|---|---|
| Government Censorship | Changing the content of websites and documents to suppress or distort scientific information.Making scientific data more difficult to find or access.Restricting public communication by scientists. | |
| Self-Censorship | Scientists voluntarily changing the content of websites and documents to suppress or distort scientific information, potentially in response to political pressure. We note that it is often difficult to determine whether self-censorship is occurring and/or its cause. We do not take any position on the accuracy of any individual report on self-censorship. | |
| Budget Cuts | Reducing funding for existing agency programs involving scientific research or scientific education.Cancelling existing grants for scientific research or scientific education. We do not include, in the “budget cuts” category, government decisions to refuse new grant applications or funding for new agency programs. | |
| Personnel Changes | Removing scientists from agency positions or creating a hostile work environment.Appointing unqualified individuals to, or failing to fill, scientific positions.Changing the composition of scientific advisory board or other bodies to remove qualified scientists or add only industry-favored members.Eliminating government bodies involved in scientific research or education or the dissemination of scientific information. | |
| Research Hindrance | Destroying data needed to undertake scientific research.Preventing or restricting the publication of scientific research.Pressuring scientists to change research findings. | |
| Bias and Misrepresentation | Engaging in “cherry picking” or only disclosing certain scientific studies (e.g., that support a particular conclusion).Misrepresenting or mischaracterizing scientific studies.Disregarding scientific studies or advice in policy-making. | |
| Interference with Education | Changing science education standards to prevent or limit the teaching of proven scientific theories.Requiring or encouraging the teaching of discredited or unproven scientific theories.Preventing the use of factually accurate textbooks and other instructional materials (e.g., on religious grounds). |