Stefaan Verhulst

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)”

Report by Joana Varon, Sasha Costanza-Chock, Mariana Tamari, Berhan Taye, and Vanessa Koetz: “‘Artificial Intelligence’ (AI) has become a buzzword all around the globe, with tech companies, research institutions, and governments all vying to define and shape its future. How can we escape the current context of AI development where certain power forces are pushing for models that, ultimately, automate inequalities and threaten socio-enviromental diversities? What if we could redefine AI? What if we could shift its production from a capitalist model to a more disruptive, inclusive, and decentralized one? Can we imagine and foster an AI Commons ecosystem that challenges the current dominant neoliberal logic of an AI arms race? An ecosystem encompassing researchers, developers, and activists who are thinking about AI from decolonial, transfeminist, antiracist, indigenous, decentralized, post-capitalist and/or socio-environmental justice perspectives?

This fieldscan research, commissioned by One Project and conducted by Coding Rights, aims to understand the (possibly) emerging “AI Common” ecosystem. Focused on key entities (organizations, cooperatives and collectives, networks, companies, projects, and others) from Africa, the Americas, and Europe advancing alternative possible AI futures, the authors identify 234 entities that are advancing the AI Commons ecosystem. The report finds powerful communities of practice, groups, and organizations producing nuanced criticism of the Big Tech-driven AI development ecosystem and, most importantly, imagining, developing, and, at times, deploying an alternative AI technology that’s informed and guided by the principles of decoloniality, feminism, antiracist, and post-capitalist AI systems…(More)”.

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)”

Paper by Eva Sørensen and Mark E. Warren: “While many democratic theorists recognise the necessity of reforming liberal democracies to keep pace with social change, they rarely consider what enables such reform. In this conceptual article, we suggest that liberal democracies are politically robust when they are able to continuously adapt and innovate how they operate when doing so is necessary to continue to serve key democratic functions. These functions include securing the empowered inclusion of those affected, collective agenda setting and will formation, and the making of joint decisions. Three current challenges highlight the urgency of adapting and innovating liberal democracies to become more politically robust: an increasingly assertive political culture, the digitalisation of political communication and increasing global interdependencies. A democratic theory of political robustness emphasises the need to strengthen the capacity of liberal democracies to adapt and innovate in response to changes, just as it helps to frame the necessary adaptations and innovations in times such as the present…(More)”

Book edited by Christian Montag and Raian Ali: “This book presents a recent framework proposed to understand how attitudes towards artificial intelligence are formed. It describes how the interplay between different variables, such as the modality of AI interaction, the user personality and culture, the type of AI applications (e.g. in the realm of education, medicine, transportation, among others), and the transparency and explainability of AI systems contributes to understand how user’s acceptance or a negative attitude towards AI develops. Gathering chapters from leading researchers with different backgrounds, this book offers a timely snapshot on factors that will be influencing the impact of artificial intelligence on societies…(More)”.

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)”.

Repository by the (UK) Local Government Association: “Building on the findings of our recent AI survey, which highlighted the need for practical examples, this bank showcases the diverse ways local authorities are leveraging AI. 

Within this collection, you’ll discover a spectrum of AI adoption, ranging from utilising AI assistants to streamline back-office tasks to pioneering the implementation of bespoke Large Language Models (LLMs). These real-world use cases exemplify the innovative spirit driving advancements in local government service delivery. 

Whether your council is at the outset of its AI exploration or seeking to expand its existing capabilities, this bank offers a wealth of valuable insights and best practices to support your organisation’s AI journey…(More)”.

Book edited by Paul F. M. J. Verschure: “Human existence depends critically on how well diverse social, cultural and political groups can collaborate. Yet the phenomenon of collaboration itself is ill-defined and badly understood, and there is no straightforward formula for its successful realization. In The Nature and Dynamics of Collaboration, edited by Paul F. M. J. Verschure, experts from wide-ranging disciplines examine how human collaboration arises, breaks down, and potentially recovers. They explore the different contexts, boundary conditions, and drivers of collaboration to expand understanding of the underlying dynamic, multiscale processes in an effort to increase chances for ethical, sustainable, and productive collaboration in the future. This volume is accompanied by twenty-four podcasts, which provide insights from real-world examples…(More)”.

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)”

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)”