Paper by Jiarui Liu, Wenkai Li, Zhijing Jin, Mona Diab: “In an era of model and data proliferation in machine learning/AI especially marked by the rapid advancement of open-sourced technologies, there arises a critical need for standardized consistent documentation. Our work addresses the information incompleteness in current human-generated model and data cards. We propose an automated generation approach using Large Language Models (LLMs). Our key contributions include the establishment of CardBench, a comprehensive dataset aggregated from over 4.8k model cards and 1.4k data cards, coupled with the development of the CardGen pipeline comprising a two-step retrieval process. Our approach exhibits enhanced completeness, objectivity, and faithfulness in generated model and data cards, a significant step in responsible AI documentation practices ensuring better accountability and traceability…(More)”.
Anti-Corruption and Integrity Outlook 2024
OECD Report: “This first edition of the OECD Anti-Corruption and Integrity Outlook analyses Member countries’ efforts to uphold integrity and fight corruption. Based on data from the Public Integrity Indicators, it analyses the performance of countries’ integrity frameworks, and explores how some of the main challenges to governments today (including the green transition, artificial intelligence, and foreign interference) are increasing corruption and integrity risks for countries. It also addresses how the shortcomings in integrity systems can impede countries’ responses to these major challenges. In providing a snapshot of how countries are performing today, the Outlook supports strategic planning and policy work to strengthen public integrity for the future…(More)”.
Big data for everyone
Article by Henrietta Howells: “Raw neuroimaging data require further processing before they can be used for scientific or clinical research. Traditionally, this could be accomplished with a single powerful computer. However, much greater computing power is required to analyze the large open-access cohorts that are increasingly being released to the community. And processing pipelines are inconsistently scripted, which can hinder reproducibility efforts. This creates a barrier for labs lacking access to sufficient resources or technological support, potentially excluding them from neuroimaging research. A paper by Hayashi and colleagues in Nature Methods offers a solution. They present https://brainlife.io, a freely available, web-based platform for secure neuroimaging data access, processing, visualization and analysis. It leverages ‘opportunistic computing’, which pools processing power from commercial and academic clouds, making it accessible to scientists worldwide. This is a step towards lowering the barriers for entry into big data neuroimaging research…(More)”.
We don’t need an AI manifesto — we need a constitution
Article by Vivienne Ming: “Loans drive economic mobility in America, even as they’ve been a historically powerful tool for discrimination. I’ve worked on multiple projects to reduce that bias using AI. What I learnt, however, is that even if an algorithm works exactly as intended, it is still solely designed to optimise the financial returns to the lender who paid for it. The loan application process is already impenetrable to most, and now your hopes for home ownership or small business funding are dying in a 50-millisecond computation…
In law, the right to a lawyer and judicial review are a constitutional guarantee in the US and an established civil right throughout much of the world. These are the foundations of your civil liberties. When algorithms act as an expert witness, testifying against you but immune to cross examination, these rights are not simply eroded — they cease to exist.
People aren’t perfect. Neither ethics training for AI engineers nor legislation by woefully uninformed politicians can change that simple truth. I don’t need to assume that Big Tech chief executives are bad actors or that large companies are malevolent to understand that what is in their self-interest is not always in mine. The framers of the US Constitution recognised this simple truth and sought to leverage human nature for a greater good. The Constitution didn’t simply assume people would always act towards that greater good. Instead it defined a dynamic mechanism — self-interest and the balance of power — that would force compromise and good governance. Its vision of treating people as real actors rather than better angels produced one of the greatest frameworks for governance in history.
Imagine you were offered an AI-powered test for post-partum depression. My company developed that very test and it has the power to change your life, but you may choose not to use it for fear that we might sell the results to data brokers or activist politicians. You have a right to our AI acting solely for your health. It was for this reason I founded an independent non-profit, The Human Trust, that holds all of the data and runs all of the algorithms with sole fiduciary responsibility to you. No mother should have to choose between a life-saving medical test and her civil rights…(More)”.
A Fourth Wave of Open Data? Exploring the Spectrum of Scenarios for Open Data and Generative AI
Report by Hannah Chafetz, Sampriti Saxena, and Stefaan G. Verhulst: “Since late 2022, generative AI services and large language models (LLMs) have transformed how many individuals access, and process information. However, how generative AI and LLMs can be augmented with open data from official sources and how open data can be made more accessible with generative AI – potentially enabling a Fourth Wave of Open Data – remains an under explored area.
For these reasons, The Open Data Policy Lab (a collaboration between The GovLab and Microsoft) decided to explore the possible intersections between open data from official sources and generative AI. Throughout the last year, the team has conducted a range of research initiatives about the potential of open data and generative including a panel discussion, interviews, and Open Data Action Labs – a series of design sprints with a diverse group of industry experts.
These initiatives were used to inform our latest report, “A Fourth Wave of Open Data? Exploring the Spectrum of Scenarios for Open Data and Generative AI,” (May 2024) which provides a new framework and recommendations to support open data providers and other interested parties in making open data “ready” for generative AI…
The report outlines five scenarios in which open data from official sources (e.g. open government and open research data) and generative AI can intersect. Each of these scenarios includes case studies from the field and a specific set of requirements that open data providers can focus on to become ready for a scenario. These include…(More)” (Arxiv).
“Data Commons”: Under Threat by or The Solution for a Generative AI Era ? Rethinking Data Access and Re-us
Article by Stefaan G. Verhulst, Hannah Chafetz and Andrew Zahuranec: “One of the great paradoxes of our datafied era is that we live amid both unprecedented abundance and scarcity. Even as data grows more central to our ability to promote the public good, so too does it remain deeply — and perhaps increasingly — inaccessible and privately controlled. In response, there have been growing calls for “data commons” — pools of data that would be (self-)managed by distinctive communities or entities operating in the public’s interest. These pools could then be made accessible and reused for the common good.
Data commons are typically the results of collaborative and participatory approaches to data governance [1]. They offer an alternative to the growing tendency toward privatized data silos or extractive re-use of open data sets, instead emphasizing the communal and shared value of data — for example, by making data resources accessible in an ethical and sustainable way for purposes in alignment with community values or interests such as scientific research, social good initiatives, environmental monitoring, public health, and other domains.
Data commons can today be considered (the missing) critical infrastructure for leveraging data to advance societal wellbeing. When designed responsibly, they offer potential solutions for a variety of wicked problems, from climate change to pandemics and economic and social inequities. However, the rapid ascent of generative artificial intelligence (AI) technologies is changing the rules of the game, leading both to new opportunities as well as significant challenges for these communal data repositories.
On the one hand, generative AI has the potential to unlock new insights from data for a broader audience (through conversational interfaces such as chats), fostering innovation, and streamlining decision-making to serve the public interest. Generative AI also stands out in the realm of data governance due to its ability to reuse data at a massive scale, which has been a persistent challenge in many open data initiatives. On the other hand, generative AI raises uncomfortable questions related to equitable access, sustainability, and the ethical re-use of shared data resources. Further, without the right guardrails, funding models and enabling governance frameworks, data commons risk becoming data graveyards — vast repositories of unused, and largely unusable, data.
In what follows, we lay out some of the challenges and opportunities posed by generative AI for data commons. We then turn to a ten-part framework to set the stage for a broader exploration on how to reimagine and reinvigorate data commons for the generative AI era. This framework establishes a landscape for further investigation; our goal is not so much to define what an updated data commons would look like but to lay out pathways that would lead to a more meaningful assessment of the design requirements for resilient data commons in the age of generative AI…(More)”
5 Ways AI Could Shake Up Democracy
Article by Shane Snider: “Tech luminary, author and Harvard Kennedy School lecturer Bruce Schneier on Tuesday offered his take on the promises and perils of artificial intelligence in key aspects of democracy.
In just two years, generative artificial intelligence (GenAI) has sparked a race to adopt (and defend against) the technology in government and the enterprise. It seems every aspect of life will soon be impacted — if not already feeling AI’s influence. A global race to place regulatory guardrails is taking shape even as companies and governments are spending billions of dollars implementing new AI technologies.
Schneier contends that five major areas of our democracy will likely see profound changes, including politics, lawmaking, administration, the legal system, and to citizens themselves.
“I don’t think it’s an exaggeration to predict that artificial intelligence will affect every aspect of our society, not necessarily by doing new things, but mostly by doing things that already or could be done by humans, are now replacing humans … There are potential changes in four dimensions: speed, scale, scope, and sophistication.”..(More)”.
The AI Mirror: How to Reclaim Our Humanity in an Age of Machine Thinking
Book by Shannon Vallor: “For many, technology offers hope for the future—that promise of shared human flourishing and liberation that always seems to elude our species. Artificial intelligence (AI) technologies spark this hope in a particular way. They promise a future in which human limits and frailties are finally overcome—not by us, but by our machines.
Yet rather than open new futures, today’s powerful AI technologies reproduce the past. Forged from oceans of our data into immensely powerful but flawed mirrors, they reflect the same errors, biases, and failures of wisdom that we strive to escape. Our new digital mirrors point backward. They show only where the data say that we have already been, never where we might venture together for the first time.
To meet today’s grave challenges to our species and our planet, we will need something new from AI, and from ourselves.
Shannon Vallor makes a wide-ranging, prophetic, and philosophical case for what AI could be: a way to reclaim our human potential for moral and intellectual growth, rather than lose ourselves in mirrors of the past. Rejecting prophecies of doom, she encourages us to pursue technology that helps us recover our sense of the possible, and with it the confidence and courage to repair a broken world. Vallor calls us to rethink what AI is and can be, and what we want to be with it…(More)”.
Digital Sovereignty: A Descriptive Analysis and a Critical Evaluation of Existing Models
Paper by Samuele Fratini et al: “Digital sovereignty is a popular yet still emerging concept. It is claimed by and related to various global actors, whose narratives are often competing and mutually inconsistent. Various scholars have proposed different descriptive approaches to make sense of the matter. We argue that existing works help advance our analytical understanding and that a critical assessment of existing forms of digital sovereignty is needed. Thus, the article offers an updated mapping of forms of digital sovereignty, while testing their effectiveness in response to radical changes and challenges. To do this, the article undertakes a systematic literature review, collecting 271 peer-reviewed articles from Google Scholar. They are used to identify descriptive features (how digital sovereignty is pursued) and value features (why digital sovereignty is pursued), which are then combined to produce four models: the rights-based model, market-oriented model, centralisation model, and state-based model. We evaluate their effectiveness within a framework of robust governance that accounts for the models’ ability to absorb the disruptions caused by technological advancements, geopolitical changes, and evolving societal norms. We find that none of the available models fully combines comprehensive regulations of digital technologies with a sufficient degree of responsiveness to fast-paced technological innovation and social and economic shifts. However, each offers valuable lessons to policymakers who wish to implement an effective and robust form of digital sovereignty…(More)”.
The Age of AI Nationalism and its Effects
Paper by Susan Ariel Aaronson: “This paper aims to illuminate how AI nationalistic policies may backfire. Over time, such actions and policies could alienate allies and prod other countries to adopt “beggar-thy neighbor” approaches to AI (The Economist: 2023; Kim: 2023 Shivakumar et al. 2024). Moreover, AI nationalism could have additional negative spillovers over time. Many AI experts are optimistic about the benefits of AI, whey they are aware of its many risks to democracy, equity, and society. They understand that AI can be a public good when it is used to mitigate complex problems affecting society (Gopinath: 2023; Okolo: 2023). However, when policymakers take steps to advance AI within their borders, they may — perhaps without intending to do so – make it harder for other countries with less capital, expertise, infrastructure, and data prowess to develop AI systems that could meet the needs of their constituents. In so doing, these officials could undermine the potential of AI to enhance human welfare and impede the development of more trustworthy AI around the world. (Slavkovik: 2024; Aaronson: 2023; Brynjolfsson and Unger: 2023; Agrawal et al. 2017).
Governments have many means of nurturing AI within their borders that do not necessarily discriminate between foreign and domestic producers of AI. Nevertheless, officials may be under pressure from local firms to limit the market power of foreign competitors. Officials may also want to use trade (for example, export controls) as a lever to prod other governments to change their behavior (Buchanan: 2020). Additionally, these officials may be acting in what they believe is the nation’s national security interest, which may necessitate that officials rely solely on local suppliers and local control. (GAO: 2021)
Herein the author attempts to illuminate AI nationalism and its consequences by answering 3 questions:
• What are nations doing to nurture AI capacity within their borders?
• Are some of these actions trade distorting?
• What are the implications of such trade-distorting actions?…(More)”