Book by Andy J. Merolla and Jeffrey A. Hall: “We spend much of our waking lives communicating with others. How does each moment of interaction shape not only our relationships but also our worldviews? And how can we create moments of connection that improve our health and well-being, particularly in a world in which people are feeling increasingly isolated?
Drawing from their extensive research, Andy J. Merolla and Jeffrey A. Hall establish a new way to think about our relational life: as existing within “social biomes”—complex ecosystems of moments of interaction with others. Each interaction we have, no matter how unimportant or mundane it might seem, is a building block of our identities and beliefs. Consequently, the choices we make about how we interact and who we interact with—and whether we interact at all—matter more than we might know. Merolla and Hall offer a sympathetic, practical guide to our vital yet complicated social lives and propose realistic ways to embrace and enhance connection and hope…(More)”.
How is AI augmenting collective intelligence for the SDGs?
Article by UNDP: “Increasingly AI techniques like natural language processing, machine learning and predictive analytics are being used alongside the most common methods in collective intelligence, from citizen science and crowdsourcing to digital democracy platforms.
At its best, AI can be used to augment and scale the intelligence of groups. In this section we describe the potential offered by these new combinations of human and machine intelligence. First we look at the applications that are most common, where AI is being used to enhance efficiency and categorize unstructured data, before turning to the emerging role of AI – where it helps us to better understand complex systems.
These are the three main ways AI and collective intelligence are currently being used together for the SDGs:
1. Efficiency and scale of data processing
AI is being effectively incorporated into collective intelligence projects where timing is paramount and a key insight is buried deep within large volumes of unstructured data. This combination of AI and collective intelligence is most useful when decision makers require an early warning to help them manage risks and distribute public resources more effectively. For example, Dataminr’s First Alert system uses pre-trained machine learning models to sift through text and images scraped from the internet, as well as other data streams, such as audio broadcasts, to isolate early signals that anticipate emergency events…(More)”. (See also: Where and when AI and CI meet: exploring the intersection of artificial and collective intelligence towards the goal of innovating how we govern).
Data Localization: A Global Threat to Human Rights Online
Article by Freedom House: “From Pakistan to Zambia, governments around the world are increasingly proposing and passing data localization legislation. These laws, which refer to the rules governing the storage and transfer of electronic data across jurisdictions, are often justified as addressing concerns such as user privacy, cybersecurity, national security, and monopolistic market practices. Notwithstanding these laudable goals, data localization initiatives cause more harm than good, especially in legal environments with poor rule of law.
Data localization requirements can take many different forms. A government may require all companies collecting and processing certain types of data about local users to store the data on servers located in the country. Authorities may also restrict the foreign transfer of certain types of data or allow it only under narrow circumstances, such as after obtaining the explicit consent of users, receiving a license or permit from a public authority, or conducting a privacy assessment of the country to which the data will be transferred.
While data localization can have significant economic and security implications, the focus of this piece—inline with that of the Global Network Initiative and Freedom House—is on its potential human rights impacts, which are varied. Freedom House’s research shows that the rise in data localization policies worldwide is contributing to the global decline of internet freedom. Without robust transparency and accountability frameworks embedded into these provisions, digital rights are often put on the line. As these types of legislation continue to pop up globally, the need for rights-respecting solutions and norms for cross-border data flows is greater than ever…(More)”.
Why more AI researchers should collaborate with governments
Article by Mohamed Ibrahim: “Artificial intelligence (AI) is beginning to transform many industries, yet its use to improve public services remains limited globally. AI-based tools could streamline access to government benefits through online chatbots or automate systems by which citizens report problems such as potholes.
Currently, scholarly advances in AI are mostly confined to academic papers and conferences, rarely translating into actionable government policies or products. This means that the expertise at universities is not used to solve real-world problems. As a No10 Innovation Fellow with the UK government and a lecturer in spatial data science, I have explored the potential of AI-driven rapid prototyping in public policy.
Take Street.AI, a prototype smartphone app that I developed, which lets citizens report issues including potholes, street violence or illegal litter dumping by simply taking a picture through the app. The AI model classifies the problem automatically and alerts the relevant local authority, passing on the location and details of the issue. A key feature of the app is its on-device processing, which ensures privacy and reduces operational costs. Similar tools were tested as an early-warning system during the riots that swept the United Kingdom in July and August 2024.
AI models can also aid complex decision-making — for instance, that involved in determining where to build houses. The UK government plans to construct 1.5 million homes in the next 5 years, but planning laws require that several parameters be considered — such as proximity to schools, noise levels, the neighbourhoods’ built-up ratio and flood risk. The current strategy is to compile voluminous academic reports on viable locations, but an online dashboard powered by AI that can optimize across parameters would be much more useful to policymakers…(More)”.
Global data-driven prediction of fire activity
Paper by Francesca Di Giuseppe, Joe McNorton, Anna Lombardi & Fredrik Wetterhall: “Recent advancements in machine learning (ML) have expanded the potential use across scientific applications, including weather and hazard forecasting. The ability of these methods to extract information from diverse and novel data types enables the transition from forecasting fire weather, to predicting actual fire activity. In this study we demonstrate that this shift is feasible also within an operational context. Traditional methods of fire forecasts tend to over predict high fire danger, particularly in fuel limited biomes, often resulting in false alarms. By using data on fuel characteristics, ignitions and observed fire activity, data-driven predictions reduce the false-alarm rate of high-danger forecasts, enhancing their accuracy. This is made possible by high quality global datasets of fuel evolution and fire detection. We find that the quality of input data is more important when improving forecasts than the complexity of the ML architecture. While the focus on ML advancements is often justified, our findings highlight the importance of investing in high-quality data and, where necessary create it through physical models. Neglecting this aspect would undermine the potential gains from ML-based approaches, emphasizing that data quality is essential to achieve meaningful progress in fire activity forecasting…(More)”.
Developing countries are struggling to achieve their technology aims. Shared digital infrastructure is the answer
Article by Nii Simmonds: “The digital era offers remarkable prospects for both economic advancement and social development. Yet for emerging economies lacking energy, this potential often seems out of reach. The harsh truths of inconsistent electricity supply and scarce resources looms large over their digital ambitions. Nevertheless, a ray of hope shines through a strategy I call shared digital infrastructure (SDI). This cooperative model has the ability to turn these obstacles into opportunities for growth. By collaborating through regional country partnerships and bodies such as the Association of Southeast Asian Nations (ASEAN), the African Union (AU) and the Caribbean Community (CARICOM), these countries can harness the revolutionary power of digital technology, despite the challenges.
The digital economy is a critical driver of global GDP, with innovations in artificial intelligence, e-commerce and financial technology transforming industries at an unprecedented pace. At the heart of this transformation are data centres, which serve as the backbone of digital services, cloud computing and AI-driven applications. Yet many developing nations struggle to establish and maintain such facilities due to high energy costs, inadequate grid reliability and limited investment capital…(More)”.
Privacy-Enhancing and Privacy-Preserving Technologies in AI: Enabling Data Use and Operationalizing Privacy by Design and Default
Paper by the Centre for Information Policy Leadership at Hunton (“CIPL”): “provides an in-depth exploration of how privacy-enhancing technologies (“PETs”) are being deployed to address privacy within artificial intelligence (“AI”) systems. It aims to describe how these technologies can help operationalize privacy by design and default and serve as key business enablers, allowing companies and public sector organizations to access, share and use data that would otherwise be unavailable. It also seeks to demonstrate how PETs can address challenges and provide new opportunities across the AI life cycle, from data sourcing to model deployment, and includes real-world case studies…
As further detailed in the Paper, CIPL’s recommendations for boosting the adoption of PETs for AI are as follows:
Stakeholders should adopt a holistic view of the benefits of PETs in AI. PETs deliver value beyond addressing privacy and security concerns, such as fostering trust and enabling data sharing. It is crucial that stakeholders consider all these advantages when making decisions about their use.
Regulators should issue more clear and practical guidance to reduce regulatory uncertainty in the use of PETs in AI. While regulators increasingly recognize the value of PETs, clearer and more practical guidance is needed to help organizations implement these technologies effectively.
Regulators should adopt a risk-based approach to assess how PETs can meet standards for data anonymization, providing clear guidance to eliminate uncertainty. There is uncertainty around whether various PETs meet legal standards for data anonymization. A risk-based approach to defining anonymization standards could encourage wider adoption of PETs.
Deployers should take steps to provide contextually appropriate transparency to customers and data subjects. Given the complexity of PETs, deployers should ensure customers and data subjects understand how PETs function within AI models…(More)”.
LLM Social Simulations Are a Promising Research Method
Paper by Jacy Reese Anthis et al: “Accurate and verifiable large language model (LLM) simulations of human research subjects promise an accessible data source for understanding human behavior and training new AI systems. However, results to date have been limited, and few social scientists have adopted these methods. In this position paper, we argue that the promise of LLM social simulations can be achieved by addressing five tractable challenges. We ground our argument in a literature survey of empirical comparisons between LLMs and human research subjects, commentaries on the topic, and related work. We identify promising directions with prompting, fine-tuning, and complementary methods. We believe that LLM social simulations can already be used for exploratory research, such as pilot experiments for psychology, economics, sociology, and marketing. More widespread use may soon be possible with rapidly advancing LLM capabilities, and researchers should prioritize developing conceptual models and evaluations that can be iteratively deployed and refined at pace with ongoing AI advances…(More)”.
Enabling an Open-Source AI Ecosystem as a Building Block for Public AI
Policy brief by Katarzyna Odrozek, Vidisha Mishra, Anshul Pachouri, Arnav Nigam: “…informed by insights from 30 open dataset builders convened by Mozilla and EleutherAI and a policy analysis on open-source Artificial intelligence (AI) development, outlines four key areas for G7 action: expand access to open data, support sustainable governance, encourage policy alignment in open-source AI and local capacity building and identification of use cases. These steps will enhance AI competitiveness, accountability, and innovation, positioning the G7 as a leader in Responsible AI development…(More)”.
Massive, Unarchivable Datasets of Cancer, Covid, and Alzheimer’s Research Could Be Lost Forever
Article by Sam Cole: “Almost two dozen repositories of research and public health data supported by the National Institutes of Health are marked for “review” under the Trump administration’s direction, and researchers and archivists say the data is at risk of being lost forever if the repositories go down.
“The problem with archiving this data is that we can’t,” Lisa Chinn, Head of Research Data Services at the University of Chicago, told 404 Media. Unlike other government datasets or web pages, downloading or otherwise archiving NIH data often requires a Data Use Agreement between a researcher institution and the agency, and those agreements are carefully administered through a disclosure risk review process.
A message appeared at the top of multiple NIH websites last week that says: “This repository is under review for potential modification in compliance with Administration directives.”
Repositories with the message include archives of cancer imagery, Alzheimer’s disease research, sleep studies, HIV databases, and COVID-19 vaccination and mortality data…
“So far, it seems like what is happening is less that these data sets are actively being deleted or clawed back and more that they are laying off the workers whose job is to maintain them, update them and maintain the infrastructure that supports them,” a librarian affiliated with the Data Rescue Project told 404 Media. “In time, this will have the same effect, but it’s really hard to predict. People don’t usually appreciate, much less our current administration, how much labor goes into maintaining a large research dataset.”…(More)”.