Paper by Miao Zhang, Salman Rahman, Vishwali Mhasawade and Rumi Chunara: “…New data sources and AI methods for extracting information are increasingly abundant and relevant to decision-making across societal applications. A notable example is street view imagery, available in over 100 countries, and purported to inform built environment interventions (e.g., adding sidewalks) for community health outcomes. However, biases can arise when decision-making does not account for data robustness or relies on spurious correlations. To investigate this risk, we analyzed 2.02 million Google Street View (GSV) images alongside health, demographic, and socioeconomic data from New York City. Findings demonstrate robustness challenges; built environment characteristics inferred from GSV labels at the intracity level often do not align with ground truth. Moreover, as average individual-level behavior of physical inactivity significantly mediates the impact of built environment features by census tract, intervention on features measured by GSV would be misestimated without proper model specification and consideration of this mediation mechanism. Using a causal framework accounting for these mediators, we determined that intervening by improving 10% of samples in the two lowest tertiles of physical inactivity would lead to a 4.17 (95% CI 3.84–4.55) or 17.2 (95% CI 14.4–21.3) times greater decrease in the prevalence of obesity or diabetes, respectively, compared to the same proportional intervention on the number of crosswalks by census tract. This study highlights critical issues of robustness and model specification in using emergent data sources, showing the data may not measure what is intended, and ignoring mediators can result in biased intervention effect estimates…(More)”
AI Localism Repository: A Tool for Local AI Governance
About: “In a world where AI continues to be ever more entangled with our communities, cities, and decision-making processes, local governments are stepping up to address the challenges of AI governance. Today, we’re excited to announce the launch of the newly updated AI Localism Repository—a curated resource designed to help local governments, researchers, and citizens understand how AI is being governed at the state, city, or community level.
What is AI Localism?
AI Localism refers to the actions taken by local decision-makers to address AI governance in their communities. Unlike national or global policies, AI Localism offers immediate solutions tailored to specific local conditions, creating opportunities for greater effectiveness and accountability in the governance of AI.
What’s the AI Localism Repository?
The AI Localism Repository is a collection of examples of AI governance measures from around the world, focusing on how local governments are navigating the evolving landscape of AI. This resource is more than just a list of laws—it highlights innovative methods of AI governance, from the creation of expert advisory groups to the implementation of AI pilot programs.
Why AI Localism Matters
Local governments often face unique challenges in regulating AI, from ethical considerations to the social impact of AI in areas like law enforcement, housing, and employment. Yet, local initiatives are frequently overlooked by national and global AI policy observatories. The AI Localism Repository fills this gap, offering a platform for local policymakers to share their experiences and learn from one another…(More)”
Governing AI for Humanity
The United Nations Secretary-General’s High-level Advisory Body on AI’s Final Report: “This report outlines a blueprint for addressing AI-related risks and sharing its transformative potential globally, including by:
- Urging the UN to lay the foundations of the first globally inclusive and distributed architecture for AI governance based on international cooperation;
- Proposing seven recommendations to address gaps in current AI governance arrangements;
- Calling on all governments and stakeholders to work together in governing AI to foster development and protection of all human rights.
This includes light institutional mechanisms to complement existing efforts and foster inclusive global AI governance arrangements that are agile, adaptive and effective to keep pace with AI’s evolution...(More)”.
New Data Browser on education, science, and culture
UNESCO: “The UIS is excited to introduce the new UIS Data Browser, which brings together all our data on education, science, and culture, making it a convenient resource for everyone, from policymakers to researchers.
With a refreshed interface, users can easily view and download customized data for their needs. The new browser also offers better tools for exploring metadata and documentation. Plus, the browser has great visualization features. You can filter indicators by country or region and create line or bar charts to see trends over time. It’s easy to share your findings on social media, too!
For those who like to dive deeper, a web-based UIS Data Application Programming Interface (API) allows for more technical data extraction for use in reports and applications. The UIS Data API provides access to all education, science, and culture data available on the UIS data browser through HTTP requests. It allows for the regular retrieval of data for custom analysis, visualizations, and applications…(More)”.
Trust in official statistics remains high but there’s still work to do
Article by Ian Diamond (UK): “..I’m excited about the potential of new data sources, and I want everyone in the UK to have the skills to understand and use the stats they allow us to create. With this in mind, we’re launching a whole host of new projects to bring our stats to the people:
How to videos
To benefit from stats, and be confident that they are reliable, we need to understand more about the data they have been derived from and how to read and use them.
Our new set of video guides are a great place to start, covering topics such as why data matters to how the ONS de-identifies them and where we get them from.
They are all available to watch on our YouTube channel.
Playground survey
During the 2023/2024 school year, we teamed up with the BBC and the Micro:bit Foundation to give children in primary schools the opportunity to take part in a nationwide playground survey.
The BBC Micro:bit Playground Survey is a wonderful way for children to learn data skills at an early age, getting to grips with data collection and analysis in a way that is relevant to their everyday lives, in a familiar and fun setting.
If children become data-literate now, they will be well prepared to navigate and take advantage of the huge amounts of data that will no doubt play an important role in their adult lives.
Keep an eye out for the results in October.
Navigating numbers – the ONS data education programme
We’ve also been busy developing a data education programme for students in further education or sixth form.
Navigating numbers: how data are used to create statistics includes a series of five classroom toolkits, exploring topics such as gender pay gaps, inflation, and health.
Created with the support of the Association of Colleges (AoC), this learning resource is free for teachers to use and available for download on the ONS website.
The ONS’s educational webinar series: Bringing data to life
If you want to learn more about measuring the cost of living or our nation’s health, then our new webinar series has you covered. These and other topics will be brought to life in this new series of online events, launching in September 2024…(More)”
Advancing Data Equity: An Action-Oriented Framework
WEF Report: “Automated decision-making systems based on algorithms and data are increasingly common today, with profound implications for individuals, communities and society. More than ever before, data equity is a shared responsibility that requires collective action to create data practices and systems that promote fair and just outcomes for all.
This paper, produced by members of the Global Future Council on Data Equity, proposes a data equity definition and framework for inquiry that spurs ongoing dialogue and continuous action towards implementing data equity in organizations. This framework serves as a dynamic tool for stakeholders committed to operationalizing data equity, across various sectors and regions, given the rapidly evolving data and technology landscapes…(More)”.
Augmenting the availability of historical GDP per capita estimates through machine learning
Paper by Philipp Koch, Viktor Stojkoski, and César A. Hidalgo: “Can we use data on the biographies of historical figures to estimate the GDP per capita of countries and regions? Here, we introduce a machine learning method to estimate the GDP per capita of dozens of countries and hundreds of regions in Europe and North America for the past seven centuries starting from data on the places of birth, death, and occupations of hundreds of thousands of historical figures. We build an elastic net regression model to perform feature selection and generate out-of-sample estimates that explain 90% of the variance in known historical income levels. We use this model to generate GDP per capita estimates for countries, regions, and time periods for which these data are not available and externally validate our estimates by comparing them with four proxies of economic output: urbanization rates in the past 500 y, body height in the 18th century, well-being in 1850, and church building activity in the 14th and 15th century. Additionally, we show our estimates reproduce the well-known reversal of fortune between southwestern and northwestern Europe between 1300 and 1800 and find this is largely driven by countries and regions engaged in Atlantic trade. These findings validate the use of fine-grained biographical data as a method to augment historical GDP per capita estimates. We publish our estimates with CI together with all collected source data in a comprehensive dataset…(More)”.
Taming Silicon Valley
Book by Gary Marcus: “On balance, will AI help humanity or harm it? AI could revolutionize science, medicine, and technology, and deliver us a world of abundance and better health. Or it could be a disaster, leading to the downfall of democracy, or even our extinction. In Taming Silicon Valley, Gary Marcus, one of the most trusted voices in AI, explains that we still have a choice. And that the decisions we make now about AI will shape our next century. In this short but powerful manifesto, Marcus explains how Big Tech is taking advantage of us, how AI could make things much worse, and, most importantly, what we can do to safeguard our democracy, our society, and our future.
Marcus explains the potential—and potential risks—of AI in the clearest possible terms and how Big Tech has effectively captured policymakers. He begins by laying out what is lacking in current AI, what the greatest risks of AI are, and how Big Tech has been playing both the public and the government, before digging into why the US government has thus far been ineffective at reining in Big Tech. He then offers real tools for readers, including eight suggestions for what a coherent AI policy should look like—from data rights to layered AI oversight to meaningful tax reform—and closes with how ordinary citizens can push for what is so desperately needed.
Taming Silicon Valley is both a primer on how AI has gotten to its problematic present state and a book of activism in the tradition of Abbie Hoffman’s Steal This Book and Thomas Paine’s Common Sense. It is a deeply important book for our perilous historical moment that every concerned citizen must read…(More)”.
G20 Compendium on Data Access and Sharing Across the Public Sector and with the Private Sector for Public Interest
OECD Report: “…presents practical examples from G20 members on data access and sharing, both across the public sector and between the public and private sectors in the public interest. The report supports G20 discussions on common opportunities, enablers and challenges to strengthen data access and sharing in the public sector, as well countries’ efforts and priorities in this policy area. It has been prepared by the OECD for the Brazilian G20 Presidency in co-ordination with the Ministry of Management and Innovation in Public Services, to inform the G20 Digital Economy Working Group at its September 2024 meeting…(More)”.
Place identity: a generative AI’s perspective
Paper by Kee Moon Jang et al: “Do cities have a collective identity? The latest advancements in generative artificial intelligence (AI) models have enabled the creation of realistic representations learned from vast amounts of data. In this study, we test the potential of generative AI as the source of textual and visual information in capturing the place identity of cities assessed by filtered descriptions and images. We asked questions on the place identity of 64 global cities to two generative AI models, ChatGPT and DALL·E2. Furthermore, given the ethical concerns surrounding the trustworthiness of generative AI, we examined whether the results were consistent with real urban settings. In particular, we measured similarity between text and image outputs with Wikipedia data and images searched from Google, respectively, and compared across cases to identify how unique the generated outputs were for each city. Our results indicate that generative models have the potential to capture the salient characteristics of cities that make them distinguishable. This study is among the first attempts to explore the capabilities of generative AI in simulating the built environment in regard to place-specific meanings. It contributes to urban design and geography literature by fostering research opportunities with generative AI and discussing potential limitations for future studies…(More)”.