Enabling Digital Innovation in Government

Curated on Posted on by Stefaan Verhulst

OECD Report: “…presents the OECD’s definition of GovTech (Chapter 2) and sets out the GovTech Policy Framework (Chapter 3). The framework is designed to guide governments on how to establish the conditions for successful, sustainable, and effective GovTech.

The framework consists of two parts: the GovTech Building Blocks and the GovTech Enablers. The building blocks (Chapter 3) represent the foundations at the micro-level needed to establish impactful GovTech practices within public sectors by introducing more agile practices, mitigating risks, and building meaningful collaboration with the GovTech ecosystem. These building blocks include:

  • Mature digital government infrastructure: including the necessary technology, infrastructure, tools, and data governance to enable both GovTech collaborations and the digital solutions they develop.
  • Capacities for collaboration and experimentation: within the public sector, including the digital skills and multidisciplinary teams; agile processes, tools, and methodologies; and a culture that encourages experimentation and accepts failure. 
  • Resources and implementation support: considering how to make funding available, how to evolve procurement approaches, and how to scale successful pilots across organisations and internationally.
  • Availability and maturity of GovTech partners: including acceleration programmes to support start-ups growth by facilitating access to capital, the scaling up of solutions, and minimising barriers to access procurement opportunities.

At the macro-level, the enablers (Chapter 4) instead create an environment that fosters the development of GovTech and facilitates good practices. This is done at the:

  • Strategic layer: where governments could use GovTech strategies and champions in senior leadership positions to mobilise support and set a clear direction for GovTech.
  • Institutional layer: where governments could seek collaboration and knowledge-sharing across institutions at the national, regional, or policy levels.
  • Network layer: where both governments and GovTech actors should seek to mobilise the network collectively to strengthen the GovTech practice and garner broader support from communities…(More)”

Annoyed Redditors tanking Google Search results illustrates perils of AI scrapers

Curated on Posted on by Stefaan Verhulst

Article by Scharon Harding: “A trend on Reddit that sees Londoners giving false restaurant recommendations in order to keep their favorites clear of tourists and social media influencers highlights the inherent flaws of Google Search’s reliance on Reddit and Google’s AI Overview.

In May, Google launched AI Overviews in the US, an experimental feature that populates the top of Google Search results with a summarized answer based on an AI model built into Google’s web rankings. When Google first debuted AI Overview, it quickly became apparent that the feature needed work with accuracy and its ability to properly summarize information from online sources. AI Overviews are “built to only show information that is backed up by top web results,” Liz Reid, VP and head of Google Search, wrote in a May blog post. But as my colleague Benj Edwards pointed out at the time, that setup could contribute to inaccurate, misleading, or even dangerous results: “The design is based on the false assumption that Google’s page-ranking algorithm favors accurate results and not SEO-gamed garbage.”

As Edwards alluded to, many have complained about Google Search results’ quality declining in recent years, as SEO spam and, more recently, AI slop float to the top of searches. As a result, people often turn to the Reddit hack to make Google results more helpful. By adding “site:reddit.com” to search results, users can hone their search to more easily find answers from real people. Google seems to understand the value of Reddit and signed an AI training deal with the company that’s reportedly worth $60 million per year…(More)”.

Exploring the Intersections of Open Data and Generative AI: Recent Additions to the Observatory

Curated on Posted on by Stefaan Verhulst

Blog by Roshni Singh, Hannah Chafetz, Andrew Zahuranec, Stefaan Verhulst: “The Open Data Policy Lab’s Observatory of Examples of How Open Data and Generative AI Intersect provides real-world use cases of where open data from official sources intersects with generative artificial intelligence (AI), building from the learnings from our report, “A Fourth Wave of Open Data? Exploring the Spectrum of Scenarios for Open Data and Generative AI.” 

The Observatory includes over 80 examples from several domains and geographies–ranging from supporting administrative work within the legal department of the Government of France to assisting researchers across the African continent in navigating cross-border data sharing laws. The examples include generative AI chatbots to improve access to services, conversational tools to help analyze data, datasets to improve the quality of the AI output, and more. A key feature of the Observatory is its categorization across our Spectrum of Scenarios framework, shown below. Through this effort, we aim to bring together the work already being done and identify ways to use generative AI for the public good.

Screenshot 2024 10 25 at 10.50.23 am

This Observatory is an attempt to grapple with the work currently being done to apply generative AI in conjunction with official open data. It does not make a value judgment on their efficacy or practices. Many of these examples have ethical implications, which merit further attention and study. 

From September through October, we added to the Observatory:

  • Bayaan Platform: A conversational tool by the Statistics Centre Abu Dhabi that provides decision makers with data analytics and visualization support.
  • Berufsinfomat: A generative AI tool for career coaching in Austria.
  • ChatTCU: A chatbot for Brazil’s Federal Court of Accounts.
  • City of Helsinki’s AI Register: An initiative aimed at leveraging open city data to enhance civic services and facilitate better engagement with residents.
  • Climate Q&A: A generative AI chatbot that provides information about climate change based on scientific reports.
  • DataLaw.Bot: A generative AI tool that disseminates data sharing regulations with researchers across several African countries…(More)”.

Open-Access AI: Lessons From Open-Source Software

Curated on Posted on by Stefaan Verhulst

Article by Parth Nobel, Alan Z. Rozenshtein, Chinmayi Sharma: “Before analyzing how the lessons of open-source software might (or might not) apply to open-access AI, we need to define our terms and explain why we use the term “open-access AI” to describe models like Llama rather than the more commonly used “open-source AI.” We join many others in arguing that “open-source AI” is a misnomer for such models. It’s misleading to fully import the definitional elements and assumptions that apply to open-source software when talking about AI. Rhetoric matters, and the distinction isn’t just semantic; it’s about acknowledging the meaningful differences in access, control, and development. 

The software industry definition of “open source” grew out of the free software movement, which makes the point that “users have the freedom to run, copy, distribute, study, change and improve” software. As the movement emphasizes, one should “think of ‘free’ as in ‘free speech,’ not as in ‘free beer.’” What’s “free” about open-source software is that users can do what they want with it, not that they initially get it for free (though much open-source software is indeed distributed free of charge). This concept is codified by the Open Source Initiative as the Open Source Definition (OSD), many aspects of which directly apply to Llama 3.2. Llama 3.2’s license makes it freely redistributable by license holders (Clause 1 of the OSD) and allows the distribution of the original models, their parts, and derived works (Clauses 3, 7, and 8). ..(More)”.

Quality Assessment of Volunteered Geographic Information

Curated on Posted on by Stefaan Verhulst

Paper by Donia Nciri et al: “Traditionally, government and national mapping agencies have been a primary provider of authoritative geospatial information. Today, with the exponential proliferation of Information and Communication Technologies or ICTs (such as GPS, mobile mapping and geo-localized web applications, social media), any user becomes able to produce geospatial information. This participatory production of geographical data gives birth to the concept of Volunteered Geographic Information (VGI). This phenomenon has greatly contributed to the production of huge amounts of heterogeneous data (structured data, textual documents, images, videos, etc.). It has emerged as a potential source of geographic information in many application areas. Despite the various advantages associated with it, this information lacks often quality assurance, since it is provided by diverse user profiles. To address this issue, numerous research studies have been proposed to assess VGI quality in order to help extract relevant content. This work attempts to provide an overall review of VGI quality assessment methods over the last decade. It also investigates varied quality assessment attributes adopted in recent works. Moreover, it presents a classification that forms a basis for future research. Finally, it discusses in detail the relevance and the main limitations of existing approaches and outlines some guidelines for future developments…(More)”.

Proactive Mapping to Manage Disaster

Curated on Posted on by Stefaan Verhulst

Article by Andrew Mambondiyani: “..In March 2019, Cyclone Idai ravaged Zimbabwe, killing hundreds of people and leaving a trail of destruction. The Global INFORM Risk Index data shows that Zimbabwe is highly vulnerable to extreme climate-related events like floods, cyclones, and droughts, which in turn destroy infrastructure, displace people, and result in loss of lives and livelihoods.

Severe weather events like Idai have exposed the shortcomings of Zimbabwe’s traditional disaster-management system, which was devised to respond to environmental disasters by providing relief and rehabilitation of infrastructure and communities. After Idai, a team of climate-change researchers from three Zimbabwean universities and the local NGO DanChurchAid (DCA) concluded that the nation must adopt a more proactive approach by establishing an early-warning system to better prepare for and thereby prevent significant damage and death from such disasters.

In response to these findings, the Open Mapping Hub—Eastern and Southern Africa (ESA Hub)—launched a program in 2022 to develop an anticipatory-response approach in Zimbabwe. The ESA Hub is a regional NGO based in Kenya created by the Humanitarian OpenStreetMap Team (HOT), an international nonprofit that uses open-mapping technology to reduce environmental disaster risk. One of HOT’s four global hubs and its first in Africa, the ESA Hub was created in 2021 to facilitate the aggregation, utilization, and dissemination of high-quality open-mapping data across 23 countries in Eastern and Southern Africa. Open-source expert Monica Nthiga leads the hub’s team of 13 experts in mapping, open data, and digital content. The team collaborates with community-based organizations, humanitarian organizations, governments, and UN agencies to meet their specific mapping needs to best anticipate future climate-related disasters.

“The ESA Hub’s [anticipatory-response] project demonstrates how preemptive mapping can enhance disaster preparedness and resilience planning,” says Wilson Munyaradzi, disaster-services manager at the ESA Hub.

Open-mapping tools and workflows enable the hub to collect geospatial data to be stored, edited, and reviewed for quality assurance prior to being shared with its partners. “Geospatial data has the potential to identify key features of the landscape that can help plan and prepare before disasters occur so that mitigation methods are put in place to protect lives and livelihoods,” Munyaradzi says…(More)”.

When combinations of humans and AI are useful: A systematic review and meta-analysis

Curated on Posted on by Stefaan Verhulst

Paper by Michelle Vaccaro, Abdullah Almaatouq & Thomas Malone: “Inspired by the increasing use of artificial intelligence (AI) to augment humans, researchers have studied human–AI systems involving different tasks, systems and populations. Despite such a large body of work, we lack a broad conceptual understanding of when combinations of humans and AI are better than either alone. Here we addressed this question by conducting a preregistered systematic review and meta-analysis of 106 experimental studies reporting 370 effect sizes. We searched an interdisciplinary set of databases (the Association for Computing Machinery Digital Library, the Web of Science and the Association for Information Systems eLibrary) for studies published between 1 January 2020 and 30 June 2023. Each study was required to include an original human-participants experiment that evaluated the performance of humans alone, AI alone and human–AI combinations. First, we found that, on average, human–AI combinations performed significantly worse than the best of humans or AI alone (Hedges’ g = −0.23; 95% confidence interval, −0.39 to −0.07). Second, we found performance losses in tasks that involved making decisions and significantly greater gains in tasks that involved creating content. Finally, when humans outperformed AI alone, we found performance gains in the combination, but when AI outperformed humans alone, we found losses. Limitations of the evidence assessed here include possible publication bias and variations in the study designs analysed. Overall, these findings highlight the heterogeneity of the effects of human–AI collaboration and point to promising avenues for improving human–AI systems…(More)”.

Make it make sense: the challenge of data analysis in global deliberation

Curated on Posted on by Stefaan Verhulst

Blog by Iñaki Goñi: “From climate change to emerging technologies to economic justice to space, global and transnational deliberation is on the rise. Global deliberative processes aim to bring citizen-centred governance to issues that no single nation can resolve alone. Running deliberative processes at this scale poses a unique set of challenges. How to select participants, make the forums accountable, impactful, fairly designed, and aware of power imbalances, are all crucial and open questions….

Massifying participation will be key to invigorating global deliberation. Assemblies will have a better chance of being seen as legitimate, fair, and publicly supported if they involve thousands or even millions of diverse participants. This raises an operational challenge: how to systematise political ideas from many people across the globe.

In a centralised global assembly, anything from 50 to 500 citizens from various countries engage in a single deliberation and produce recommendations or political actions by crossing languages and cultures. In a distributed assembly, multiple gatherings are convened locally that share a common but flexible methodology, allowing participants to discuss a common issue applied both to local and global contexts. Either way, a global deliberation process demands the organisation and synthesis of possibly thousands of ideas from diverse languages and cultures around the world.

How could we ever make sense of all that data to systematise citizens’ ideas and recommendations? Most people turn their heads to computational methods to help reduce complexity and identify patterns. First up, one technique for analysing text amounts to little more than simple counting, through which we can produce something like a frequency table or a wordcloud…(More)”.

Nature-rich nations push for biodata payout

Curated on Posted on by Stefaan Verhulst

Article by Lee Harris: “Before the current generation of weight-loss drugs, there was hoodia, a cactus that grows in southern Africa’s Kalahari Desert, and which members of the region’s San tribe have long used to stave off hunger. UK-based Phytopharm licensed the active ingredient in the cactus in 1996, and made numerous attempts to commercialise weight-loss products derived from it.

The company won licensing deals with Pfizer and Unilever, but drew outrage from campaigners who argued that the country was ripping off indigenous groups that had made the discovery. Indignation grew after the chief executive said it could not compensate local tribes because “the people who discovered the plant have disappeared”. (They had not).

This is just one example of companies using biological resources discovered in other countries for financial gain. The UN has attempted to set fairer terms with treaties such as the 1992 Convention on Biological Diversity, which deals with the sharing of genetic resources. But this approach has been seen by many developing countries as unsatisfactory. And earlier tools governing trade in plants and microbes may become less useful as biological data is now frequently transmitted in the form of so-called digital sequence information — the genetic code derived from those physical resources.

Now, the UN is working on a fund to pay stewards of biodiversity — notably communities in lower-income countries — for discoveries made with genetic data from their ecosystems. The mechanism was established in 2022 as part of the Conference of Parties to the UN Convention on Biological Diversity, a sister process to the climate “COP” initiative. But the question of how it will be governed and funded will be on the table at the October COP16 summit in Cali, Colombia.

If such a fund comes to fruition — a big “if” — it could raise billions for biodiversity goals. The sectors that depend on this genetic data — notably, pharmaceuticals, biotech and agribusiness — generate revenues exceeding $1tn annually, and African countries plan to push for these sectors to contribute 1 per cent of all global retail sales to the fund, according to Bloomberg.

There’s reason to temper expectations, however. Such a fund would lack the power to compel national governments or industries to pay up. Instead, the strategy is focused around raising ambition — and public pressure — for key industries to make voluntary contributions…(More)”.

The Critical Role of Questions in Building Resilient Democracies

Curated on Posted on by Stefaan Verhulst

Article by Stefaan G. Verhulst, Hannah Chafetz, and Alex Fischer: “Asking questions in new and participatory ways can complement advancements in data science and AI while enabling more inclusive and more adaptive democracies…

Yet a crisis, as the saying goes, always contains kernels of opportunity. Buried within our current dilemma—indeed, within one of the underlying causes of it—is a potential solution. Democracies are resilient and adaptive, not static. And importantly, data and artificial intelligence (AI), if implemented responsibly, can contribute to making them more resilient. Technologies such as AI-supported digital public squares and crowd-sourcing are examples of how generative AI and large language models can improve community connectivity, societal health, and public services. Communities can leverage these tools for democratic participation and democratizing information. Through this period of technological transition, policy makers and communities are imagining how digital technologies can better engage our collective intelligence…

Achieving this requires new tools and approaches, specifically the collective process of asking better questions.

Formulated inclusively, questions help establish shared priorities and impart focus, efficiency, and equity to public policy. For instance, school systems can identify indicators and patterns of experiences, such as low attendance rates, that signal a student is at risk of not completing school. However, they rarely ask the positive outlier question of what enables some at-risk students to overcome challenges and finish school. Is it a good teacher relationship, an after-school program, the support of a family member, or a combination of these and other factors? Asking outlier (and orphan, or overlooked and neglected) questions can help refocus programs and guide policies toward areas with the highest potential for impact.

Not asking the right questions can also have adverse effects. For example, many city governments have not asked whether and how people of different genders, in different age groups, or with different physical mobility needs experience local public transportation systems. Creating the necessary infrastructure for people with a variety of needs to travel safely and efficiently increases health and well-being. Questions like whether sidewalks are big enough for strollers and whether there is sufficient public transport near schools can help spotlight areas for improvement, and show where age- or gender-disaggregated data is needed most…(More)”.