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)”

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)”.

Unlocking data for climate action requires trusted marketplaces

Curated on Posted on by Stefaan Verhulst

Report by Digital Impact Alliance: “In 2024, the northern hemisphere recorded the hottest summer overall, the hottest day, and the hottest ever month of August. That same month – August 2024 – this warming fueled droughts in Italy and intensified typhoons that devastated parts of the Philippines, Taiwan, and China. The following month, new research calculated that warming is costing the global economy billions of dollars: an increase in extreme heat and severe drought costs about 0.2% of a country’s GDP. 

These are only the latest stories and statistics that illustrate the growing costs of climate change – data points that have emerged in the short time since we published our second Spotlight on unlocking climate data with open transaction networks.

This third paper in the series continues the work of the Joint Learning Network on Unlocking Data for Climate Action (Climate Data JLN). This multi-disciplinary network identified multiple promising models to explore in the context of unlocking data for climate action. This Spotlight paper examines the third of these models: data spaces. Through examination of data spaces in action, the paper analyzes the key elements that render them more or less applicable to specific climate-related data sets. Data spaces are relatively new and mostly conceptual, with only a handful of implementations in process and concentrated in a few geographic areas. While this model requires extensive up-front work to agree upon governance and technical standards, the result is an approach that overcomes trust and financing issues by maintaining data sovereignty and creating a marketplace for data exchange…(More)”.

Local Systems

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Position Paper by USAID: “…describes the key approaches USAID will use to translate systems thinking into systems practice. It focuses on ways USAID can better understand and engage local systems to support them in producing more sustainable results. Systems thinking is a mindset and set of tools that we use to understand how systems behave and produce certain results or outcomes. Systems practice is the application of systems thinking to better understand challenges and strengthen the capacity of local systems to unlock locally led, sustained progress. The shift from systems thinking to systems practice is driven by a desire to integrate systems practice throughout the Program Cycle and increase our capacity to actively and adaptively manage programming in ways that recognize complexity and help make our programs more effective and sustainable.

These approaches will be utilized alongside and within the context of USAID’s policies and guidance, including technical guidance for specific sectors, as well as evidence and lessons learned from partners around the world. Systems thinking is a long-standing discipline that can serve as a powerful tool for understanding and working with local systems. It has been a consistent component of USAID’s decades-long commitment to locally led development and humanitarian assistance. USAID uses systems thinking to better understand the complex and interrelated challenges we confront – from climate change to migration to governance – and the perspectives of diverse stakeholders on these issues. When we understand challenges as complex systems – where outcomes emerge from the interactions and relationships between actors and elements in that system – we can leverage and help strengthen the local capacities and relationships that will ultimately drive sustainable progress…(More)”.

Commission launches public consultation on the rules for researchers to access online platform data under the Digital Services Act

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Press Release: “Today, the Commission launched a public consultation on the draft delegated act on access to online platform data for vetted researchers under the Digital Services Act (DSA).

text Digital Services Act inside a white triangle against a blue background

With the Digital Services Act, researchers will for the first time have access to data to study systemic risks and to assess online platforms’ risk mitigation measures in the EU. It will allow the research community to play a vital role in scrutinising and safeguarding the online environment.

The draft delegated act clarifies the procedures on how researchers can access Very Large Operating Platforms’ and Search Engines’ data. It also sets out rules on data formats and data documentation requirements. Lastly, it establishes the DSA data access portal, a one-stop-shop for researchers, data providers, and DSCs to exchange information on data access requests. The consultation follows a first call for evidence.

The consultation will run until 26 November 2024. After gathering public feedback, the Commission plans to adopt the rules in the first quarter of 2025…(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)”.

The Emerging Age of AI Diplomacy

Curated on Posted on by Stefaan Verhulst

Article by Sam Winter-Levy: “In a vast conference room, below chandeliers and flashing lights, dozens of dancers waved fluorescent bars in an intricately choreographed routine. Green Matrix code rained down in the background on a screen that displayed skyscrapers soaring from a desert landscape. The world was witnessing the emergence of “a sublime and transcendent entity,” a narrator declared: artificial intelligence. As if to highlight AI’s transformative potential, a digital avatar—Artificial Superintelligence One—approached a young boy and together they began to sing John Lennon’s “Imagine.” The audience applauded enthusiastically. With that, the final day dawned on what one government minister in attendance described as the “world’s largest AI thought leadership event.”

This surreal display took place not in Palo Alto or Menlo Park but in Riyadh, Saudi Arabia, at the third edition of the city’s Global AI Summit, in September of this year. In a cavernous exhibition center next to the Ritz Carlton, where Crown Prince Mohammed bin Salman imprisoned hundreds of wealthy Saudis on charges of corruption in 2017,robots poured tea and mixed drinks. Officials in ankle-length white robes hailed Saudi Arabia’s progress on AI. American and Chinese technology companies pitched their products and announced memorandums of understanding with the government. Attendantsdistributed stickers that declared, “Data is the new oil.”

For Saudi Arabia and its neighbor, the United Arab Emirates (UAE), AI plays an increasingly central role in their attempts to transform their oil wealth into new economic models before the world transitions away from fossil fuels. For American AI companies, hungry for capital and energy, the two Gulf states and their sovereign wealth funds are tantalizing partners. And some policymakers in Washington see a once-in-a-generation opportunity to promise access to American computing power in a bid to lure the Gulf states away from China and deepen an anti-Iranian coalition in the Middle East….The two Gulf states’ interest in AI is not new, but it has intensified in recent months. Saudi Arabia plans to create a $40 billion fund to invest in AI and has set up Silicon Valley–inspired startup accelerators to entice coders to Riyadh. In 2019, the UAE launched the world’s first university dedicated to AI, and since 2021, the number of AI workers in the country has quadrupled, according to government figures. The UAE has also released a series of open-source large language models that it claims rival those of Google and Meta, and earlier this year it launched an investment firm focused on AI and semiconductors that could surpass $100 billion in assets under management…(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)”.