Can the world’s most successful index get back up the rankings?

Curated on Posted on by Stefaan Verhulst

Article by James Watson: “You know your ranking model is influential when national governments change policies with the explicit goal of boosting their position on your index. That was the power of the Ease of Doing Business Index (also known as Doing Business) until 2021.

However, the index’s success became its downfall. Some governments set up dedicated teams with an explicit goal of improving the country’s performance on the index. If those teams’ activity was solely focussed on positive policy reform, that would be great; unfortunately, in at least some cases, they were simply trying to game the results.

World Bank’s Business Ready Index

Index ranking optimisation (aka gaming the results)

To give an example of how that could happen, we need to take a brief detour into the world of qualitative indicators. Bear with me. In many indexes grappling with complex topics, there is a perennial problem of data availability. Imagine you want to measure the number of days it takes to set up a new business (this was one of the indicators in Doing Business). You will find that most of the time the data either doesn’t exist or is rarely updated by governments. Instead, put very simplistically, you’d need to ask a few experts or businesses for their views, and use those to create a numerical score for your index.

This is a valid approach, and it’s used in a lot of studies. Take Transparency International’s long-running Corruption Perceptions Index (CPI). Transparency International goes to great lengths to use robust and comparable data across countries, but measuring actual corruption is not viable — for obvious reasons. So the CPI does something different, and the clue is in the name: it measures people’s perceptions of corruption. It asks local businesses and experts whether they think there’s much bribery, nepotism and other forms of corruption in their country. This foundational input is then bolstered with other data points. The data doesn’t aim to measure corruption; instead, it’s about assessing which countries are more, or less, corrupt. 

Transparency International’s Corruption Perceptions Index (CPI)

This technique can work well, but it got a bit shaky as Doing Business’s fame grew. Some governments that were anxious to move up the rankings started urging the World Bank to tweak the methodology used to assess their ratings, or to use the views of specific experts. The analysts responsible for assessing a country’s scores and data points were put under significant pressure, often facing strong criticism from governments that didn’t agree with their assessments. In the end, an internal review showed that a number of countries’ scores had been improperly manipulated…The criticism must have stung, because the team behind the World Bank’s new Business Ready report has spent three years trying to address those issues. The new methodology handbook lands with a thump at 704 pages…(More)”.

Synthetic content and its implications for AI policy: a primer

Curated on Posted on by Stefaan Verhulst

UNESCO Paper: “The deployment of advanced Artificial Intelligence (AI) models, particularly generative AI, has sparked discussions regarding the creation and use of synthetic content – i.e. AI-generated or modified outputs, including text, images, sounds, and combinations thereof – and its impact on individuals, societies, and economies. This note explores the different ways in which synthetic content can be generated and used and proposes a taxonomy that encompasses synthetic media and deepfakes, among others. The taxonomy aims to systematize key characteristics, enhancing understanding and informing policy discussions. Key findings highlight both the potential benefits and concerns associated with synthetic content in fields like data analytics, environmental sustainability, education, creativity, and mis/disinformation and point to the need to frame them ethically, in line with the principles and values of UNESCO Recommendation on the Ethics of Artificial Intelligence. Finally, the note brings to the fore critical questions that policymakers and experts alike need to address to ensure that the development of AI technologies aligns with human rights, human dignity, and fundamental freedoms…(More)”.

Synthetic Data, Synthetic Media, and Surveillance

Curated on Posted on by Stefaan Verhulst

Paper by Aaron Martin and Bryce Newell: “Public and scholarly interest in the related concepts of synthetic data and synthetic media has exploded in recent years. From issues raised by the generation of synthetic datasets to train machine learning models to the public-facing, consumer availability of artificial intelligence (AI) powered image manipulation and creation apps and the associated increase in synthetic (or “deepfake”) media, these technologies have shifted from being niche curiosities of the computer science community to become topics of significant public, corporate, and regulatory import. They are emblematic of a “data-generation revolution” (Gal and Lynskey 2024: 1091) that is already raising pressing questions for the academic surveillance studies community. Within surveillance studies scholarship, Fussey (2022: 348) has argued that synthetic media is one of several “issues of urgent societal and planetary concern” and that it has “arguably never been more important” for surveillance studies “researchers to understand these dynamics and complex processes, evidence their implications, and translate esoteric knowledge to produce meaningful analysis.” Yet, while fields adjacent to surveillance studies have begun to explore the ethical risks of synthetic data, we currently perceive a lack of attention to the surveillance implications of synthetic data and synthetic media in published literature within our field. In response, this Dialogue is designed to help promote thinking and discussion about the links and disconnections between synthetic data, synthetic media, and surveillance…(More)”

Social licence for health data

Curated on Posted on by Stefaan Verhulst

Evidence Brief by NSW Government: “Social licence, otherwise referred to as social licence to operate, refers to an approval or consensus from the society members or the community for the users, either as a public or private enterprise or individual, to use their health data as desired or accepted under certain conditions. Social licence is a dynamic and fluid concept and is subject to change over time often influenced by societal and contextual factors.
The social licence is usually indicated through ongoing engagement and negotiations with the public and is not a contract with strict terms and conditions. It is, rather, a moral and ethical responsibility assumed by the data users based on trust and legitimacy, It supplements the techno-legal mechanisms to regulate the use of data.
For example, through public engagement, certain values and principles can emerge as pertinent to public support for using their data. Similarly, the public may view certain activities relating to their data use as acceptable and beneficial, implying their permission for certain activities or usecase scenarios. Internationally, although not always explicitly referred to as a social licence, the most common approach to establishing public trust and support and identifying common grounds or agreements on acceptable practices for use of data is through public engagement. Engagement methods and mechanisms for gaining public perspectives vary across countries (Table 1).
− Canada – Health Data Research Network Canada reports on social licence for uses of health data, based on deliberative discussions with 20 experienced public and patient advisors. The output is a list of agreements and disagreements on what uses and users of health data have social licence.
− New Zealand – In 2022, the Ministry of Health commissioned a survey on public perceptions on use of personal health information. This report identified conditions under which the public supports the re-use of their data…(More)”.

AI could help scale humanitarian responses. But it could also have big downsides

Curated on Posted on by Stefaan Verhulst

Article by Thalia Beaty: “As the International Rescue Committee copes with dramatic increases in displaced people in recent years, the refugee aid organization has looked for efficiencies wherever it can — including using artificial intelligence.

Since 2015, the IRC has invested in Signpost — a portfolio of mobile apps and social media channels that answer questions in different languages for people in dangerous situations. The Signpost project, which includes many other organizations, has reached 18 million people so far, but IRC wants to significantly increase its reach by using AI tools — if they can do so safely.

Conflict, climate emergencies and economic hardship have driven up demand for humanitarian assistance, with more than 117 million people forcibly displaced in 2024, according to the United Nations refugee agency. The turn to artificial intelligence technologies is in part driven by the massive gap between needs and resources.

To meet its goal of reaching half of displaced people within three years, the IRC is testing a network of AI chatbots to see if they can increase the capacity of their humanitarian officers and the local organizations that directly serve people through Signpost. For now, the pilot project operates in El Salvador, Kenya, Greece and Italy and responds in 11 languages. It draws on a combination of large language models from some of the biggest technology companies, including OpenAI, Anthropic and Google.

The chatbot response system also uses customer service software from Zendesk and receives other support from Google and Cisco Systems.

If they decide the tools work, the IRC wants to extend the technical infrastructure to other nonprofit humanitarian organizations at no cost. They hope to create shared technology resources that less technically focused organizations could use without having to negotiate directly with tech companies or manage the risks of deployment…(More)”.

Privacy guarantees for personal mobility data in humanitarian response

Curated on Posted on by Stefaan Verhulst

Paper by Nitin Kohli,  Emily Aiken & Joshua E. Blumenstock: “Personal mobility data from mobile phones and other sensors are increasingly used to inform policymaking during pandemics, natural disasters, and other humanitarian crises. However, even aggregated mobility traces can reveal private information about individual movements to potentially malicious actors. This paper develops and tests an approach for releasing private mobility data, which provides formal guarantees over the privacy of the underlying subjects. Specifically, we (1) introduce an algorithm for constructing differentially private mobility matrices and derive privacy and accuracy bounds on this algorithm; (2) use real-world data from mobile phone operators in Afghanistan and Rwanda to show how this algorithm can enable the use of private mobility data in two high-stakes policy decisions: pandemic response and the distribution of humanitarian aid; and (3) discuss practical decisions that need to be made when implementing this approach, such as how to optimally balance privacy and accuracy. Taken together, these results can help enable the responsible use of private mobility data in humanitarian response…(More)”.

Digital surveillance capitalism and cities: data, democracy and activism

Curated on Posted on by Stefaan Verhulst

Paper by Ashish Makanadar: “The rapid convergence of urbanization and digital technologies is fundamentally reshaping city governance through data-driven systems. This transformation, however, is largely controlled by surveillance capitalist entities, raising profound concerns for democratic values and citizen rights. As private interests extract behavioral data from public spaces without adequate oversight, the principles of transparency and civic participation are increasingly threatened. This erosion of data sovereignty represents a critical juncture in urban development, demanding urgent interdisciplinary attention. This comment proposes a paradigm shift in urban data governance, advocating for the reclamation of data sovereignty to prioritize community interests over corporate profit motives. The paper explores socio-technical pathways to achieve this goal, focusing on grassroots approaches that assert ‘data dignity’ through privacy-enhancing technologies and digital anonymity tools. It argues for the creation of distributed digital commons as viable alternatives to proprietary data silos, thereby democratizing access to and control over urban data. The discussion extends to long-term strategies, examining the potential of blockchain technologies and decentralized autonomous organizations in enabling self-sovereign data economies. These emerging models offer a vision of ‘crypto-cities’ liberated from extractive data practices, fostering environments where residents retain autonomy over their digital footprints. By critically evaluating these approaches, the paper aims to catalyze a reimagining of smart city technologies aligned with principles of equity, shared prosperity, and citizen empowerment. This realignment is essential for preserving democratic values in an increasingly digitized urban landscape…(More)”.

Rethinking the Measurement of Resilience for
Food and Nutrition Security

Curated on Posted on by Stefaan Verhulst

Paper by John M. Ulimwengu: “This paper presents a novel framework for assessing resilience in food systems, focusing on three dynamic metrics: return time, magnitude of deviation, and recovery rate. Traditional resilience measures have often relied on static and composite indicators, creating gaps in understanding the complex responses of food systems to shocks. This framework addresses these gaps, providing a more nuanced assessment of resilience in agrifood sectors. It highlights how integrating dynamic metrics enables policymakers to design tailored, sector-specific interventions that enhance resilience. Recognizing the data intensity required for these metrics, the paper indicates how emerging satellite imagery and advancements in artificial intelligence (AI) can make data collection both high-frequency and location-specific, at a fraction of the cost of traditional methods. These technologies facilitate a scalable approach to resilience measurement, enhancing the accuracy, timeliness, and accessibility of resilience data. The paper concludes with recommendations for refining resilience tools and adapting policy frameworks to better respond to the increasing challenges faced by food systems across the world…(More)”.

Setting the Standard: Statistical Agencies’ Unique Role in Building Trustworthy AI

Curated on Posted on by Stefaan Verhulst

Article by Corinna Turbes: “As our national statistical agencies grapple with new challenges posed by artificial intelligence (AI), many agencies face intense pressure to embrace generative AI as a way to reach new audiences and demonstrate technological relevance. However, the rush to implement generative AI applications risks undermining these agencies’ fundamental role as authoritative data sources. Statistical agencies’ foundational mission—producing and disseminating high-quality, authoritative statistical information—requires a more measured approach to AI adoption.

Statistical agencies occupy a unique and vital position in our data ecosystem, entrusted with creating the reliable statistics that form the backbone of policy decisions, economic planning, and social research. The work of these agencies demands exceptional precision, transparency, and methodological rigor. Implementation of generative AI interfaces, while technologically impressive, could inadvertently compromise the very trust and accuracy that make these agencies indispensable.

While public-facing interfaces play a valuable role in democratizing access to statistical information, statistical agencies need not—and often should not—rely on generative AI to be effective in that effort. For statistical agencies, an extractive AI approach – which retrieves and presents existing information from verified databases rather than generating new content – offers a more appropriate path forward. By pulling from verified, structured datasets and providing precise, accurate responses, extractive AI systems can maintain the high standards of accuracy required while making statistical information more accessible to users who may find traditional databases overwhelming. An extractive, rather than generative,  approach allows agencies to modernize data delivery while preserving their core mission of providing reliable, verifiable statistical information…(More)”

Revealed: bias found in AI system used to detect UK benefits fraud

Curated on Posted on by Stefaan Verhulst

Article by Robert Booth: “An artificial intelligence system used by the UK government to detect welfare fraud is showing bias according to people’s age, disability, marital status and nationality, the Guardian can reveal.

An internal assessment of a machine-learning programme used to vet thousands of claims for universal credit payments across England found it incorrectly selected people from some groups more than others when recommending whom to investigate for possible fraud.

The admission was made in documents released under the Freedom of Information Act by the Department for Work and Pensions (DWP). The “statistically significant outcome disparity” emerged in a “fairness analysis” of the automated system for universal credit advances carried out in February this year.

The emergence of the bias comes after the DWP this summer claimed the AI system “does not present any immediate concerns of discrimination, unfair treatment or detrimental impact on customers”.

This assurance came in part because the final decision on whether a person gets a welfare payment is still made by a human, and officials believe the continued use of the system – which is attempting to help cut an estimated £8bn a year lost in fraud and error – is “reasonable and proportionate”.

But no fairness analysis has yet been undertaken in respect of potential bias centring on race, sex, sexual orientation and religion, or pregnancy, maternity and gender reassignment status, the disclosures reveal.

Campaigners responded by accusing the government of a “hurt first, fix later” policy and called on ministers to be more open about which groups were likely to be wrongly suspected by the algorithm of trying to cheat the system…(More)”.