From the Economic Graph to Economic Insights: Building the Infrastructure for Delivering Labor Market Insights from LinkedIn Data


Blog by Patrick Driscoll and Akash Kaura: “LinkedIn’s vision is to create economic opportunity for every member of the global workforce. Since its inception in 2015, the Economic Graph Research and Insights (EGRI) team has worked to make this vision a reality by generating labor market insights such as:

In this post, we’ll describe how the EGRI Data Foundations team (Team Asimov) leverages LinkedIn’s cutting-edge data infrastructure tools such as Unified Metrics PlatformPinot, and Datahub to ensure we can deliver data and insights robustly, securely, and at scale to a myriad of partners. We will illustrate this through a case study of how we built the pipeline for our most well-known and oft-cited flagship metric: the LinkedIn Hiring Rate…(More)”.

AI and Global Governance: Modalities, Rationales, Tensions


Paper by Michael Veale, Kira Matus and Robert Gorwa: “Artificial intelligence (AI) is a salient but polarizing issue of recent times. Actors around the world are engaged in building a governance regime around it. What exactly the “it” is that is being governed, how, by who, and why—these are all less clear. In this review, we attempt to shine some light on those questions, considering literature on AI, the governance of computing, and regulation and governance more broadly. We take critical stock of the different modalities of the global governance of AI that have been emerging, such as ethical councils, industry governance, contracts and licensing, standards, international agreements, and domestic legislation with extraterritorial impact. Considering these, we examine selected rationales and tensions that underpin them, drawing attention to the interests and ideas driving these different modalities. As these regimes become clearer and more stable, we urge those engaging with or studying the global governance of AI to constantly ask the important question of all global governance regimes: Who benefits?…(More)”.

Artificial Intelligence in the COVID-19 Response


Report by Sean Mann, Carl Berdahl, Lawrence Baker, and Federico Girosi: “We conducted a scoping review to identify AI applications used in the clinical and public health response to COVID-19. Interviews with stakeholders early in the research process helped inform our research questions and guide our study design. We conducted a systematic search, screening, and full text review of both academic and gray literature…

  • AI is still an emerging technology in health care, with growing but modest rates of adoption in real-world clinical and public health practice. The COVID-19 pandemic showcased the wide range of clinical and public health functions performed by AI as well as the limited evidence available on most AI products that have entered use.
  • We identified 66 AI applications (full list in Appendix A) used to perform a wide range of diagnostic, prognostic, and treatment functions in the clinical response to COVID-19. This included applications used to analyze lung images, evaluate user-reported symptoms, monitor vital signs, predict infections, and aid in breathing tube placement. Some applications were used by health systems to help allocate scarce resources to patients.
  • Many clinical applications were deployed early in the pandemic, and most were used in the United States, other high-income countries, or China. A few applications were used to care for hundreds of thousands or even millions of patients, although most were used to an unknown or limited extent.
  • We identified 54 AI-based public health applications used in the pandemic response. These included AI-enabled cameras used to monitor health-related behavior and health messaging chatbots used to answer questions about COVID-19. Other applications were used to curate public health information, produce epidemiologic forecasts, or help prioritize communities for vaccine allocation and outreach efforts.
  • We found studies supporting the use of 39 clinical applications and 8 public health applications, although few of these were independent evaluations, and we found no clinical trials evaluating any application’s impact on patient health. We found little evidence available on entire classes of applications, including some used to inform care decisions such as patient deterioration monitors.
  • Further research is needed, particularly independent evaluations on application performance and health impacts in real-world care settings. New guidance may be needed to overcome the unique challenges to evaluating AI application impacts on patient- and population-level health outcomes….(More)” – See also: The #Data4Covid19 Review

From LogFrames to Logarithms – A Travel Log


Article by Karl Steinacker and Michael Kubach: “..Today, authorities all over the world are experimenting with predictive algorithms. That sounds technical and innocent but as we dive deeper into the issue, we realise that the real meaning is rather specific: fraud detection systems in social welfare payment systems. In the meantime, the hitherto banned terminology had it’s come back: welfare or social safety nets are, since a couple of years, en vogue again. But in the centuries-old Western tradition, welfare recipients must be monitored and, if necessary, sanctioned, while those who work and contribute must be assured that there is no waste. So it comes at no surprise that even today’s algorithms focus on the prime suspect, the individual fraudster, the undeserving poor.

Fraud detection systems promise that the taxpayer will no longer fall victim to fraud and efficiency gains can be re-directed to serve more people. The true extent of welfare fraud is regularly exaggerated  while the costs of such systems is routinely underestimated. A comparison of the estimated losses and investments doesn’t take place. It is the principle to detect and punish the fraudsters that prevail. Other issues don’t rank high either, for example on how to distinguish between honest mistakes and deliberate fraud. And as case workers spent more time entering and analysing data and in front of a computer screen, the less they have time and inclination to talk to real people and to understand the context of their life at the margins of society.

Thus, it can be said that routinely hundreds of thousands of people are being scored. Example Denmark: Here, a system called Udbetaling Danmark was created in 2012 to streamline the payment of welfare benefits. Its fraud control algorithms can access the personal data of millions of citizens, not all of whom receive welfare payments. In contrast to the hundreds of thousands affected by this data mining, the number of cases referred to the Police for further investigation are minute. 

In the city of Rotterdam in the Netherlands every year, data of 30,000 welfare recipients is investigated in order to flag suspected welfare cheats. However, an analysis of its scoring system based on machine learning and algorithms showed systemic discrimination with regard to ethnicity, age, gender, and parenthood. It revealed evidence of other fundamental flaws making the system both inaccurate and unfair. What might appear to a caseworker as a vulnerability is treated by the machine as grounds for suspicion. Despite the scale of data used to calculate risk scores, the output of the system is not better than random guesses. However, the consequences of being flagged by the “suspicion machine” can be drastic, with fraud controllers empowered to turn the lives of suspects inside out.

As reported by the World Bank, the recent Covid-19 pandemic provided a great push to implement digital social welfare systems in the global South. In fact, for the World Bank the so-called Digital Public Infrastructure (DPI), enabling “Digitizing Government to Person Payments (G2Px)”, are as fundamental for social and economic development today as physical infrastructure was for previous generations. Hence, the World Bank is finances globally systems modelled after the Indian Aadhaar system, where more than a billion persons have been registered biometrically. Aadhaar has become, for all intents and purposes, a pre-condition to receive subsidised food and other assistance for 800 million Indian citizens.

Important international aid organisations are not behaving differently from states. The World Food Programme alone holds data of more than 40 million people on its Scope data base. Unfortunately, WFP like other UN organisations, is not subject to data protection laws and the jurisdiction of courts. This makes the communities they have worked with particularly vulnerable.

In most places, the social will become the metric, where logarithms determine the operational conduit for delivering, controlling and withholding assistance, especially welfare payments. In other places, the power of logarithms may go even further, as part of trust systems, creditworthiness, and social credit. These social credit systems for individuals are highly controversial as they require mass surveillance since they aim to track behaviour beyond financial solvency. The social credit score of a citizen might not only suffer from incomplete, or inaccurate data, but also from assessing political loyalties and conformist social behaviour…(More)”.

An Action Plan Towards a “New Deal on Data” in Africa


Blog by Charlie Martial Ngounou, Hannah Chafetz, Sampriti Saxena, Adrienne Schmoeker, Stefaan G. Verhulst, & Andrew J. Zahuranec: “To help accelerate responsible data use across the African data ecosystem, AfroLeadership with the support of The GovLab hosted two Open Data Action Labs in March and April 2023 focused on advancing open data policy across Africa. The Labs brought together domain experts across the African data ecosystem to build upon the African Union’s Data Policy Framework and develop an instrument to help realize Agenda 2063.

The Labs included discussions about the current state of open data policy and what could be involved in a “New Deal on Data” across the African continent. Specifically, the Labs explored how open data across African countries and communities could become more:

  1. Purpose-led: how to strengthen the value proposition of and incentives for open data and data re-use, and become purpose-led?
  2. Practice-led: how to accelerate the implementation of open data and data re-use policies, moving from policy to practice?
  3. People-led: how to trigger engagement, collaboration and coordination with communities and stakeholders toward advancing data rights, community interests, and diversity of needs and capacities?

Following the Labs, the organizing team conducted a brainstorming session to synthesize the insights gathered and develop an action plan towards a “New Deal on Data” for Africa. Below we provide a summary of our action plan. The action plan includes four vehicles that could make progress towards becoming purpose-, practice-, and people-led. These include:

  1. A “New Deal” Observatory: An online resource that takes stock of the the current state of open data policies, barriers to implementation, and use cases from the local to continental levels
  2. A Community-Led Platform: A solutions platform that helps advance data stewardship across African countries and communities
  3. “New Deal” Investment: Supporting the development of locally sourced solutions and nuanced technologies tailored to the African context
  4. Responsible Data Stewardship Framework: A framework that open data stewards can use to support their existing efforts when looking to encourage or implement grassroots policies…(More)”

Filling Africa’s Data Gap


Article by Jendayi Frazer and Peter Blair Henry: “Every few years, the U.S. government launches a new initiative to boost economic growth in Africa. In bold letters and with bolder promises, the White House announces that public-private partnerships hold the key to growth on the continent. It pledges to make these partnerships a cornerstone of its Africa policy, but time and again it fails to deliver.

A decade after U.S. President Barack Obama rolled out Power Africa—his attempt to solve Africa’s energy crisis by mobilizing private capital—half of the continent’s sub-Saharan population remains without access to electricity. In 2018, the Trump administration proclaimed that its Prosper Africa initiative would counter China’s debt-trap diplomacy and “expand African access to business finance.” Five years on, Chad, Ethiopia, Ghana, and Zambia are in financial distress and pleading for debt relief from Beijing and other creditors. Yet the Biden administration is once more touting the potential of public-private investment in Africa, organizing high-profile visits and holding leadership summits to prove that this time, the United States is “all in” on the continent.

There is a reason these efforts have yielded so little: goodwill tours, clever slogans, and a portfolio of G-7 pet projects in Africa do not amount to a sound investment pitch. Potential investors, public and private, need to know which projects in which countries are economically and financially worthwhile. Above all, that requires current and comprehensive data on the expected returns that investment in infrastructure in the developing world can yield. At present, investors lack this information, so they pass. If the United States wants to “build back better” in Africa—to expand access to business finance and encourage countries on the continent to choose sustainable and high-quality foreign investment over predatory lending from China and Russia—it needs to give investors access to better data…(More)”.

Model evaluation for extreme risks


Paper by Toby Shevlane et al: “Current approaches to building general-purpose AI systems tend to produce systems with both beneficial and harmful capabilities. Further progress in AI development could lead to capabilities that pose extreme risks, such as offensive cyber capabilities or strong manipulation skills. We explain why model evaluation is critical for addressing extreme risks. Developers must be able to identify dangerous capabilities (through “dangerous capability evaluations”) and the propensity of models to apply their capabilities for harm (through “alignment evaluations”). These evaluations will become critical for keeping policymakers and other stakeholders informed, and for making responsible decisions about model training, deployment, and security.

Figure 1 | The theory of change for model evaluations for extreme risk. Evaluations for dangerous capabilities and alignment inform risk assessments, and are in turn embedded into important governance processes…(More)”.

How to decode modern conflicts with cutting-edge technologies


Blog by Mykola Blyzniuk: “In modern warfare, new technologies are increasingly being used to manipulate information and perceptions on the battlefield. This includes the use of deep fakes, or the malicious use of ICT (Information and Communication Technologies).

Likewise, emerging tech can be instrumental in documenting human rights violationstracking the movement of troops and weaponsmonitoring public sentiments and the effects of conflict on civilians and exposing propaganda and disinformation.

The dual use of new technologies in modern warfare highlights the need for further investigation. Here are two examples how the can be used to advance politial analysis and situational awareness…

The world of Natural Language Processing (NLP) technology took a leap with a recent study on the Russia-Ukraine conflict by Uddagiri Sirisha and Bolem Sai Chandana of the School of Computer Science and Engineering at Vellore Institute of Technology Andhra Pradesh ( VIT-AP) University in Amaravathi Andhra Pradesh, India.

The researchers developed a novel artificial intelligence model to analyze whether a piece of text is positive, negative or neutral in tone. This new model referred to as “ABSA-based ROBERTa-LSTM”, looks at not just the overall sentiment of a piece of text but also the sentiment towards specific aspects or entities mentioned in the text. The study took a pre-processed dataset of 484,221 tweets collected during April — May 2022 related to the Russia-Ukraine conflict and applied the model, resulting in a sentiment analysis accuracy of 94.7%, outperforming current techniques….(More)”.

Citizens’ juries can help fix democracy


Article by Martin Wolf: “…our democratic processes do not work very well. Adding referendums to elections does not solve the problem. But adding citizens’ assemblies might.

In his farewell address, George Washington warned against the spirit of faction. He argued that the “alternate domination of one faction over another . . . is itself a frightful despotism. But . . . The disorders and miseries which result gradually incline the minds of men to seek security and repose in the absolute power of an individual”. If one looks at the US today, that peril is evident. In current electoral politics, manipulation of the emotions of a rationally ill-informed electorate is the path to power. The outcome is likely to be rule by those with the greatest talent for demagogy.

Elections are necessary. But unbridled majoritarianism is a disaster. A successful liberal democracy requires constraining institutions: independent oversight over elections, an independent judiciary and an independent bureaucracy. But are they enough? No. In my book, The Crisis of Democratic Capitalism, I follow the Australian economist Nicholas Gruen in arguing for the addition of citizens’ assemblies or citizens’ juries. These would insert an important element of ancient Greek democracy into the parliamentary tradition.

There are two arguments for introducing sortition (lottery) into the political process. First, these assemblies would be more representative than professional politicians can ever be. Second, it would temper the impact of political campaigning, nowadays made more distorting by the arts of advertising and the algorithms of social media…(More)”.

WHO Launches Global Infectious Disease Surveillance Network


Article by Shania Kennedy: “The World Health Organization (WHO) launched the International Pathogen Surveillance Network (IPSN), a public health network to prevent and detect infectious disease threats before they become epidemics or pandemics.

IPSN will rely on insights generated from pathogen genomics, which helps analyze the genetic material of viruses, bacteria, and other disease-causing micro-organisms to determine how they spread and how infectious or deadly they may be.

Using these data, researchers can identify and track diseases to improve outbreak prevention, response, and treatments.

“The goal of this new network is ambitious, but it can also play a vital role in health security: to give every country access to pathogen genomic sequencing and analytics as part of its public health system,” said WHO Director-General Tedros Adhanom Ghebreyesus, PhD, in the press release.  “As was so clearly demonstrated to us during the COVID-19 pandemic, the world is stronger when it stands together to fight shared health threats.”

Genomics capacity worldwide was scaled up during the pandemic, but the press release indicates that many countries still lack effective tools and systems for public health data collection and analysis. This lack of resources and funding could slow the development of a strong global health surveillance infrastructure, which IPSN aims to help address.

The network will bring together experts in genomics and data analytics to optimize routine disease surveillance, including for COVID-19. According to the press release, pathogen genomics-based analyses of the SARS-COV-2 virus helped speed the development of effective vaccines and the identification of more transmissible virus variants…(More)”.