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

Curated on Posted on by Stefaan Verhulst

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 Platform, Pinot, 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

Curated on Posted on by Stefaan Verhulst

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

Curated on Posted on by Stefaan Verhulst

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

Curated on Posted on by Stefaan Verhulst

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)”.

How AI could take over elections – and undermine democracy

Curated on Posted on by Stefaan Verhulst

Article by Archon Fung and Lawrence Lessig: “Could organizations use artificial intelligence language models such as ChatGPT to induce voters to behave in specific ways?

Sen. Josh Hawley asked OpenAI CEO Sam Altman this question in a May 16, 2023, U.S. Senate hearing on artificial intelligence. Altman replied that he was indeed concerned that some people might use language models to manipulate, persuade and engage in one-on-one interactions with voters.

Altman did not elaborate, but he might have had something like this scenario in mind. Imagine that soon, political technologists develop a machine called Clogger – a political campaign in a black box. Clogger relentlessly pursues just one objective: to maximize the chances that its candidate – the campaign that buys the services of Clogger Inc. – prevails in an election.

While platforms like Facebook, Twitter and YouTube use forms of AI to get users to spend more time on their sites, Clogger’s AI would have a different objective: to change people’s voting behavior.

As a political scientist and a legal scholar who study the intersection of technology and democracy, we believe that something like Clogger could use automation to dramatically increase the scale and potentially the effectiveness of behavior manipulation and microtargeting techniques that political campaigns have used since the early 2000s. Just as advertisers use your browsing and social media history to individually target commercial and political ads now, Clogger would pay attention to you – and hundreds of millions of other voters – individually.

It would offer three advances over the current state-of-the-art algorithmic behavior manipulation. First, its language model would generate messages — texts, social media and email, perhaps including images and videos — tailored to you personally. Whereas advertisers strategically place a relatively small number of ads, language models such as ChatGPT can generate countless unique messages for you personally – and millions for others – over the course of a campaign.

Second, Clogger would use a technique called reinforcement learning to generate a succession of messages that become increasingly more likely to change your vote. Reinforcement learning is a machine-learning, trial-and-error approach in which the computer takes actions and gets feedback about which work better in order to learn how to accomplish an objective. Machines that can play Go, Chess and many video games better than any human have used reinforcement learning.How reinforcement learning works.

Third, over the course of a campaign, Clogger’s messages could evolve in order to take into account your responses to the machine’s prior dispatches and what it has learned about changing others’ minds. Clogger would be able to carry on dynamic “conversations” with you – and millions of other people – over time. Clogger’s messages would be similar to ads that follow you across different websites and social media…(More)”.

How Differential Privacy Will Affect Estimates of Air Pollution Exposure and Disparities in the United States

Curated on Posted on by Stefaan Verhulst

Article by Madalsa Singh: “Census data is crucial to understand energy and environmental justice outcomes such as poor air quality which disproportionately impact people of color in the U.S. With the advent of sophisticated personal datasets and analysis, Census Bureau is considering adding top-down noise (differential privacy) and post-processing 2020 census data to reduce the risk of identification of individual respondents. Using 2010 demonstration census and pollution data, I find that compared to the original census, differentially private (DP) census significantly changes ambient pollution exposure in areas with sparse populations. White Americans have lowest variability, followed by Latinos, Asian, and Black Americans. DP underestimates pollution disparities for SO2 and PM2.5 while overestimates the pollution disparities for PM10…(More)”.

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

Curated on Posted on by Stefaan Verhulst

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)”

Governing the Unknown

Curated on Posted on by Stefaan Verhulst

Article by Kaushik Basu: “Technology is changing the world faster than policymakers can devise new ways to cope with it. As a result, societies are becoming polarized, inequality is rising, and authoritarian regimes and corporations are doctoring reality and undermining democracy.

For ordinary people, there is ample reason to be “a little bit scared,” as OpenAI CEO Sam Altman recently put it. Major advances in artificial intelligence raise concerns about education, work, warfare, and other risks that could destabilize civilization long before climate change does. To his credit, Altman is urging lawmakers to regulate his industry.

In confronting this challenge, we must keep two concerns in mind. The first is the need for speed. If we take too long, we may find ourselves closing the barn door after the horse has bolted. That is what happened with the 1968 Nuclear Non-Proliferation Treaty: It came 23 years too late. If we had managed to establish some minimal rules after World War II, the NPT’s ultimate goal of nuclear disarmament might have been achievable.

The other concern involves deep uncertainty. This is such a new world that even those working on AI do not know where their inventions will ultimately take us. A law enacted with the best intentions can still backfire. When America’s founders drafted the Second Amendment conferring the “right to keep and bear arms,” they could not have known how firearms technology would change in the future, thereby changing the very meaning of the word “arms.” Nor did they foresee how their descendants would fail to realize this even after seeing the change.

But uncertainty does not justify fatalism. Policymakers can still effectively govern the unknown as long as they keep certain broad considerations in mind. For example, one idea that came up during a recent Senate hearing was to create a licensing system whereby only select corporations would be permitted to work on AI.

This approach comes with some obvious risks of its own. Licensing can often be a step toward cronyism, so we would also need new laws to deter politicians from abusing the system. Moreover, slowing your country’s AI development with additional checks does not mean that others will adopt similar measures. In the worst case, you may find yourself facing adversaries wielding precisely the kind of malevolent tools that you eschewed. That is why AI is best regulated multilaterally, even if that is a tall order in today’s world…(More)”.

Filling Africa’s Data Gap

Curated on Posted on by Stefaan Verhulst

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)”.

Yes, No, Maybe? Legal & Ethical Considerations for Informed Consent in Data Sharing and Integration

Curated on Posted on by Stefaan Verhulst

Report by Deja Kemp, Amy Hawn Nelson, & Della Jenkins: “Data sharing and integration are increasingly commonplace at every level of government, as cross-program and cross-sector data provide valuable insights to inform resource allocation, guide program implementation, and evaluate policies. Data sharing, while routine, is not without risks, and clear legal frameworks for data sharing are essential to mitigate those risks, protect privacy, and guide responsible data use. In some cases, federal privacy laws offer clear consent requirements and outline explicit exceptions where consent is not required to share data. In other cases, the law is unclear or silent regarding whether consent is needed for data sharing. Importantly, consent can present both ethical and logistical challenges, particularly when integrating cross-sector data. This brief will frame out key concepts related to consent; explore major federal laws governing the sharing of administrative data, including individually identifiable information; and examine important ethical implications of consent, particularly in cases when the law is silent or unclear. Finally, this brief will outline the foundational role of strong governance and consent frameworks in ensuring ethical data use and offer technical alternatives to consent that may be appropriate for certain data uses….(More)”.