Data Innovation in Demography, Migration and Human Mobility

Curated on Posted on by Stefaan Verhulst

Report by Bosco, C., Grubanov-Boskovic, S., Iacus, S., Minora, U., Sermi, F. and Spyratos, S.: “With the consolidation of the culture of evidence-based policymaking, the availability of data has become central for policymakers. Nowadays, innovative data sources have offered opportunity to describe more accurately demographic, mobility- and migration- related phenomena by making available large volumes of real-time and spatially detailed data. At the same time, however, data innovation has brought up new challenges (ethics, privacy, data governance models, data quality) for citizens, statistical offices, policymakers and the private sector.

Focusing on the fields of demography, mobility and migration studies, the aim of this report is to assess the current state of utilisation of data innovation in the scientific literature as well as to identify areas in which data innovation has the most concrete potential for policymaking. For that purpose, this study has reviewed more than 300 articles and scientific reports, as well as numerous tools, that employed non-traditional data sources for demographic, human mobility or migration research.The specific findings of our report contribute to a discussion on a) how innovative data is used in respect to traditional data sources; b) domains in which innovative data have the highest potential to contribute to policymaking; c) prospects for an innovative data transition towards systematic contribution to official statistics and policymaking…(More)”. See also Big Data for Migration Alliance.

Using social media data to ‘nowcast’ migration around the globe

Curated on Posted on by Stefaan Verhulst

Report by RAND: “In recent years, unprecedented waves of refugees, economic migrants and people displaced by a variety of factors have made migration a high-priority policy issue around the world. Despite this, official migration statistics often come with a time lag and can fail to correctly capture the full extent of migration, leaving decision makers without timely and robust data to make informed policy decisions.

In a RAND-initiated, self-funded research study, we developed a methodological tool to compute near real-time migration estimates for European Union member states and the United States. The tool, underpinned by a Bayesian model, is capable of providing ‘nowcasts’ of migrant stocks by combining real-time data from the Facebook Marketing Application Programming Interface and data from official migration sources, such as Eurostat and the US Census Bureau.

These nowcasts can serve as an early-warning system to anticipate ‘shock events’ and rapid migration trends that would otherwise be captured too late or not at all by official migration data sources. The tool could therefore enable decision makers to make informed, evidence-based policy decisions in the rapidly changing social policy sphere of international migration.

The study also provides a useful example of how to combine ‘big data’ with traditional data to improve measurement and estimation which can be applied to other social and demographic phenomena…(More)”.

Strengthening CRVS Systems to Improve Migration Policy: A Promising Innovation

Curated on Posted on by Stefaan Verhulst

Blog by Tawheeda Wahabzada and Deirdre Appel: “Migration is one of the most pressing issues of our time and innovation for migration policy can take on several different shapes to help solve challenges. It is seen through radical technological breakthrough such as biometric identifiers that completely transform the status quo as well as technological disruptions like mobile phone fund transforms that alter an existing process. There is also incremental innovation, or the gradual improvement of an existing process or institution even. Regardless of where the fall on the spectrum, their innovative applications are all relevant to migration policy.

Incremental innovation for civil registration and vital statistics (CRVS) systems can greatly benefit migrants and the policymakers trying to help them. According to World Health Organization, a well-functioning CRVS system registers all births and deaths, issues birth and death certificates, and compiles and disseminates vital statistics, including cause of death information. It may also record marriages and divorces. Each of these services brings a world of crucial advantages. But despite the social and legal benefits for individuals, especially migrants, these systems remain underfunded and under functioning. More than 100 low and middle-income countries lack functional CRVS systems and about one-third of all births are not registered. This amounts to more than one billion people without a legal identity leaving them unable to prove who they are and creating serious barriers to access health, education, financial, and other social services.

Throughout countries in Africa, there are great differences in CRVS coverage, where birth coverage ranges from above 90 percent in some North African countries to under 50 percent across several countries in different regions; and with death registration having greater gaps with either no information or lower coverage rates. For countries with low functioning CRVS systems, potential migrants from these countries could face additional obstacles in obtaining birth certificates and proof of identification….(More)”. See also https://data4migration.org/blog/

Automating Decision-making in Migration Policy: A Navigation Guide

Curated on Posted on by Stefaan Verhulst

Report by Astrid Ziebarth and Jessica Bither: “Algorithmic-driven or automated decision-making models (ADM) and programs are increasingly used by public administrations to assist human decision-making processes in public policy—including migration and refugee policy. These systems are often presented as a neutral, technological fix to make policy and systems more efficient. However, migration policymakers and stakeholders often do not understand exactly how these systems operate. As a result, the implications of adopting ADM technology are still unclear, and sometimes not considered. In fact, automated decision-making systems are never neutral, nor is their employment inevitable. To make sense of their function and decide whether or how to use them in migration policy will require consideration of the specific context in which ADM systems are being employed.

Three concrete use cases at core nodes of migration policy in which automated decision-making is already either being developed or tested are examined: visa application processes, placement matching to improve integration outcomes, and forecasting models to assist for planning and preparedness related to human mobility or displacement. All cases raise the same categories of questions: from the data employed, to the motivation behind using a given system, to the action triggered by models. The nuances of each case demonstrate why it is crucial to understand these systems within a bigger socio-technological context and provide categories and questions that can help policymakers understand the most important implications of any new system, including both technical consideration (related to accuracy, data questions, or bias) as well as contextual questions (what are we optimizing for?).

Stakeholders working in the migration and refugee policy space must make more direct links to current discussions surrounding governance, regulation of AI, and digital rights more broadly. We suggest some first points of entry toward this goal. Specifically, for next steps stakeholders should:

  1. Bridge migration policy with developments in digital rights and tech regulation
  2. Adapt emerging policy tools on ADM to migration space
  3. Create new spaces for exchange between migration policymakers, tech regulators, technologists, and civil society
  4. Include discussion on the use of ADM systems in international migration fora
  5. Increase the number of technologists or bilinguals working in migration policy
  6. Link tech and migration policy to bigger questions of foreign policy and geopolitics…(More)”.

Designing data collaboratives to better understand human mobility and migration in West Africa

Curated on Posted on by Stefaan Verhulst


“The Big Data for Migration Alliance (BD4M) is released the report, “Designing Data Collaboratives to Better Understand Human Mobility and Migration in West Africa,” providing findings from a first-of-its-kind rapid co-design and prototyping workshop, or “Studio.” The first BD4M Studio convened over 40 stakeholders in government, international organizations, research, civil society, and the public sector to develop concrete strategies for developing and implementing cross- sectoral data partnerships, or “data collaboratives,” to improve ethical and secure access to data for migration-related policymaking and research in West Africa.

BD4M is an effort spearheaded by the International Organization for Migration’s Global Migration Data Analysis Centre (IOM GMDAC), European Commission’s Joint Research Centre (JRC), and The GovLab to accelerate the responsible and ethical use of novel data sources and methodologies—such as social media, mobile phone data, satellite imagery, artificial intelligence—to support migration-related programming and policy on the global, national, and local levels. 

The BD4M Studio was informed by The Migration Domain of The 100 Questions Initiative — a global agenda-setting exercise to define the most impactful questions related to migration that could be answered through data collaboration. Inspired by the outputs of The 100 Questions, Studio participants designed data collaboratives that could produce answers to three key questions: 

  1. How can data be used to estimate current cross-border migration and mobility by sex and age in West Africa?
  2.  How can data be used to assess the current state of diaspora communities and their migration behavior in the region?
  3. How can we use data to better understand the drivers of migration in West Africa?…(More)”

Artificial Intelligence in Migration: Its Positive and Negative Implications

Curated on Posted on by Stefaan Verhulst

Article by Priya Dialani: “Research and development in new technologies for migration management are rapidly increasing. To quote certain migration examples, big data was used to predict population movements in the Mediterranean, AI lie detectors used at the European border, and the recent one is the government of Canada using automated decision-making in immigration and refugee applications. Artificial intelligence in migration is helping countries to manage international migration.

Every corner of the world is encountering an unprecedented number of challenging migration crises. As an increasing number of people are interacting with immigration and refugee determination systems, nations are taking a stab at artificial intelligence. AI in global immigration is helping countries to automate a plethora of decisions that are made almost daily as people want to cross borders and look for new homes.

AI projects in migration management can help in predicting the next migration crisis with better accuracy. Artificial intelligence can predict the movements of people migrating by taking into account different types of data such as WiFi positioning, Google Trends, etc. This data can further help the nations and government to be prepared more efficiently for mass migration. Governments can use AI algorithms to examine huge datasets and look for potential gaps in their reception facilities such as the absence of appropriate places for people or vulnerable unaccompanied children.

Recognizing such gaps can allow the government to alter their reception conditions as well as be prepared to comply with their legal obligations under international human rights law (IHRL).

AI applications can also help in changing the lives of asylum seekers and refugees. AI machine learning and optimized algorithms are helping in improving refugee integration. Annie MOORE (Matching Outcome Optimization for Refugee Empowerment) is one such project that matches refugees to communities where they can find the resources and environment as per their preferences and needs.

Asylum seekers or refugees most of the time lack access to lawyers and legal advice. A UK-based chatbot DoNotPay provides free legal advice to asylum seekers using intelligent algorithms. It also provides personalized legal support, which includes help through the UK asylum application process.

AI tech is not just helpful to the government but also to international organisations taking care of international migration. Some organizations are already leveraging machine learning in association with biometric technology. IOM has introduced the Big Data for Migration Alliance project, which intends to use different technologies in international migration….(More)”.

Digital Identity, Virtual Borders and Social Media: A Panacea for Migration Governance?

Curated on Posted on by Stefaan Verhulst

Book edited by Emre Eren Korkmaz: “…discusses how states deploy frontier and digital technologies to manage and control migratory movements. Assessing the development of blockchain technologies for digital identities and cash transfer; artificial intelligence for smart borders, resettlement of refugees and assessing asylum applications; social media and mobile phone applications to track and surveil migrants, it critically examines the consequences of new technological developments and evaluates their impact on the rights of migrants and refugees.

Chapters evaluate the technology-based public-private projects that govern migration globally and illustrate the political implications of these virtual borders. International contributors compare and contrast different forms of political expression, in both personal technologies, such as social media for refugees and smugglers, and automated decision-making algorithms used by states to enable migration governance. This timely book challenges hegemonic approach to migration governance and provides cases demonstrating the dangers of employing frontier technologies denying basic rights, liberties and agencies of migrants and refugees.

Stepping into a contentious political climate for migrants and refugees, this provocative book is ideal reading for scholars and researchers of political science and public policy, particularly those focusing on migration and refugee studies. It will also benefit policymakers and practitioners dealing with migration, such as humanitarian NGOs, UN agencies and local authorities….(More)”.

Leave No Migrant Behind: The 2030 Agenda and Data Disaggregation

Curated on Posted on by Stefaan Verhulst

Guide by the International Organization for Migration (IOM): “To date, disaggregation of global development data by migratory status remains low. Migrants are largely invisible in official SDG data. As the global community approaches 2030, very little is known about the impact of the 2030 Agenda on migrants. Despite a growing focus worldwide on data disaggregation, namely the breaking down of data into smaller sub-categories, there is a lack of practical guidance on the topic that can be tailored to address individual needs and capacities of countries.

Developed by IOM’s Global Migration Data Analysis Centre (GMDAC), the guide titled ‘Leave No Migrant Behind: The 2030 Agenda and Data Disaggregation‘ centres on nine SDGs focusing on hunger, education, and gender equality among others. The document is the first of its kind, in that it seeks to address a range of different categorization interests and needs related to international migrants and suggests practical steps that practitioners can tailor to best fit their context…The guide also highlights the key role disaggregation plays in understanding the many positive links between migration and the SDGs, highlighting migrants’ contributions to the 2030 Agenda.

The guide outlines key steps for actors to plan and implement initiatives by looking at sex, gender, age and disability, in addition to migratory status. These steps include undertaking awareness raising, identifying priority indicators, conducting data mapping, and more….Read more about the importance of data disaggregation for SDG indicators here….(More)”

Liberation Technology

Curated on Posted on by Stefaan Verhulst

Tim Keary at the Stanford Social Innovation Review: “Human traffickers have forced hundreds of women, children, and men into sexual slavery in Colombia during the past decade. According to Colombia’s Ministry of the Interior and Justice, 686 cases of human trafficking occurred within the country from January 2013 to July 2020. Many of those seized were women, children, and Venezuelan migrants.

To combat this crime, Migración Colombia, the nation’s border control agency; the US Bureau of Population, Refugees, and Migration (PRM); and the International Organization for Migration (IOM) launched a mobile application called LibertApp last July. Pressing the app’s panic button immediately sends the user’s live geolocation data to the Colombian Ministry of the Interior’s Anti-Human Trafficking Operations Center (COAT), where an expert anti-trafficking team investigates the report.

The app also functions as a resource hub for information and prevention. It offers an educational module (available in both English and Spanish) that explains what human trafficking is, who is the most at risk, and the most common strategies that traffickers use to isolate and exploit victims. LibertApp also includes a global directory of consulates’ contact information that users can access for support.

While COAT and Migración Colombia now manage the app, IOM, an international organization that supports migrant communities and advises national governments on migration policy, developed the original concept, provided technical support, created user profiles, and built the educational module. IOM saw LibertApp as a new tool to support high-risk groups such as Venezuelan migrants and refugees. “It is necessary to permanently search for different strategies for the prevention of trafficking” and to ensure the “rescue of victims who are in Colombia or abroad,” says Ana Durán-Salvatierra, IOM Colombia’s chief of mission….

PRM funded the app, which had a budget of $15,000. The investment was part of the department’s overall contribution through the United Nations appeal known as the Refugee and Migrant Response Plan, a global initiative that had granted a total of $276.4 million to Colombia as of November 2020.

In less than a year of operation, 246 people have used the app to make reports, culminating in a handful of investigations and rescues. The most notable success story occurred last summer when COAT received a report from LibertApp that led to the rescue of a Venezuelan minor from a bar in Maní, in the Casanare region of Colombia, that was being run as a brothel. During the raid, authorities captured two Colombian citizens alleged to have managed the establishment and who coerced 15 women into sexual slavery….(More)”

The Cruel New Era of Data-Driven Deportation

Curated on Posted on by Stefaan Verhulst

Article by Alvaro M. Bedoya: “For a long time, mass deportations were a small-data affair, driven by tips, one-off investigations, or animus-driven hunches. But beginning under George W. Bush, and expanding under Barack Obama, ICE leadership started to reap the benefits of Big Data. The centerpiece of that shift was the “Secure Communities” program, which gathered the fingerprints of arrestees at local and state jails across the nation and compared them with immigration records. That program quickly became a major driver for interior deportations. But ICE wanted more data. The agency had long tapped into driver address records through law enforcement networks. Eyeing the breadth of DMV databases, agents began to ask state officials to run face recognition searches on driver photos against the photos of undocumented people. In Utah, for example, ICE officers requested hundreds of face searches starting in late 2015. Many immigrants avoid contact with any government agency, even the DMV, but they can’t go without heat, electricity, or water; ICE aimed to find them, too. So, that same year, ICE paid for access to a private database that includes the addresses of customers from 80 national and regional electric, cable, gas, and telephone companies.

Amid this bonanza, at least, the Obama administration still acknowledged red lines. Some data were too invasive, some uses too immoral. Under Donald Trump, these limits fell away.

In 2017, breaking with prior practice, ICE started to use data from interviews with scared, detained kids and their relatives to find and arrest more than 500 sponsors who stepped forward to take in the children. At the same time, ICE announced a plan for a social media monitoring program that would use artificial intelligence to automatically flag 10,000 people per month for deportation investigations. (It was scuttled only when computer scientists helpfully indicated that the proposed system was impossible.) The next year, ICE secured access to 5 billion license plate scans from public parking lots and roadways, a hoard that tracks the drives of 60 percent of Americans—an initiative blocked by Department of Homeland Security leadership four years earlier. In August, the agency cut a deal with Clearview AI, whose technology identifies people by comparing their faces not to millions of driver photos, but to 3 billion images from social media and other sites. This is a new era of immigrant surveillance: ICE has transformed from an agency that tracks some people sometimes to an agency that can track anyone at any time….(More)”.