Automating Decision-making in Migration Policy: A Navigation Guide


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



“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


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?


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


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


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


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)”.

Global collaboration on human migration launches digital hub


Press Release: “The International Organization for Migration (IOM) and the Joint Research Centre (JRC) of the European Commission joined forces with The Governance Lab (The GovLab) at the NYU Tandon School of Engineering to launch an online home for the Big Data for Migration (BD4M) Alliance, the first-ever global network dedicated to facilitating responsible data innovation and collaboration for informed decision making on migration and human mobility.

We live in a fast-moving world where a huge amount of data is being generated by the private sector but public-private data partnerships still remain limited. The BD4M, convened in 2018 by the European Commission’s Knowledge Centre on Migration and Demography (KCMD) and the IOM’s Global Migration Data Analysis Centre (GMDAC), seeks to foster more cooperation in this area by connecting stakeholders and leveraging non-traditional data sources to improve understanding.

The new BD4M web page, www.data4migration.org, hosted by the GovLab, serves as a hub for the Alliance’s activities. It aims to inform stakeholders about the BD4M members, its objectives, ongoing projects, upcoming events and opportunities for collaboration.

To facilitate access to knowledge about how data innovation has contributed to informing migration policy and programs, for example, the BD4M recently launched the Data Innovation Directory, which features examples of applications of new data sources and methodologies in the field of migration and human mobility.

The BD4M is open to members of international organizations, NGOs, the private sector, researchers and individual experts. In its partnership with The GovLab, the BD4M has helped identify a set of priority questions on migration that new data sources could contribute to answering. These questions were formulated by experts and validated through a public voting campaign as part of The 100 Questions Initiative….(More)”.

Human migration: the big data perspective


Alina Sîrbu et al at the International Journal of Data Science and Analytics: “How can big data help to understand the migration phenomenon? In this paper, we try to answer this question through an analysis of various phases of migration, comparing traditional and novel data sources and models at each phase. We concentrate on three phases of migration, at each phase describing the state of the art and recent developments and ideas. The first phase includes the journey, and we study migration flows and stocks, providing examples where big data can have an impact. The second phase discusses the stay, i.e. migrant integration in the destination country. We explore various data sets and models that can be used to quantify and understand migrant integration, with the final aim of providing the basis for the construction of a novel multi-level integration index. The last phase is related to the effects of migration on the source countries and the return of migrants….(More)”.

The human rights impacts of migration control technologies


Petra Molnar at EDRI: “At the start of this new decade, over 70 million people have been forced to move due to conflict, instability, environmental factors, and economic reasons. As a response to the increased migration into the European Union, many states are looking into various technological experiments to strengthen border enforcement and manage migration. These experiments range from Big Data predictions about population movements in the Mediterranean to automated decision-making in immigration applications and Artificial Intelligence (AI) lie detectors at European borders. However, often these technological experiments do not consider the profound human rights ramifications and real impacts on human lives

A human laboratory of high risk experiments

Technologies of migration management operate in a global context. They reinforce institutions, cultures, policies and laws, and exacerbate the gap between the public and the private sector, where the power to design and deploy innovation comes at the expense of oversight and accountability. Technologies have the power to shape democracy and influence elections, through which they can reinforce the politics of exclusion. The development of technology also reinforces power asymmetries between countries and influence our thinking around which countries can push for innovation, while other spaces like conflict zones and refugee camps become sites of experimentation. The development of technology is not inherently democratic and issues of informed consent and right of refusal are particularly important to think about in humanitarian and forced migration contexts. For example, under the justification of efficiency, refugees in Jordan have their irises scanned in order to receive their weekly rations. Some refugees in the Azraq camp have reported feeling like they did not have the option to refuse to have their irises scanned, because if they did not participate, they would not get food. This is not free and informed consent….(More)”.