AI ML Asylum Understanding the Intersection of Technology and Humanitarian Law

The intersection of AI, ML, and humanitarian law is a complex and rapidly evolving field. This convergence has significant implications for asylum seekers and refugees.

In recent years, AI-powered tools have been increasingly used to analyze asylum claims, with some systems claiming to be able to detect deception with high accuracy. However, these systems often rely on biased data and may perpetuate existing inequalities.

The use of AI in asylum processing raises concerns about accountability and transparency. For instance, a study found that AI-powered decision-making systems can be opaque and difficult to understand, making it challenging to identify and address errors.

As a result, it's essential to develop and implement AI systems that are fair, transparent, and accountable, ensuring that they align with humanitarian law principles.

About 35 million displaced people are recognized as refugees, nearly half of whom are under the age of 18.

The resettlement process is rich with empirical questions, but placement officers have historically relied on experience and intuition rather than data.

By harnessing existing data in management and administrative systems, researchers were able to create an algorithm that matches refugees with the communities where they're most likely to thrive.

The algorithm increased projected employment by roughly 40% in the U.S. and 75% in Switzerland.

Immigration Law plays a crucial role in the intersection of immigration and AI. The US Citizenship Act of 2021, for instance, proposes a pathway to citizenship for undocumented immigrants who arrived in the US as children, known as Dreamers.

The proposed Act also aims to increase the number of visas available for highly skilled workers, which could have implications for AI development and deployment in the US. This is because AI requires a large pool of skilled workers to design, train, and maintain these systems.

The US immigration system is complex and often slow, which can lead to frustration and anxiety for immigrants. For example, the processing time for an H-1B visa, which is commonly used by tech companies to hire foreign workers, can take several months or even years.

AI can help streamline some aspects of the immigration process, such as document verification and language translation. For instance, AI-powered chatbots can assist with answering common questions and providing general information to immigrants.

However, the use of AI in immigration law raises concerns about bias and discrimination. For example, if AI algorithms are trained on biased data, they may perpetuate existing inequalities in the immigration system.

The US government has taken steps to address these concerns, such as the development of guidelines for the use of AI in immigration decision-making. These guidelines emphasize the importance of transparency, accountability, and fairness in the use of AI.

The intersection of immigration law and AI is a rapidly evolving field, and it will be interesting to see how these developments unfold in the coming years.

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Partnerships are crucial in the field of immigration and AI. Many organizations are working together to develop more efficient and effective immigration systems.

The International Organization for Migration (IOM) has partnered with various governments and tech companies to develop AI-powered tools for migration management. These tools help to streamline the process of visa applications and border control.

One notable example is the IOM's partnership with the government of Australia to develop an AI-powered visa application system. This system uses machine learning algorithms to quickly and accurately assess visa applications.

The use of AI in immigration systems can help to reduce processing times and increase the accuracy of decisions. This can lead to a more efficient and effective immigration process for both applicants and governments.

The IOM's partnership with the government of Australia has resulted in a significant reduction in processing times for visa applications. The system is now able to process applications in a matter of minutes, rather than days or weeks.

The use of AI in immigration systems also raises important questions about data protection and privacy. It is essential that organizations working in this field prioritize the security and confidentiality of sensitive data.

The webinar offers a unique opportunity to learn about AI/ML applications in the humanitarian sector.

The webinar focuses on two practical implementations of AI/ML in the humanitarian sector.

Danish Refugee Council (DRC) is using AI/ML to forecast forced displacement in countries including Afghanistan, Myanmar, and West Africa.

The Foresight tool uses open data from sources like UNHCR, the World Bank, and NGO agencies to predict forced displacement over the next one to three years.

This tool is used for strategic planning and operational preparedness within DRC and the wider humanitarian sector.

The International Rescue Committee (IRC) is using AI/ML to facilitate jobs matching for Syrian refugees in Jordan.

Project Match uses an adapted Thompson’s Algorithm to target different job seekers with the most impactful interventions.

The matching algorithm, combined with cash support, increased employment by 7.9%, or 143 percentage points, compared to no intervention.

The algorithm itself was responsible for 6.2%, or 111 percentage points, of that gain.

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AI systems have been adopted for refugee placement and screening, with GeoMatch, a machine learning tool, increasing projected employment by 40% in the U.S. and 75% in Switzerland.

Over 100 million people worldwide have been forced to flee their homes, and AI has the potential to dramatically improve millions of lives.

The use of AI systems in refugee forecasting and screening raises ethical and legal concerns, including inaccuracy, bias, and a lack of transparency or interpretability affecting refugee rights.

AI has the potential to dramatically improve the lives of millions of people, like the 35 million recognized refugees worldwide, nearly half of whom are under the age of 18.

The issue of refugee resettlement is a complex one, with placement officers relying on experience and intuition more than data to make decisions.

A team of researchers at Stanford developed an algorithm that matches refugees with the communities where they're most likely to thrive, using individual background characteristics and employment success rates as benchmarks.

The algorithm increased projected employment by roughly 40% in the U.S. and 75% in Switzerland, showing the potential for AI to make a real difference.

More than 100 million people have been forced to flee their homes, highlighting the need for effective solutions like the GeoMatch algorithm.

The GeoMatch team is working hard to build guardrails around their algorithm to prevent potential harms. Nobody at GeoMatch believes that the algorithm should operate without human oversight.

Placement officers will remain as final decision-makers when considering where refugees land, and suggestions from GeoMatch are intended to support and improve these decisions.

The team has also proactively engaged with concerns around bias, with Elisabeth Paulson crafting a modified set of algorithms to allow resettlement agencies to carefully examine results like employment across distinct subgroups.

If one agency wants to make sure specific employment thresholds are achieved based on a refugee's country of origin, this can be set as the desired outcome. Another agency could instead tune the algorithm to look at employment rates based on gender.

The goal is to maximize overall employment without accidentally harming one subgroup. This is a crucial step in ensuring fairness in the algorithm.

Rothenhaeusler was recruited to the GeoMatch team because of his expertise in "distribution shifts", which is the fact that machine learning models are trained on historical data creates unseen problems when present-day circumstances don't mirror historical circumstances.

Looking for employment today in the Bay Area is a different endeavor than it was two years ago, and asylum seekers showing up in Europe eight years ago are different in important ways from the Ukrainians seeking asylum there today.

The GeoMatch team is building resilience against these kinds of changes into their algorithm. This will help ensure that the algorithm remains effective even as circumstances change.

The team hopes to increase the number of countries it partners with, including work ongoing in the Netherlands, and the populations it serves.