Page 26 - IJPS-11-6

International Journal of
Population Studies Migration data management insights

and distributing resources appropriately. Nonetheless, While deductive reasoning relies on structured logic
there is frequently a deficiency in the availability of data for reliability, it lacks flexibility in complex scenarios
concerning migration and mobility, and the data that are compared to inductive reasoning, which leverages data
accessible often do not arrive in a timely fashion. Despite to foster innovation. However, researchers must carefully
the existence of such sources, the available data may not manage the uncertainties it introduces. Both approaches
possess the necessary level of detail to analyze migration play crucial roles in AI development and application.
patterns effectively, or it may not be disseminated in a It has been found that AI algorithms can use extensive
sufficiently prompt manner to track fluctuations in these datasets, particularly big data, characterized by its high
trends (Alexander et al., 2022). velocity and complexity, in order to identify patterns
Migration has emerged as a critical area of study in the and predict future behaviors (Burrell, 2016), which can
contemporary global landscape, driven by various factors be particularly beneficial for forecasting and managing
such as economic disparity, conflict, climate change, and migration patterns (Nyoni, 2017; IOM, 2018).
globalization. The increasing complexity of migration Moreover, AI technologies present significant
patterns necessitates robust data collection and analysis opportunities for state authorities to enhance their
to inform policy-making and humanitarian efforts. In preparedness for large-scale migrations. By leveraging
this context, new opportunities in migration data research sophisticated algorithms, decision-makers can analyze
have arisen, particularly through the integration of vast datasets to predict and manage the arrival of
advanced technologies such as AI and big data analytics. individuals seeking refuge or relocation (Beduschi, 2021).
Furthermore, the use of other data sources, such as social This predictive capability allows governments to identify
media (Facebook and Twitter) and mobile phone records, potential deficiencies in their reception infrastructure,
represents an opportunity for migration studies to address
the lack of information that impedes this field (Hughes thereby facilitating a more organized response.
et al., 2016; Tjaden, 2021). A pioneering application of AI was developed by
the Hong Kong Immigration Department in 2007 as
Historically, scholars have relied on census records,
administrative data, and household surveys (Meyer part of the eBrain program. An array of AI technologies
et al., 2015; Roos et al., 2017), but these sources often lack was utilized in this project to enhance the efficiency of
timeliness, fail to capture entire migrant populations, and administrative processes, including the processing of visa
applications, travel documents, identity cards, and work
suffer from quality issues, necessitating periodic updates
for effective policy-making. While traditional data has permits. The implementation of AI significantly improved
declined in prominence, advancements in computational the quality of services offered to both residents and tourists
power have spurred a data revolution, with digital traces, in Hong Kong. This advancement is achieved by efficiently
optimizing application processes and reducing the time
such as georeferenced information from Twitter (tracking
migrant locations) and Facebook (revealing demographics required for completion (Wong & Chun, 2007). Toward
like nationality, gender, age, and occupation), emerging the end of 2017, the United Nations introduced the Unite
as alternative sources. However, digital trace data differs Ideas Internal Displacement Event Tagging and Extraction
fundamentally from conventional datasets, offering real- Clustering Tool (United Nations, 2017). The objective was
time insights despite not adhering to traditional definitions. to develop tools for tracking and analyzing individuals
who have been displaced from their homes due to
4.1. AI conflict or disaster. The data for democracy team emerged
In certain nations, the implementation of algorithmic victorious by creating a tool that effectively monitors and
decision-making in the realm of immigration and asylum analyzes data related to refugees and others who have been
compelled to leave their residences.
processes has transitioned from theoretical discourse to
practical application. Notably, Canada has adopted such Bircan & Korkmaz (2021) argue that the use of big data
methodologies to inform its immigration and asylum analytics and AI technology is promoted for its ability to
adjudications (CILA, 2023). Similarly, Germany has help resolve current and emerging challenges in the social,
engaged in pilot projects that leverage technological economic, and governmental realms. Effectively leveraging
advancements, employing facial recognition and dialect big data and AI in the realm of migration governance
analysis to facilitate the determination of asylum claims. necessitates significantly enhanced cooperation among
In AI, reasoning can be deductive or inductive, with migrants, which includes civil society and grassroots
inductive reasoning often driving research but sometimes organizations that advocate for them, data scientists,
yielding uncertain outcomes (Khan & Ullah, 2010). migration researchers, and policymakers.

Volume 11 Issue 6 (2025) 20 https://doi.org/10.36922/ijps.6846