In|Between Frontiers

Habitizing the Border through AI

Jutang Gao + Chandana Rao + Ranya Betts-Chen

The project “In Between | Frontiers : Habitizing the border through AI” places the U.S.-Mexico border at the center of speculation and tries to address the long, ongoing debate on what the present-day connotation of the term “border” really means. Politics in the United States revolves around building a physical “wall” on the U.S.-Mexico border whose borderline has shifted for nearly 100 years before settling where it currently sits on a map in 1848. These divisive black lines called borders, breed animosity between nationalities, religions, and cultures and take away from the identity of a democratic and liberal nation. The U.S.-Mexico border is a tool for political divisiveness—an exclusionary approach that has led to the justification of xenophobia. The border not just divides but is mainly a symbol of inequality. Inequality in terms of infrastructure, economy, and environmental systems. In a situation of justified biases, we want to examine the role of artificial intelligence (AI) in resolving and addressing some of these sociopolitical issues that have traditionally always been humanistic in nature. We want to place AI in a position of neutrality in examining some of the current conditions at the site of margin. While the term neutral or bias-free may be utopic in nature, we want to push the understanding of AI in solving larger political problems with a humanistic and almost bias-free approach. The project will focus on equitable housing along the border, with a concentrated focus on the equitable procurement of water as a resource. The project aims to shed light on the prowess of AI and its intersection with social and community-driven architecture.

The project In|Between Frontiers aims to test the application of AI in the socio-political realm where interventions, while dire, haven’t taken shape due to many extraneous factors. The project aims to address topics of inclusive data and human-AI interaction by placing AI at the center of one such socio-political issue, the US-Mexico border. While the extents of interventions are multifold, this project aims to address the issues of migration along the border.

The Juarez-El Paso region, also known as the Borderplex, is a vibrant and dynamic transborder agglomeration located along the border between Mexico and the United States. It is centered on two major cities: Ciudad Juarez, which is located in the state of Chihuahua, Mexico, and El Paso, which is located in the state of Texas, United States. The region is known for its unique cultural blend, as it shares a rich history and heritage that transcends the international boundary.

The Juarez-El Paso region has a significant population, with over 2.7 million people, making it the second largest conurbation (urban area formed by the merging of two or more cities) on the U.S.-Mexico border, with San Diego-Tijuana being the largest. The region has a diverse population, with a mix of cultures, languages, and traditions, including a significant Hispanic population.

More than 2,500 people arrive in El Paso each day. With the shelters at capacities, migrant camps offering a below-par standard of living, and Texas having fallen short on affordable housing, the question now is where do these people go?

The project aims at addressing this dire need for housing through AI.

With the goal in mind to use AI to reimagine the land, not as a border, but as a place to live, we had to carefully craft a process, a methodology that we could use to help us envision this possibility. The process we have cultivated serves as a tool to make prototypes and speed up the initial stages of the design process.

Overall, using a methodology that incorporates AI into the design process will save architects and designers a lot of time when working on large-scale projects and can add value to our work. This is what we have seen while implementing our method of design.

While using AI in the design process is not all-encompassing, it is a point of departure.

For more information about this project, please visit

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