UBCO Professor Wendy Wong is one of two professors who will discuss the government’s role in the ever-changing digital era at a public talk Friday.

As artificial intelligence starts acting more human, could it change the way governments understand their relationships with citizens?

This is one of many questions up for discussion on Friday night, says Dr. Wendy H. Wong, a Professor of Political Science in UBC Okanagan’s Irving K. Barber Faculty of Arts and Social Sciences.

Dr. Wong is a world-renowned author and researcher who has organized a public talk called Virtual Realities: States and Territory in the Digital Era. On Friday, she and fellow AI researcher Dr. Louise Amoore will discuss government’s role in the digital era.

Why are you hosting this event?

I’m the founder of the Governance of Emerging Technologies group, which is primarily a collection of scholars who’ve been brought together through a shared interest in the political and social impacts of technology, and how those impacts might be managed.

We understand that the public has an important stake in these issues as well as scholars, so this event is about creating a space where we can all have honest conversations about the impacts of technology.

What is the goal of this event?

Our goal is to educate the public on how the logics created by deep learning technologies—like ChatGPT and other forms of artificial intelligence—impact the way governments are able to understand themselves and their relationships to citizens. For example, what are the social benefits and costs of either “leading” or “falling behind” on AI?

Can you tell us about the evening’s guest speaker Dr. Louise Amoore?

Dr. Louise Amoore is a widely celebrated scholar of political geography from Durham University in the United Kingdom. Dr. Amoore has published several books on technology and algorithms, most recently one called Cloud Ethics. Her work is wide-ranging and showcases how changing technological landscapes can impact our political futures.

What can people expect?

Guests can expect a brief, publicly oriented lecture by Dr. Amoore, followed by a conversation between myself and Dr. Amoore. I will ask exploratory questions that follow up on her lecture. I expect Dr. Amoore to explain how AI changes the way that governments think about themselves in relation to their citizens.

This shift in the “logic” of sovereignty means that there is potential for states to change the way they govern. How do existing rules apply when the logic of AI, which runs on extensive data collection and high levels of computing power that run sophisticated algorithms, becomes part of what government does?

Are states more or less powerful as a result?

As this talk is designed to be open-ended and broad, guests can also expect discussions on AI-related topics like data and data collection. Following the presentation, there will be a Q&A session.

Who is welcome to attend this event?

Thanks to sponsors, this event is free and open to everyone. Due to space restrictions, pre-registration is required.

The event takes place, Friday, May 5 at 5 pm at the Kelowna Innovation Centre, at 460 Doyle Ave. To register or find out more, visit: events.ok.ubc.ca/event/virtual-realities-states-and-territory-in-the-digital-era

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New UBCO research can help decision-makers in the real estate industry forecast future regional house prices and better explain pricing.

A group of UBC Okanagan researchers is trying to take the mathematical mystery out of what could be a person’s biggest investment—buying a home.

While the real estate market changes rapidly and is connected to the fluctuations of the economy, there are many other considerations to make when purchasing a property, says UBCO School of Engineering Professor Zheng Liu.

Dr. Liu and his doctoral student Junchi Bin, along with Faculty of Management Associate Professor Eric Li, have created a regional house price mining and forecasting framework (RHPMF) and recently published research that tests the tool they created.

“Real estate is always one of the largest expenses throughout a person’s life,” says Dr. Liu. “Before making decisions on house transactions, people consult real estate agents to obtain knowledge of the market. And these days, people are more cautious than ever about costly failures such as a real estate investment.”

The idea behind the RHPMF is to help people understand the population, growth and historical background of a specific community or even a neighbourhood based on real-world housing data including history, social dynamics and housing costs.

“The real estate market has a significant impact on people’s daily life,” adds Bin, who notes there is not a lot of empirical research about the real estate industry. “Therefore, it is crucial to understand real estate from both the spatial and historical perspectives. What’s going on in the neighbourhood where you want to buy?”

To fully understand a local market, Bin says people must “mine” the area for data—learn about supply, the location of expensive or affordable houses, the history and current dynamics of an area including crime rates—before they can assess and forecast the house prices and then finally determine if the area is right for them.

Specifically, the RHPMF framework introduces a series of filtering algorithms to extract spatial and historical factors about a particular neighbourhood. For example, the users can input a street address into the web-based or mobile matrix tool. The algorithm can analyze the data and release a comprehensive report to users with all the corresponding information. The result, explains Bin, is to assist estate brokers in visualizing, analyzing and forecasting the spatial and progressive evolution of estate prices from multi-source information.

The researchers tested their matrix using exploratory trials and experiments in Virginia Beach, Philadelphia and Los Angeles. Dr. Li says the forecasting accuracy of the matrix worked well and their series of tests demonstrate the RHPMF’s considerable capability and robustness.

“These case studies indicate that the RHPMF framework can accurately capture the market’s spatial distribution and evolution and then forecast future regional house prices compared with recent baselines,” says Dr. Li. “The results suggest the great potential of the proposed RHPMF in real estate industries.”

Dr. Liu says the proposed framework can help decision-makers in the real estate industry as it can forecast future regional house prices and also provide explainable price factors for in-depth analysis.

“The RHPMF successfully integrates exploratory analysis and price forecasting as a framework,” he adds. “With accurate and explainable analysis, the clients can make smart and reliable decisions related to the estate market.”

The research was published recently in Information Fusion.

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