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What are the practical applications of AI in smart cities today that can actually benefit citizens? In our latest Smart City Podcast, we interviewed Seth Dobrin, chief AI officer for IBM, and Graham MacDonald, CIO and VP of technology and data sciences at the Urban Institute about their collaborative AI-based approach to mitigating the adverse effects of gentrification in American cities and communities. 

The theme of this podcast was "AI for Good." There's a lot of hype surrounding AI, and it can be difficult to find truly practical and actionable use cases, but this is a great use case with direct benefits for people being affected by increasing gentrification in many US cities and communities. It's also a great example of a public/private partnership that is producing results. The focus of this project was to put the tools and technologies offered by companies like IBM into the hands of nonprofit research organizations like the Urban Institute so they can do research and execute projects that provide the greatest benefit to the people that need it most. 

Seth describes the technology side of the solution and Graham describes the outcomes.  Here's what Seth had to say about the project:

"How do we help the Urban Institute better manage gentrification in the communities? How do we ensure that it's done in a trustworthy manner? How do we ensure that it's fair and free of specific types of biases, or in this case, how do we help them identify specific types of biases? How do we make the models transparent? How do we make them robust? And how do we ensure that we're preserving the privacy of people that are being impacted by these models? What we did was we brought our tools, our Cloud Pak for Data with Watson Studio, to the Urban Institute, along with some folks from the data science and AI elite team, to work on this problem."

Armed with these tools, Grant MacDonald and the Urban Institute team were able to develop a solution to measure and mitigate the adverse effects of gentrification, where affluent residents move into long-established neighborhoods comprised of middle-class and working-class citizens who in many cases find themselves priced out of their own homes because they can no longer afford to live there. According to Grant:

"We initially worked with HUD to define neighborhoods that were gentrifying, which were declining, and which were inclusively growing. The vast majority of neighborhoods happened to be in the unchanging or inclusively growing category. And there are a small number of neighborhoods that are in the declining and gentrifying category. And what we were trying to do was to find those neighborhoods, and then use machine learning, taking the data that we have to project or predict, in real-time, what is happening right now."

You'll have to listen to the rest to get the details, but it's an excellent real-world example of a practical application of AI that provides true economic and social benefits to citizens and communities.  

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