![[OLD FALL 2019] 15-104 • Introduction to Computing for Creative Practice](https://courses.ideate.cmu.edu/15-104/f2019/wp-content/uploads/2020/08/stop-banner.png)
The Looking Outwards that I found particularly interesting was Tai Manheim’s exploration of the work of Stamen, an artificial intelligence tool used to analyze wealth and poverty at an urban scale. The project was developed by Stamen and researchers at Carnegie Mellon University and it implements neural networks to develop machine learning frameworks that take in information on infrastructure in various neighborhoods and subsequently uses the information to make predictions on the average income of the areas. I found Tai’s explanations of the digital techniques and data structure networks that make this project possible to be highly informative and intriguing. With a project of this scope, there are inevitably associations and unintended associations made between income, race, crime, and location which can be used negatively for increased surveillance and policing strategies. This raises issues on the interface between Artificial Intelligence, machine learning, and politics, as the impressions and conclusions derived from a project like this may be based on hard data, but can be used negatively. There are also clear design incentives as to how residential areas can be organized better according to the data found from a project like this.
https://stamen.com/work/penny/
https://courses.ideate.cmu.edu/15-104/f2019/2019/10/06/taisei-manheim-looking-outward-07/