“How an algorithm can fight election bias so every vote counts” By Brian Olson (It is titled differently on YouTube but it is the same video)

In Brian Olson’s TED Talk, he spoke about how human-made congressional districts are often racially or partisanly biased and proposed his computer algorithm as a way to mitigate this issue. The main points of the speech included defining gerrymandering with a divided array as an example, showing how gerrymandering is implemented in real-world cases (Florida and North Carolina), and finally demonstrating how his compactness algorithm helps to fix the gerrymander. Although this topic is something I am already fairly knowledgeable on, I did find his explanation of compactness and his algorithm to be illuminating, and it helped me to get a deeper understanding of how compactness algorithms work in relation to population size/density.

Olson was one of the more obviously nervous TED speakers I have seen, but he was still able to communicate his message to the audience effectively. Although he may have stumbled over a few words, Olson sounded very knowledgeable about the topic, adding ethos to his argument and making the audience trust his algorithm. Olson also used a more conversational/storytelling style, which differentiates a TED Talk from a normal speech. Instead of simply delivering a memorized script, it felt like Olson was explaining the topic to a friend or colleague. This made a potentially complicated or confusing topic accessible to the broader audience.