Crawford, Kate, NY Times, “Artifical Intelligence’s White Guy Problem” – This reading taught me that AI can have the same biases that people have. I was surprised that technology that seems smart can still make serious mistakes like misidentifying Black people or giving some people fewer job ads based on their gender. I also learned that the data used to train an AI system can affect how it works. If the data does not include enough different types of people the system may not work fairly for everyone. The example about predictive policing also stood out to me because the system can use old crime data and send more police to the same neighborhoods. This can lead to more arrests in those areas and continue the same problem. I agree that people need to pay more attention to how AI systems are designed and tested. It also makes me wonder how many other AI problems people have not discovered yet.
Benjamin, Race After Technology (Excerpt) – One idea that stood out to me was the idea of a glitch. I usually think of a glitch as a small computer mistake that needs to be fixed, but this reading explains that a glitch can show a bigger problem in the system. I also found the idea of being watched but not seen interesting. Technology can collect information about people without really understanding them. The reading made me think about how people may trust technology more because they believe computers are objective. A computer can still make unfair decisions if the system was built with unfair ideas or information. I was also surprised by the idea that a technology can be created to help people but still cause more problems. This made me question whether fixing a technology is enough when the larger system is already unfair. The reading showed me that we really need to look at why a system was created and who it was designed to help.
