Degand’s article discusses his study of two Black teenage male students with different backgrounds, living in two different neighborhoods in New York, and compares how they view social success, their career goals and their media experiences. The study examines the differences in their community and school demographics, how it is perceived and the impact it has socially. He evaluates the many social and individual factors that influence social success. Noble’s writing illustrates the social inequalities embedded in data algorithms designed by Google and companies like Google. She highlights how technological applications that we engage with online are often thought of as neutral and harmless, but they are designed by people who many times are racist, sexist or who falsely claim meritocracy and these attitudes become part of the online structure and content. It is not accidental. It is part of the structural inequalities in our society that needs to be addressed. Steinkuehler’s video demonstrates how video games can motivate students to read and learn. She explains how much more motivated students are when they are interested in the material they read, and they choose to read it. Students will read at a higher level when they are motivated. The Ted podcast discusses the differences between algorithms and statistics. It examines how people have their own impressions of statistics which are often inaccurate. Decision making is impacted by these perceptions. It is important to delve deeper into statistics to understand what’s behind the data. If the numbers don’t make sense, ask questions. But data is important. Data and statistics have been used to make one of the US cities with the highest crime rates safer. Statistics can be a beneficial resource, but it needs to be done with the understanding that statistics are biased.
In one part of the Ted podcast they say that people often don’t see themselves in the statistics which resonated with me. I’ll hear a stat about something I relate to or a social group I belong to but many times the stat doesn’t accurately represent me. I learned in one of my stats classes years ago to look underneath the numbers to see what is being measured, how it is determined and by whom. I also look at who is sponsoring or funding the study or report. In terms of statistics I don’t trust much of what is published in mainstream media or certain organizations.
What other media or technology currently thought of as neutral should be examined for bias?
Sylvia's blog 