The Digital Age: The History Of The Digital Age

What the problem is, why is it important
Economic inequality has been an unceasing aspect of human societies. As German philosopher Karl Marx famously observed, “The history of all previous societies has been the history of class struggles”. But while the underlying power dynamics have historically been linked to material possessions and social divides, a new invisible and ubiquitous medium of inequality has emerged. Computers and technology have begun to completely dominate our lives, thrusting the world into what some would call the Digital Age. (Reference) Underlying this plethora of computers are nothing more than numbers and characters, arranged to form algorithms. These algorithms are essential in any computer type device for operation;

Bias is an inherent and vital component in algorithm creation. As Eric Goldman explains, bias is necessary for algorithms (operating) (to puppeteer these) search engines as it allows for the optimization of results; search engines are purposely designed to carefully select the most relevant websites and results to include. (Reference) This bias is an essential part of data sorting. Bias becomes problematic when it is used to
Machine Learning
Programmers are increasingly relying on machine learning to create more effective, complicated systems of data organization and processing never before obtainable. (Reference) Machine learning is a broad term used to describe a computer’s ability to learn and improve without explicit human input. (Reference) This can be done through initial programming of parameters and rules for the program to follow and allow processes to ensue. Additionally, machine learning can be paired with large quantities of data to discover underlying patterns and relationships. Evidently, due to both the nature and the scope of these programs, the inner workings of incredible processes may elude even the programmers themselves. This notion is often referred to as a black box, whereby the input and output are observed and understandable, but the processes

Using AdFisher, they created virtual “people” with no previous browser or cookie history and the desired characteristics to understand how these characteristics would affect the resulting ads. The researchers discovered users had some degree of control over the ads they received, finding that changing user’s tracking settings affected the ads displayed; however, found that there was a statistically significant degree of discrimination on the basis of gender. Notably, there was a *significant* disparity between the displayed frequency of executive positions prefaced by “$200k+” for male and female avatars; male avatars were shown the advertisements 1852 times, while the female group was shown just 318. The range of this study was limited, and further research should be done to confirm and explore these