Weapons of Math Destruction Themes
The main themes in Weapons of Math Destruction are risk to marginalized communities, corruption, and human bias.
- Risk to marginalized communities: Decisions made by Weapons of Math Destruction (or WMDs) often amplify and perpetuate discriminatory practices based on wealth, race, gender, and other identity categories.
- Corruption: The use of “Big Data” that is unavailable to the people it affects encourages corrupt practices and the unethical use of people’s information.
- Human bias: In relying on humans to create and alter WMDs, we often preserve human bias within the algorithms themselves.
Last Updated on April 2, 2020, by eNotes Editorial. Word Count: 352
The central claims O'Neil uses to develop her argument against Big Data are that it preys on the disadvantaged, operates without public knowledge, and justifies old biases that can’t be questioned.
Risk to Marginalized Communities
O'Neil argues that the data industry puts marginalized communities at a specific disadvantage based on their already misfortunate circumstances. She explains how credit scores are increasingly used in hiring decisions, meaning that applicants with lower scores are automatically disqualified. Furthermore, those who live in impoverished neighborhoods are often more heavily watched by law enforcement based on crime prediction models. O'Neil says that this allows for the wealthy to maintain control and for the possibility of upward mobility to diminish.
O'Neil explores how the opaque processes by which data analysis operates inevitably lead to corruption. E-scores are one of these processes that quantify and classify individuals based on undisclosed factors. The increasing prevalence of artificial intelligence in creating individual dossiers allows the collection of personal data to be largely invisible to the public. O'Neil contrasts these shady methods of data application with the transparency of FICO credit scores and...
(The entire section contains 352 words.)
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