Summary of Wendy Hui Kyong Chun's Discriminating Data

By IRB Media

Get the Summary of Wendy Hui Kyong Chun's Discriminating Data: Correlation, Neighborhoods, and the New Politics of Recognition in 20 minutes. Please note: This is a summary & not the original book.Original book introduction: In Discriminating Data, Wendy Hui Kyong Chun reveals how polarization is a goal—not an error—within big data and machine learning. These methods, she argues, encode segregation, eugenics, and identity politics through their default assumptions and conditions. Correlation, which grounds big data’s predictive potential, stems from twentieth-century eugenic attempts to “breed” a better future. Recommender systems foster angry clusters of sameness through homophily. Users are “trained” to become authentically predictable via a politics and technology of recognition. Machine learning and data analytics thus seek to disrupt the future by making disruption impossible.

Chun, who has a background in systems design engineering as well as media studies and cultural theory, explains that although machine learning algorithms may not officially include race as a category, they embed whiteness as a default. Facial recognition technology, for example, relies on the faces of Hollywood celebrities and university undergraduates—groups not famous for their diversity. Homophily emerged as a concept to describe white U.S. resident attitudes to living in biracial yet segregated public housing. Predictive policing technology deploys models trained on studies of predominantly underserved neighborhoods. Trained on selected and often discriminatory or dirty data, these algorithms are only validated if they mirror this data.

• Language: English
• Publisher: IRB Media
• Release date: Nov 19, 2021
• ISBN: 9781638157328

IRB Media

With IRB books, you can get the key takeaways and analysis of a book in 15 minutes. We read every chapter, identify the key takeaways and analyze them for your convenience.

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Insights on Wendy Hui Kyong Chun's Discriminating Data

Contents

Insights from Chapter 1

Insights from Chapter 2

Insights from Chapter 3

Insights from Chapter 4

Insights from Chapter 5

Insights from Chapter 6

Insights from Chapter 7

Insights from Chapter 1

#1

The Cambridge Analytica scandal showed how social media can be abused and manipulate elections.

#2

Psychographics superseded demographics, geographics, and economics in terms of impact. It was determined that people’s personalities could be changed with rational, yet fear-based messages.

#3

The claims made by Cambridge Analytica, and many other companies that use psychographic targeting, need to be taken with several grains of salt. Their efficacy has not yet been proven.

#4

Cambridge Analytica’s goal was to create a red pill experience, a kind of psychological warfare that would change people’s political views.

#5

Cambridge Analytica used the personality of Facebook users to create highly specific ads that would appeal to certain voters.

#6

The researchers created a vast but sparse matrix of likes and dislikes based on various factors, including political views, parents stayed together until the individual was 21, and intelligence. They then used singular value decomposition to reduce the matrix into a series of vectors, ranked by how much they explained the original data set.

#7

There have been many