Read The Power Chapter From Data Feminism And Then Do The Fo

Readthe Power Chapter Fromdata Feminismand Then Do The Followingsumma

Read the Power Chapter from Data Feminism and then do the following: Summarize one example from the reading and explain how the example demonstrates a power dynamic that occurs when people work with data. (4-6 sentences) How do the lessons from data feminism relate to the work we do in data visualization? (2-5 sentences) Warning: This article contains content about violence against women and people of color. If you don't think you can complete the assignment contact the professor for an alternative assignment. The original source of the article can be found here: The Power Chapter from (Data Feminism by Catherine D'Ignazio and Lauren Klein)

Paper For Above instruction

The chapter “The Power” from Data Feminism provides a compelling example of how data can reinforce existing social hierarchies and marginalize vulnerable groups. One notable illustration describes how facial recognition technologies have perpetuated racial biases by misidentifying Black and Indigenous people at higher rates than white individuals. This example demonstrates a profound power dynamic: those in control of data and technology can unintentionally, or intentionally, reinforce systemic inequalities that silence and discriminate against marginalized communities. This power imbalance arises from biased training data, lack of diversity among developers, and the absence of critical scrutiny about who benefits from these technologies. As a result, marginalized groups often bear the consequences of these biases, highlighting how data practices can reinforce societal hierarchies rather than challenge them.

The lessons from data feminism emphasize the importance of questioning and challenging power structures embedded within data systems. In data visualization, these lessons encourage us to represent data ethically, ensuring marginalized voices are included and biases are acknowledged. By recognizing the power dynamics inherent in data, practitioners can create visualizations that not only inform but also empower communities to advocate for change, fostering a more equitable data culture.

References

D’Ignazio, C., & Klein, L. (2020). Data Feminism. MIT Press.

Benjamin, R. (2019). Race After Technology: Abolitionist Tools for the New Jim Code. Polity Press.

Buolamwini, J., & Gebru, T. (2018). Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. Proceedings of Machine Learning Research, 81, 1-15.

Boyd, D., & Crawford, K. (2012). Critical Questions for Big Data: Warrants for Data-based Decision Making. Information, Communication & Society, 15(5), 662-679.

Eubanks, V. (2018). Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. St. Martin’s Press.