Data

Lecture

Guest expert: Jacquie Rowe

Slides: 

Reading

Required - Data Justice

"What is data justice? The case for connecting digital rights and freedoms globally"

https://journals.sagepub.com/doi/full/10.1177/2053951717736335

This paper provides an introduction to some of the disparity different people experience in the gathering or use of their data. It also proposes a solution, which is less critical if you'd rather read one or more of the optional readings.

Optional - The Work Behind Data

"Justice for "Data Janitors""

https://www.publicbooks.org/justice-for-data-janitors/ or https://www.degruyter.com/document/doi/10.7312/marc19008-003/html

This chapter discusses the amount of hidden work done behind the scenes by people who get very little credit for enabling the power of Big Data.

If this is particularly interesting to you, check out the book "Ghost Work" by Mary L Gray and Siddharth Suri.

Optional - The Future of Data

"Data as Property"

https://phenomenalworld.org/analysis/data-as-property

A more challenging read talks about possible future directions for data ownership.

Optional - Data Ownership

"It’s time for a Bill of Data Rights"

https://www.technologyreview.com/2018/12/14/138615/its-time-for-a-bill-of-data-rights/

This essay argues that ownership is not only a poor way to solve problems with the use of people's data, but actually introduces new problems.

Optional - Values in Datasets

"Making Intelligence: Ethical Values in IQ and ML Benchmarks"

https://dl.acm.org/doi/pdf/10.1145/3593013.3593996

This paper explores how ethical values end up entangled in supposedly objective benchmarks, in this case in relation to the use of IQ in machine learning research.