60 Bias

You can follow along with the slides here if they do not appear below.

60.1 Curated Videography

60.1.1 Data Science Ethics in 6 Minutes

60.1.2 AI for Good in the R and Python ecosystems

60.1.3 Are We Automating Racism?

60.1.4 Big Tech’s B.S. about AI ethics

60.1.5 More Bias

60.2 Annotated Bibliography Instructions

An annotated bibliography is a list of citations but with commentary! Ok, more like just an enhanced list where you summarize the source and explain why it is important to include.

Your mission is to add either a citation or an annotation (or both) to this list of Data Science and Ethics Readings.

  1. Carole Cadwalladr and Emma Graham-Harrison. How Cambridge Analytica turned Facebook ‘likes’ into a lucrative political tool. The Guardian. 17 March 2018.
  2. Chen Wenhong and Anabel Quan-Haase. Big Data Ethics and Politics: Toward New Understandings. Social Science Computer Review. 14 November 2018.
  3. Nitasha Tiku. [Google hired Timnit Gebru to be an outspoken critic of unethical AI. Then she was fired for it.]. (https://www.washingtonpost.com/technology/2020/12/23/google-timnit-gebru-ai-ethics/). The Washington Post. 23 December 2020.
  4. Dan Swinhoe. The biggest data breach fines, penalties, and settlements so far. CSO. 5 March 2021.
  5. Sara Morrison.Why you should care about data privacy even if you have “nothing to hide”. Vox. Jan 28 2021.
  6. Richard Van Noorden. The ethical questions that haunt facial-recognition research. Nature. 18 November 2020.
  7. Karen Hao. Big Tech’s guide to talking about AI ethics. MIT Technology Review. April 13 2021
  8. Catherine D’Ignazio and Lauren F. Klein. Data feminism. Mit Press, 2020.
  9. Inclusive Communication Principles in Health Equity Guiding Principles for Inclusive Communication. CDC, 2022