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510-ethics-checklist.qmd
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510-ethics-checklist.qmd
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# Checklist {#ethics-checklist .unnumbered}
```{r ethics-checklist}
#| echo: false
#| results: asis
#source("_common.R")
#status("complete")
```
## Videos / Chapters
- [ ] [ Privacy](https://imperial.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=5d85c64e-ce0a-4a18-bd3b-afa5010ceca8) (19 min) [[slides]](https://github.com/zakvarty/effective-data-science-slides-2022/raw/main/05-01-privacy/05-01-privacy.pdf)
- [ ] [ Fairness](https://imperial.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=96301a9c-c129-44fd-bd3f-afa700fdeeaa) (20 min) [[slides]](https://github.com/zakvarty/effective-data-science-slides-2022/raw/main/05-02-fairness/05-02-fairness.pdf)
- [ ] [ Codes of Conduct](https://imperial.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=5c457e71-1f44-4272-a46b-afa701747ba6) (14 min) [[slides]](https://github.com/zakvarty/effective-data-science-slides-2022/raw/main/05-03-codes-of-conduct/05-03-codes-of-conduct.pdf)
## Reading
Use the [Data Science Ethics](#ethics-reading) section of the reading list to support and guide your exploration of this week's topics. Note that these texts are divided into core reading, reference materials and materials of interest.
## Activities
This week has fewer activities, so that you may look over the second assessment before the end of the course.
_Core:_
- Read [Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification](https://proceedings.mlr.press/v81/buolamwini18a.html) by Joy Buolamwini and Timnit Gebru (2018). Proceedings of the 1st Conference on Fairness, Accountability and Transparency.
- Find an example case study or method relating to ethical data science that has not been covered in the lectures. Share what you find by writing a short summary of the case study or method on the discussion forum.
- Skim over the Professional Guidelines listed in the reference materials for this week, in preparation for the live session.
_Bonus_
- Complete the [Fairness worksheet](https://zakvarty.github.io/effective-data-science-resources/activities/fairness-worksheet/fairness-worksheet-questions.html).
- Complete the [Privacy worksheet](https://zakvarty.github.io/effective-data-science-resources/activities/privacy-worksheet/privacy-worksheet-questions.html).
## Live Session
In the live session this week we will begin with a few minutes of Q & A about the assessments. We will then break into groups to discuss and compare several sets of professional guidelines on ethical data science.
Finally, if time allows we will round up the session with an activity on randomised response survey designs or data anonymisation.