Week 7
Misrepresentation of findings, data privacy, and algorithmic bias.
No. |
Title |
Slides |
Additional |
|---|---|---|---|
| 13 | Ethics: Misrepresentation | Raw | |
| 14 | Ethics: Bias | Raw |
Lecture Recordings
Lecture recordings are only available for IDS students via the University of Edinburgh virtual learning environment. To access the recordings, go to the Introduction to Data Science area on LEARN ULTRA and select Lecture Recordings under the Course Content section. This will open echovideo where you will find a list of all lecture recordings to watch once they become available.
Some of this material is required and some of it is optional. We expect you to watch the required videos and read the required reading. This required material is part of the course so it may be assessed in the assignments and it may not be covered in the lectures. The optional material is extra reading for those that are interested!
| MDSR: Chp 8 - Data science ethics | Required | |
| Ethical challenges in online research: Public/private perceptions | Optional | |
| Algorithmic Unfairness Without Any Bias Baked In | Optional |
Below are a number of optional "guest lectures" from YouTube that illustrates why it is important to be aware of ethical issues when doing data science. Note that some videos are about 1 hour long, so we recommend watching as much as of them as you’re interested – they cover highly important topics without being highly technical.
In this week's workshop you will continue to work on your group projects. Further information can be found here and this page will be updated regularly towards the end of semester. There will be the first check-in this week so it is highly recommended that you need to arrange a meeting in your team to work on your project before Friday. Please regularly follow the project related updates and announcement.
A check-in will consist of a discussion with an allocated tutor to receive feedback on your current progress and on your future plans. You should demonstrate that you have a basic understanding of the data you have selected and have made initial steps in cleaning, summarising and visualising your data. We will be looking to understand whether you have a clear problem statement and that you have a plan as to how you would explore the data to answer your statement.