Week 4
Resources
| Chapter | Topic | Slides | Annotated Slides | Recording |
|---|---|---|---|---|
| 10 | Expected Values of discrete RVs | |||
| 11 | Expected Values of sums of discrete RVs |
For the slides, once they are opened, if you would like to print or save them as a PDF, the best way to do this is:
- Click on the icon with three horizontal bars on the bottom left of the browser.
- Click on “Tools” with the gear icon at the top of the sidebar.
- Click on “PDF Export Mode.”
- From there, you can print or save the PDF as you would normally from your internet browser.
On the Horizon
Class Exit Tickets
Additional Information
Statistician of the Week: Joy Buolamwini
Dr. Buolamwini earned a BS in Computer Science from Georgia Institute of Technology, an Master’s from University of Oxford, and MS and PhD (2022) degrees in Media Arts & Sciences from Massachusetts Institute of Technology. While a graduate student, Dr. Buolamwini was part of the MIT Media Lab. Additionally, she is the founder of the Algorithmic Justice League.
Topics covered
Dr. Buolamwini has done substantial work demonstrating how algorithms can encode bias. Her undergraduate senior project was to create a inspired “mask” mirror as a way to raise spirits for the person who looked into the mirror. The project relied on off the shelf facial recognition software that could not recognize Dr. Buolamwini’s face.
Since then, she has focused her work on demonstrating bias across racial and gender spectra in off the shelf software. Her work has been cited as directly influencing Microsoft and Google’s changes to their algorithms.
Among many other aspects, a big focus of Dr. Buolamwini’s work is pointing out the biased data which directly impacts how algorithms learn how to do tasks.
Relevant work
Buolamwini, J., Gebru, T. Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. Proceedings of Machine Learning Research 81:1–15, 2018 Conference on Fairness, Accountability, and Transparency.
Raji, I & Buolamwini, J. Actionable Auditing: Investigating the Impact of Publicly Naming Biased Performance Results of Commercial AI Products. Conference on Artificial Intelligence, Ethics, and Society, 2019
Outside links
Other
Dr. Buolamwini has done a lot of work on how data propagates through systems to encode the same types of bias into different algorithms. In her video AI, Ain’t I a Woman? she demonstrates how systems designed to determine gender are particularly poor when using dark skinned faces.
Her work was featured in a recent documentary Coded Bias.
Please note the statisticians of the week are taken directly from the CURV project by Jo Hardin.
Muddiest Points
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Clearest Points
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