In layman terms, this is about getting results from that is skewed because of the input data.
You’ll learn this in your machine learning modules (19, 20, and 21) that a data model is highly dependent on the input data which you fed it. Thus, data collection techniques, cleaning and pre-processing is of supreme importance since it largely affects how your model is going to perform.
Students Do: Healthcare Bias
Let’s open up the file(s) in the 01-Stu_HealthcareBias folder to get started.
Algorithmic Bias Materials
- https://www.datarobot.com/newsroom/press/datarobots-state-of-ai-bias-report-reveals-81-of-technology-leaders-want-government-regulation-of-ai-bias/
- https://thecounter.org/usda-algorithm-food-stamp-snap-fraud-small-businesses/
- https://www.scientificamerican.com/article/racial-bias-found-in-a-major-health-care-risk-algorithm/
Bootstrap Crash Course
I have created a public repo to demonstrate Bootstrap here: https://github.com/jonathan-moo/bootstrap-crash-course
The focal point is the layout of a page where you can render your data visualizations in. The rest is up to your experimentation, creativity and imagination.
We will not dive into front-end development or UX design because it is purely out of scope.
