The good combination of IBM coder program and code patterns motivated me to dig in into the data science area a little bit, relating to the real-world topic diversity and inclusion . Also it was interesting to see: How the AI Fairness 360 toolkit, which is an open-source library to help detect and remove bias in machine learning models, can be used for data science in this context.
The diversity and inclusion topic started with a good statement:
“Diversity and inclusion is more than a checkbox, it’s fuel for growth and success in any business or community.” IBM Coder Program.
This was one reason why I completed the challenges of the Diversity and Inclusion topic in the IBM coder program .
The IBM coder program
If you don’t know the IBM coder program, just visit this link .
The program is about:
- Growing skillset and reputation
- Discovering and transparently discuss content
- Being rewarded with various tangible and intangible rewards
The code pattern Ensure loan fairness was one of the challenges for Diversity and Inclusion topic.
Code pattern “Ensure loan fairness”
This code pattern is about machine learning with artificial intelligence, data science and python. It gives an introduction in how to use the AIF360 tool kit .
- “The AI Fairness 360 toolkit is an open-source library to help detect and remove bias in machine learning models. The AI Fairness 360 Python package includes a comprehensive set of metrics for datasets and models to test for biases, explanations for these metrics, and algorithms to mitigate bias in datasets and models.“
In the related demo on IBM research-trusted UI, you can verify given ai models with different datasets and the bias mitigation algorithms .
Review questions for the code pattern
Here are my observation, to my three review questions , which were defined in my previous Let`s pattern blog.
1. Did the code pattern work?
There are two ways to instantiate the code pattern and I tried via IBM Cloud Watson Studio in the github project and it worked!
I want to add some small additional information : When you create an IBM Cloud Watson Studio project you have many options and project types. The best project type, which fits here is data science. This project should contain data and notebooks.
But when you create such a project, you will be a little bit confused, because there is no option to add notebooks as defined in the instructions. But, you can create a notebook directly using tools->notebook in the menu and then you can add your notebook to your project, with the given url.
Also it would be good if you use the “Default Python 3.5 Free (1 vCPU and 4 GB RAM) ” as runtime. The installation of spark and the additional python packages needed worked for me with this configuration.
2. Did I need to make changes to the readme?
3. How complex was it for me?
Even if it was a small code pattern, I did notice that this is really targeting the data science topic and I am not data science expert. This was a good reason for me to dig a little bit deeper into bias mitigation algorithm. But the code pattern was easy to instantiate with IBM Watson Studio.
It was a good combination of the IBM coder program and the code pattern, it motivated me to dig in a little bit into the data science area, with a good real-life topic diversity and inclusion .
Maybe now you are a little bit curious and you want to check out the code pattern and the diversity and inclusion challenges? Have fun and learn 😉
I hope this was useful for you and let’s see what’s next?
PS: By the way, you can use the IBM Cloud for free, if you simply create an IBM Lite account. Here you only need an e-mail address.
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