Explainability is the ability for data scientists to produce applicable results for their peers, managers, and end-users. Can your results be understood and applied by other data scientists and stakeholders? It is up to you as a data scientist to use your business knowledge, mathematical, and programmatic skills to communicate your findings and methods. Explainability is an important tool used to make your reasoning behind each decision transparent and repeatable.
In this course, learn the importance of building an explainability workflow and how to implement these practices from the beginning. Then, using your new skills and tools, apply what you have learned by submitting your own project. Each submission for the hackathon will have a chance at cash prizes during the 6-week competition.
MARCH 17- 12:00PM PST
Online hackathon opens
You can start by publishing your own project and let attendees know you are looking for teammates. You can also join other projects.
6 WEEKS OF IDEATION
Topical training webinars on explainability
Users can attend webinars with our IBM experts to ask questions and receive advice on creating their explainable models for judging
MAY 1- 6:00PM PST
Final submission deadline
All participants submit their project using LALE or AIX 360 combined with their project created on IBM Watson Studio or their preferred notebook
MAY 4
We contact the winners about prizes
Best submissions will be rewarded according to the judging criteria
MAY 6
Winners announced
Stand by after the hack, we'll ask you to say a little something into the camera for the internet!