Seldonian Toolkit Competition - Safety and Fairness for Machine Learning

About the challenge We are happy to announce the first Seldonian Toolkit Competition, a competition that allows undergraduate and graduate students in the US and Canada to learn about and develop safe and fair machine learning algorithms using the recently launched Seldonian Toolkit. This toolkit was created to make it easier for data scientists to apply machine learning responsibly: with high-confidence safety and fairness constraints on the machine learning system's behavior. We welcome people from different fields (not just computer science!) to participate in this competition. Students will have the freedom to select the application and dataset that they work with, allowing them to tackle problems within their own discipline using our machine learning tools and with expert support. Time Commitment The open-ended nature of this competition means that participants could spend a relatively small amount of time, like a weekend, putting together a submission. However, participants might also devote significant time to their project over the 1-2 months that the contest runs. Overall, we anticipate that the most successful teams will spend at least 3-5 hours per week on the project for the 1-2 months that the contest runs.