Get hands-on with Explainable AI at Machine Learning Interpretability(MLI) Gym!

Hello Makers! Join us this evening to attend a hands-on workshop on machine learning interpretability!

Following is a brief agenda for the evening:

6 - 6:30 PM - Doors open and pizza
6:30 - 7:00 PM - Pramit's Talk
7:00 - 7:45 PM - Hands-on Lab
7:45 - 8:00 PM - Q&A and Networking

Note: Please bring your laptops for the hands-on part.

Description:

With the effort and contributions from researchers and practitioners from academia and industry, Machine Learning Interpretation has become a young sub-field of ML. However, the norms around its definition and understanding is still in its infancy and there are numerous different approaches emerging rapidly. However, there seems to be a lack of a consistent explanation framework to evaluate and consistently benchmark different algorithms - evaluating against interpretation, completeness and consistency of the algorithms.
The idea with the gym is to provide a controlled interactive environment for all forms of Machine Learning algorithms, - initially focusing on supervised predictive modeling problems, to allow analysts and data-scientists to explore, debug and generate insightful understanding of the models by

1.Model Validation: Ways to explore and validate black box ML systems enabling model comparison both globally and locally - identifying biases in the training data through interpretation.

2.What-if Analysis: An interactive environment where communication can happen i.e. enable learning through interactions. User having the ability to conduct "What-If" analysis - effect of single or multiple features and their interactions

3.Model Debugging: Ways to analyze the misbehavior of the model by exploring counterfactual examples(adversarial examples and training)

4. Interpretable Models: Ability to build natively interpretable models - with the goal to simplify complex models to enable better understanding.
   The central concept with MLI gym is to have an interactive environment where one could explore and simulate variations in the world(a world post a model is operationalized) beyond the defined model metrics point estimates - e.g. ROC-AUC, confusion matrix, RMSE, R2 score and others.

Speaker's Bio:
Pramit is a Lead Data Scientist/ at H2O.ai. His area of interests is building Statistical/Machine Learning models(Bayesian and Frequentist Modeling techniques) to help the business realize their data-driven goals.

Currently, he is exploring "Model Interpretation" as means to efficiently understand the true nature of predictive models to enable model robustness and security. He believes effective Model Inference coupled with Adversarial training could lead to building trustworthy models with known blind spots. He has started an open source project Skater: https://github.com/datascienceinc/Skater to solve the need for Model Inference(The project is still in its early stages of development but check it out, always eager for feedback)