Proactive Explanations: Python Workflows for Data Science and AI

Welcome to the "AI Trust, Bias and Explainability" learning series, by IBM AI. In collaboration with IBM team, we host a series of practical introductory sessions to AI trust, bias and explainability.

This is the 5th session:
In this workshop, we will talk on the typical data science workflow with a focus on explainability. It focuses on skills and tactics used to help data scientists articulate their findings to end-users, stakeholders, and other data scientists. From data ingestion, cleaning and feature selection, and ultimately model selection, explainability can be incorporated into a data scientists workflow. Using a combination of semi-automated and open source software,

This workshop will expand and go deeper on the previous webniar, and walks you through an explainable workflow.

All sessions of the series:

  • Jul 27th - AI Security Privacy-Preserving Machine Learning by IBM AI. Session 1
  • Aug 10th - Explainable AI Workflows using Python. Session 2
  • Aug 17th - Understanding and Removing Unfair Bias in ML. Session 3
  • Aug 24th - Adversarial Robustness 360 Toolbox For ML. Session 4
  • Aug 31st - Workshop: Explainable AI Workflows. Session 5
  • Austin Eovito

    Austin is a Data Scientist in IBM, who focuses on the balance of bleeding-edge research produced by academia and the tools used in applied data science.
    • Date: Aug 31, 10:00 AM PST
    • Fee: Free
    • Available Seats: 20 (max 200)
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