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 7th 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
Sep 9th - Workshop: Explainable AI Workflows. Session 6
Sep 21st - Workshop: Explainable AI Workflows. Session 7
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.