Intro to Explainable AI

Abstract of the talk:

It seems like AI is everywhere these days, yet few people trust this technology, due in large part to black box algorithms, which prevent people—including the systems’ creators—from knowing and understanding why such algorithms make recommendations and decisions. Left unchecked, this lack of transparency can lead to biased outcomes that put people and businesses at risk.

Luckily, there’s an answer to this problem: explainable AI—machine learning that can be understood by and explained to those outside engineering.

In this talk, you’ll learn how explainable AI’s more transparent nature decreases bias and increases trust in AI and leads teams toward interpretability by helping them predict how algorithms will behave. Whether you're trying to incorporate AI into your business or want to understand how algorithms make decisions, this course will help you quickly learn the core tenets of explainable AI, the cases for and against it, and how to use it in your own business.

About the speaker:

Dr. Lauren Maffeo leads business intelligence research at GetApp, which matches software shoppers with the right tools and technologies to grow their businesses. As an analyst, Lauren’s areas of interest include speech and natural language tools, data mining techniques, predictive analytics, and building a business case for data science. Lauren has presented her research on bias in AI at Princeton and Columbia Universities, Twitter’s San Francisco headquarters, and Google DevFest DC, among others. She holds membership in the Association for Computing Machinery’s Distinguished Speakers Program and the International Academy of Digital Arts and Sciences.

Schedule for the event:

17:45 - Doors open, meet and greet

18:05 - Introductions

18:15 - Talk and Q/A

19:20 - Cleanup & Networking