Ground Truth Keynote - Great Disasters of Machine Learning

Explore the pitfalls and challenges of machine learning in this thought-provoking keynote address from BSidesLV 2016. Delve into real-world examples of machine learning disasters, including the Joshua Brown case and Google's image recognition controversies. Examine the learning expectations gap and unprofessional biases in AI systems. Analyze the concept of "Do No Evil" in the context of big tech companies and data usage. Discover potential solutions, including the importance of tradeoffs and implementing trusted reflective learning models. Gain valuable insights into the ethical considerations and practical challenges facing the field of machine learning.

Introduction
Agenda
Sailing
Passing
Machine Learning
Joshua Brown
Learning Expectations Gap
Machine Learning Mistakes
Examples
Failures
Unprofessional Hair
Google Girls
Do No Evil
Data
Google
False Victory
How do we fix this
Tradeoffs
Trusted reflective learning model
Questions