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1
Introduction
2
Scenario
3
Apple Credit Card Story
4
Diversity in Tech
5
Testing Recommendations
6
IBM Watson
7
Testing Machine Learning Algorithms
8
Learning How Machine Learning Works
9
Testing Machine Learning
10
The Pox Method
11
The Problem
12
The Bug Report
13
The Meeting
14
The Trolley Problem
15
Ethics
16
Diversity
17
Security Privacy Bias
18
QA for AI
19
Testing for outliers
Description:
Explore the challenges and strategies for quality assurance in AI-driven applications in this 54-minute conference talk. Delve into the critical importance of testing machine learning-enabled systems, gaining insights into testable ML features and a step-by-step approach to ensure AI applications function as intended. Learn from real-world examples, including the Apple Credit Card controversy, and understand the ethical implications of AI development. Discover the "Pox Method" for testing machine learning algorithms, and examine key considerations such as diversity, security, privacy, and bias in AI systems. Gain valuable knowledge on testing for outliers and ensuring the quality of cutting-edge AI applications across various industries.

QA for AI: Testing Machine Learning Applications - The Reality of Developing an Artificial World

ChariotSolutions
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