Episode 20: Biases in Machine Learning Part I Fast and easy tech!

Classic machine learning algorithms involve techniques such as decision trees and association rule learning, including market basket analysis (ie, customers who bought Y also bought Z). Deep learning, a subset of machine learning that includes neural networks, attempts to model brain architecture through the use of multiple, overlaying models.

Classic examples of Machine learning include Virtual Personal Assistants like Siri, Alexa, Google, Predictions while Commuting, Videos Surveillance, Social Media Services from personalizing your news feed to better ads targeting, Face recognition, Email Spam and Malware Filtering, Online Customer Support including chat bots, Search Engine Result Refining, Product Recommendations based on previous purchase trends, and Online Fraud Detection including efforts to curb money laundering.

Removing bias from AI is the result of deliberate, calculated and thought-out human endeavors, and certainly not an unintended byproduct of certain data analysis. Companies employing any or all five forms of AI — computer vision, natural language, virtual assistants, robotic process automation, and advanced machine learning — must realize that any output they hope to derive are only as good as the data on which the applications are trained.