From the course: Security Risks in AI and Machine Learning: Categorizing Attacks and Failure Modes
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ML dataset hygiene
From the course: Security Risks in AI and Machine Learning: Categorizing Attacks and Failure Modes
ML dataset hygiene
- [Instructor] If you've ever accidentally taught your autocorrect system that a typo is an actual word, and then had to spend months correcting that typo every time your system tried to insert it, you know how frustrating it can be when machines learn the wrong thing. If the data ML and AI systems are trained on isn't good or accurate, the outcome from the model won't be good either. That's why it's incredibly important to vet datasets and implement dataset hygiene policies. Biased data leads to biased classifications and predictions. But bias isn't always obvious or intentional. Consider an automated faucet that is programed to turn on when the computer vision recognizes human hands in front of the faucet. If the system is trained only with light-skinned adult hands, it may not turn on if darker-skinned or small hands are in front of the sensor. Systems need to be trained on datasets that represent the entire…
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