Preventing AI from deepening social inequality
Preventing AI from deepening social inequality
From Google searches and dating sites to detecting credit card fraud, artificial intelligence (AI) keeps finding new ways to creep into our lives. But can we trust the algorithms that drive it?
As humans, we make errors. We can have attention lapses and misinterpret information. Yet when we reassess, we can pick out our errors and correct them.
But when an AI system makes an error, it will be repeated again and again no matter how many times it looks at the same data under the same circumstances.
AI systems are trained using data that inevitably reflect the past. If a training data set contains inherent biases from past human decisions, these biases are codified and amplified by the system.
Or if it contains less data about a particular minority group, predictions for that group will tend to be worse. This is called “algorithmic bias”.
Gradient Institute has co-authored a paper demonstrating how businesses can identify algorithmic bias in AI systems, and how they can mitigate it.
An article in The Conversation by Gradient’s Tiberio Caetano and Bill Simpson-Young discussing a technical paper co-written with Australian Human Rights Commission, Consumer Policy Research Centre, CHOICE and CSIRO’s Data61.
Access the full article here.
