Chinese tech giants have opened their AI platforms to scientists fighting coronavirus. Alibaba Cloud and Baidu are offering a powerful weapon to life-science researchers working to stop the spread of the illness officially known as Covid-19: free access to their computing horsepower and tools.
Computer scientists are struggling to purge bias from one of AI’s most important datasets. ImageNet’s 14 million photos are a go-to collection for training computer-vision systems, yet their descriptive labels have been rife with derogatory and stereotyped attitudes toward race, gender, and sex.
Sophisticated models trained on biased data can learn discriminatory patterns, which leads to skewed decisions. A new solution aims to prevent neural networks from making decisions based on common biases.
Will biases in training data unwittingly turn AI into a tool for persecution? Bias encoded in software used by nominally objective institutions like, say, the justice or education systems will become impossible to root out.
One of the largest open datasets for training face recognition systems has its roots in a popular photo-sharing service. Companies that have used this data could find themselves liable for millions in legal recompense.
The latest language models are great at answering questions about a given text passage. However, these models are also powerful enough to recognize an individual writer’s style, which can clue them in to the right answers. New research measures such annotator bias in several data sets.
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