Fooling AI and transparency – testing and honesty is critical

The effect of digitally changing an image on a classifier. Can you tell the difference between the pictures? Image from Brendel et al 2017.

If you follow my posts on AI (here and on other sites) then you’ll know that I’m a big believer on ensuring that AI models are thoroughly tested and that their accuracy, precision and recall are clearly identified.  Indeed, my submission to the Science and Technology select committee earlier this year highlighted this need, even though the algorithms themselves may never be transparent.  It was not a surprise in the slightest that a paper has been released on tricking “black box” commercial AI into misclassification with minimal effort. Continue reading Fooling AI and transparency – testing and honesty is critical