This is not a cat


The other day I happened to notice the Microsoft’s OneDrive software had graciously went through my photos and tag them based on what it thought was the content of the photo.  Slightly irritated (I did not ask it to tag the photos) I scrolled through the tags to find the follow picture of my beloved late dog Hiko:

hiko-cat

As some of you are aware, when training pattern recognition neural networks a series of contrasting photos are shown to allow the algorithm to learn what it is seeking.  In come cases people use cat and dog images (example here) to build such a detection algorithm.  Clearly, Microsoft’s OneDrive algorithm needs some tuning.

When I mentioned this to a colleague, he proceeded to run the same picture of my dog through his own cat/dog deep learning system… and pronounced that it also classified my dog as a cat.

After a few laughs around the office, it struck me that in lieu of some significant ground truth (like, I lived with this dog for 13 years and can vouch his dog-ness) it would be hard to argue against 2 independent algorithms using the same information to come to the same conclusion.  Imagine if some algorithms got together and decided I was prone to criminality.  Or maybe that you would be a poor choice for a job.  Or as a parent.  In these less black and white situations, the independent results of 2 algorithms would be hard to argue against, especially if we don’t know how the decision was arrived at.

In the latest report from the New York University’s’ AI Now Institute, (report on Medium here) there are 10 recommendations regarding improving the equity  responsibility AI algorithms and their societal applications.  These range from limiting use of black box algorithms (like the one used for my dog) to improving the quality of the datasets and trained algorithms, including regular auditing.

For those of you working actively in the AI field, take heed.

 

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