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Bias and Truth and AI, Oh My October 4, 2017

Posted by Peter Varhol in Machine Learning, Software development, Technology and Culture.
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I was just accepted to speak at the Toronto Machine Learning Summit next month, a circumstance that I never thought might happen.  I am not an academic researcher, after all, and while I have jumped back into machine learning after a hiatus of two decades, many more are fundamentally better at it than me.

The topic is Cognitive Bias in AI:  What Can Go Wrong?  It’s rather a follow-on from the presentations I’ve done on bias in software development and testing, but it doesn’t really fit into my usual conferences, so I attempted to cast my net into new waters.  For some reason, the Toronto folks said yes.

But it mostly means that I have to actually write the presentation.  And here is the rub:  We tend to believe that intelligent systems are always correct, and in those rare circumstances where they are not, it is simply the result of a software bug.

No.  A bug is a one-off error that can be corrected in code.  A bias is a systematic adjustment toward a predetermined conclusion that cannot be fixed with a code change.  At the very least the training data and machine learning architecture have to be re-thought.

And we have examples such as these:

If you’re not a white male, artificial intelligence’s use in healthcare could be dangerous.

When artificial intelligence judges a beauty contest, white people win.

But the fundamental question, as we pursue solutions across a wide range of applications, is:  Do we want human decisions, or do we want correct ones?  That’s not to say that all human decisions are incorrect, but only to point out that much of what we decide is colored by our bias.

I’m curious about what AI applications decide about this one.  Do we want to eliminate the bias, or do we want to reflect the values of the data we choose to use?  I hope the former, but the latter may win out, for a variety of reasons.

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