New article by Bill Vorhies.

Summary:  There is a great hue and cry about the danger of bias in our predictive models when applied to high significance events like who gets a loan, insurance, a good school assignment, or bail.  It’s not as simple as it seems and here we try to take a more nuanced look.  The result is not as threatening as many headlines make it seem.

Is social bias in our models a threat to equal and fair treatment?  Here’s a sample of recent headlines:

• Biased Algorithms Are Everywhere, and No One Seems to Care
• Researchers Combat Gender and Racial Bias in Artificial Intelligence
• Bias in Machine Learning and How to Stop It
• AI and Machine Learning Bias Has Dangerous Implications
• AI Professor Details Real-World Dangers of Algorithm Bias
• When Algorithms Discriminate

Holy smokes!  The sky is falling.  There’s even an entire conference dedicated to the topic:  the conference on Fairness, Accountability, and Transparency (FAT* – it’s their acronym, I didn’t make this up) now in its fifth year.

Read full article here.