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.
