Mainstream media have caught onto the difficulties of machine learning. Most saliently, they just love to report how AI and bots can be as racist, discriminatory, or biased as humans. Some examples:
- Microsoft’s racist Twitter bot (Verge, 2016)
- Gender-biased text mining AI (Guardian, 2017)
- Racist criminal profiling bot (ProPublica, 2016)
Google’s Sentiment Analyzer Thinks Being Gay Is Bad (Andrew Thompson on Motherboard, 2017)
Instead of arguing to shut down all bots, I would prefer news outlets to to explain what’s really happening. However, this can be quite difficult and complex, especially when the audience has no knowledge of machine learning. Fortunately, I found the video below, where some people at Google provide a really good laymen explanation as to how bias slips into our machine learning models. It covers interaction bias (where the human-machine interactions bias the learner), latent bias (where unobserved patterns in the learning data cause bias), and selection bias (where the selected learning sample isn’t representative of the population). Can you try and figure out which one(s) apply to the news articles above?