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Feedback loops

Even with predictive applications that have less immediately far-reaching effects on people, such as recommendation systems, there are difficult issues that we must confront. When services become good at predicting what content users want to see, they may end up showing people only opinions they already agree with, leading to echo chambers in which stereotypes, misinformation, and polarization can breed. We are already seeing the impact of social media echo chambers on election campaigns [91].

When predictive analytics affect people’s lives, particularly pernicious problems arise due to self-reinforcing feedback loops. For example, consider the case of employers using credit scores to evaluate potential hires. You may be a good worker with a good credit score, but suddenly find yourself in financial difficulties due to a misfortune outside of your control. As you miss payments on your bills, your credit score suffers, and you will be less likely to find work. Joblessness pushes you toward poverty, which further worsens your scores, making it even harder to find employment [87]. It’s a downward spiral due to poisonous assumptions, hidden behind a camouflage of mathematical rigor and data.

We can’t always predict when such feedback loops happen. However, many consequences can be predicted by thinking about the entire system (not just the computerized parts, but also the people interacting with it)—an approach known as systems thinking [92]. We can try to understand how a data analysis system responds to different behaviors, structures, or characteristics. Does the system reinforce and amplify existing differences between people (e.g., making the rich richer or the poor poorer), or does it try to combat injustice? And even with the best intentions, we must beware of unintended consequences.

 
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