  Successful deployment of artificial intelligence (AI) in various settings has
led to numerous positive outcomes for individuals and society. However, AI
systems have also been shown to harm parts of the population due to biased
predictions. AI fairness focuses on mitigating such biases to ensure AI
decision making is not discriminatory towards certain groups. We take a closer
look at AI fairness and analyze how lack of AI fairness can lead to deepening
of biases over time and act as a social stressor. More specifically, we discuss
how biased models can lead to more negative real-world outcomes for certain
groups, which may then become more prevalent by deploying new AI models trained
on increasingly biased data, resulting in a feedback loop. If the issues
persist, they could be reinforced by interactions with other risks and have
severe implications on society in the form of social unrest. We examine current
strategies for improving AI fairness, assess their limitations in terms of
real-world deployment, and explore potential paths forward to ensure we reap
AI's benefits without causing society's collapse.
