What are some real-world examples of algorithmic bias in AI?

What are some real-world examples of algorithmic bias in AI?

Algorithmic bias can be seen in various areas. For instance, facial recognition systems have shown higher error rates for people of color compared to white individuals.

Can you explain why this bias occurs?

Bias often occurs due to unrepresentative training data, where the data used to train AI models does not adequately reflect the diversity of the real world.

What are the consequences of algorithmic bias?

Consequences can include unfair treatment in hiring processes, biased law enforcement practices, and unequal access to services, which can perpetuate social inequalities.

How can we mitigate algorithmic bias?

Mitigating bias involves using diverse datasets, implementing fairness checks, and continuously monitoring AI systems for biased outcomes.