What are the biases in facial recognition technology?

What are the biases in facial recognition technology?

Facial recognition technology can exhibit biases based on race, gender, and age, leading to misidentification.

Can you give examples of how these biases manifest in real-world applications?

Sure! For instance, studies have shown that facial recognition systems often misidentify women and people of color more frequently than white males.

What are the implications of these biases for society?

The implications include potential discrimination in law enforcement, hiring practices, and privacy violations, which can exacerbate social inequalities.

How can we mitigate these biases in AI systems?

Mitigating biases involves using diverse training datasets, implementing fairness audits, and promoting transparency in AI development.