AI systems today exhibit biases that have real world consequences. For example:
- Hiring algorithms are rejecting candidates based solely on their names, often due to associations with certain ethnic or cultural backgrounds.
- Facial recognition technology performs poorly on individuals with darker skin tones, leading to higher error rates and unfair outcomes.
- Language models perpetuate gender stereotypes by associating roles like "CEO" with "he" and "nurse" with "she."
Here's the key takeaway: Every AI system you develop will either help combat these biases or inadvertently amplify them. There is no such thing as true neutrality in AI design.
I won't dwell on this further, but keep it in mind as you build and deploy systems. We've discussed this issue before—make sure you don't overlook it.