What if it is not fair?

AI ethics expert Timnit Gebru has stated that "AI systems reflect and amplify existing societal inequalities if not designed carefully." This shows that AI can be unfair, as biases in data can lead to discrimination. However, this is not inevitable. To reduce these risks, transparency must be improved, data must be diversified, and regulations must be established to decrease biases in algorithms. With these measures, AI can become more fair and more equitable.

One example of this unfairness was seen in an Amazon hiring system, which discriminated against women because historical data favored men in the tech industry. This case demonstrates how biases in data can lead to unfair decisions. To avoid these problems, companies must audit their models and correct flaws before implementation.

Another case occurred in the U.S. judicial system, where the COMPAS software more frequently labeled African Americans as high-risk compared to white individuals. This negatively impacted many people's lives. To fix this, AI systems in sensitive areas must undergo rigorous testing and human oversight.

Some argue that AI merely reflects reality and is not unfair in itself. However, this argument ignores that data is not neutral and that, if left unchecked, it can reinforce inequalities. The key is not just accepting the data but designing models that interpret it more fairly.

In conclusion, AI can be unfair, but with proper regulations and constant audits, it can become fairer and more beneficial for everyone.