Why trying to remove bias from AI is a terrible approach
Listen to this article:
It is often said that artificial intelligence algorithms can learn biases from the data they are trained on. This can lead some to believe that the solution is to go in and fix the biases in the algorithms manually. However, this is both unnecessary and impractical.
First and foremost, it is essential to understand that the biases in AI algorithms do not necessarily reflect the personal beliefs of the developers or the corporations that create them. Instead, they are a result of the biases that exist in the data that the algorithms are trained on. This data is a product of thousands of years of human history and cultural norms. It is unrealistic to expect that we can go in and manually remove all of the biases from this data.
Furthermore, the idea that AI algorithms should be completely bias-free is misguided. Bias is a natural part of human decision-making and is not inherently evil or problematic. In many cases, biases can actually be helpful in making decisions more quickly and efficiently. The real issue is not the presence of bias but rather the unintended consequences that can result from blindly relying on biased algorithms.
Another issue with trying to remove biases from AI algorithms manually is that it is an impractical and challenging task. Neural networks are complex, non-linear systems that can have millions of parameters. Manually trying to remove biases from such a system is a Herculean task that would require a significant amount of time and resources.
The idea that we should try to remove biases from AI algorithms manually is not only unrealistic but also misguided. Instead of trying to remove bias, we should focus on creating algorithms that are transparent, explainable, and accountable. This means that we need to be mindful of the potential biases that exist in the data we use to train these algorithms and take steps to mitigate any unintended consequences. By doing so, we can create AI systems that are better equipped to handle the challenges of our complex and diverse world.
