LArtificial intelligence (AI) has revolutionized the way we interact with the world, and its impact is undeniable. However, it's also true that, in many cases, this technology reflects deeply rooted prejudices in society. Cases of bias related to skin color, nationality, and gender have raised alarms about how AI could perpetuate or even amplify inequalities. Today, Allmarket teaches you how to avoid biases and gives you real-life examples.
The problem lies in the data. AI models are telegram data with information gathered from human sources, and, as we well know, humans are not exempt from biases. This means that AI systems can inherit those biases and apply them in scenarios such as personnel selection, access to credit, or facial recognition.
Examples of biases in AI
One of the most notable cases of racial bias occurred with facial recognition systems. High-profile tech companies found that their algorithms had significantly higher error rates when identifying dark-skinned people compared to those with light skin. This problem not only creates inequities , but also has serious implications for security and privacy .