🎲 AI's hidden prejudice: your system's biases lead to unfair decisions

AI data and trends for business leaders | AI systems series

AI once hailed as a beacon of objectivity, is increasingly recognized as a conduit for human biases.

These biases, often deeply ingrained in the data used to train AI models, can lead to unfair and discriminatory outcomes.

The adage "garbage in, garbage out" is particularly relevant to AI. If the data used to train an AI model is biased, the model will inevitably learn those biases.

For instance, a facial recognition system trained on a dataset primarily consisting of white faces may struggle to identify people of color accurately.

Similarly, a language model trained on text data that contains harmful stereotypes may perpetuate those stereotypes in its output.

Let’s try to better understand through facts.