AI is not inherently biased, but the data used to train AI models can be biased. AI models learn from the data they are trained on, so if the data contains biases, the model will learn and replicate those biases in its decisions and opinions.
There are various sources of bias in AI, including biased training data, biased algorithms, and biased model evaluation. Biased training data can result from historical injustices and social inequalities, as well as intentional or unintentional human biases in data collection and labeling. Biased algorithms can be due to the design choices and assumptions made by the developers of the AI model. Biased model evaluation can occur when the performance metrics used to evaluate the AI model do not account for certain groups or factors.
Regarding opinions about corporations, AI models do not have inherent opinions, as they are just mathematical models that process data. However, the data used to train AI models can reflect the opinions and biases of t