What typically causes computational bias?

Computational bias primarily arises from model assumptions and data issues. When developing AI models, the assumptions underlying their design can shape how they process information and make predictions. If these assumptions are flawed or do not accurately reflect the complexities of real-world data, it can lead to biased outcomes. For instance, if a model is trained on a dataset that is not representative of the population it is intended to serve, it may perpetuate or even amplify existing biases present in that data.

Additionally, the quality and quantity of the data used to train an AI model can significantly influence its performance and fairness. If the data contains historical biases, such as underrepresentation of certain groups, the model will likely learn and replicate those biases rather than provide equitable outcomes. This interplay between model assumptions and data issues is crucial for understanding the roots of computational bias, making it critical to address these factors during the AI development process to create fair and effective systems.

Other options like outdated technology, lack of training for users, and insufficient data privacy laws, while they might impact the overall effectiveness and ethical handling of AI systems, do not directly cause computational bias in the way that model assumptions and data-related issues do. Therefore, focusing on the inherent aspects of the model and the quality of the