What are some common data quality issues in AI?

What are some common data quality issues in AI?

Common data quality issues in AI include missing data, duplicate data, inconsistent data formats, and biased data.

Can you explain how missing data affects AI models?

Missing data can lead to inaccurate predictions, as the model may not have enough information to learn effectively.

What are the consequences of using biased data?

Using biased data can result in unfair or discriminatory outcomes, as the AI may reinforce existing biases in the data.

How can we improve data quality for AI?

Improving data quality can be achieved through data cleaning, validation, and ensuring diverse and representative datasets.