In AI, data correction, also known as data cleaning or data scrubbing, refers to the process of identifying and correcting errors, inconsistencies, and inaccuracies in datasets to improve their quality and reliability. This process is crucial because AI algorithms are trained on data, and if the data is flawed, the AI models will produce inaccurate or biased results.