What is a risk associated with AI that involves the distortion of data leading to incorrect outputs?

Model drift is a phenomenon that occurs when an AI model’s performance degrades over time due to changes in the underlying data patterns, resulting in incorrect outputs. This risk is particularly significant in dynamic environments where the data landscape can change rapidly. As the model continues to generate outputs based on outdated or irrelevant data, the decisions made could lead to erroneous conclusions or actions, exacerbating the impact of the model’s predictions.

Understanding model drift is crucial for maintaining the accuracy and reliability of AI systems. It highlights the importance of continuous monitoring and updating of models to deal with evolving data distributions. By regularly assessing the performance of an AI model and recalibrating it to reflect current data trends, organizations can mitigate the risk of producing misleading results and ensure that their AI systems remain effective over time.