Doing the Data Work to Mitigate Data Cascades

Data cascades are compounding negative effects that arise in machine learning systems due to underlying issues in the data. These issues can amplify throughout the ML pipeline, leading to poor model performance, biased outcomes, and technical debt. Mitigating these cascades requires diligent 'data work,' which involves robust processes for data collection, labeling, and quality assurance to ensure the integrity of the entire system.