It’s High Time ML Community Looked Into Effects of Data Cascades

“Model drifts are more common when models in high-stakes domains, such as air quality sensing or ultrasound scanning due to lack of curated datasets.” When AI models are applied in high-stakes domains like health and industrial automation, data quality suddenly becomes a significant aspect of the whole pipeline. Models in the real world are prone to many vulnerabilities that go undetected in a controlled environment. For instance, even the seasons have their say in model outcomes. Wind can unexpectedly move image sensors in deployment, a form of cascade. Google’s research showed even a small drop of oil or water can affect data that could be used to train a cancer prediction model. These small deviations can go unnoticed for 2-3 years before they show up in production. This is why