Software systems that learn from data are being deployed in increasing numbers in real world application scenarios. It is a difficult and tedious task to ensure at development time that the end-to-end ML pipelines for such applications adhere to …
Software systems that learn from user data with machine learning (ML) have become ubiquitous over the last years. Recent law such as the General Data Protection Regulation (GDPR) requires organisations that process personal data to delete user data …
Machine Learning (ML) is increasingly used to automate impactful decisions, and the risks arising from this wide-spread use are garnering attention from policymakers, scientists, and the media. ML applications are often very brittle with respect to …
Machine Learning (ML) is increasingly used to automate impactful decisions, and the risks arising from this wide-spread use are garnering attention from policy makers, scientists, and the media. ML applications are often very brittle with respect to …
Modern companies and institutions rely on data to guide every single decision. Missing or incorrect information seriously compromises any decision process. In previous work, we presented Deequ, a Spark-based library for automating the verification of …
Modern machine learning (ML) systems are comprised of complex ML pipelines which typically have many implicit assumptions about the data they consume (e.g., about the scales of variables, the presence of missing values or the dictionary of …