LOA/Fixy

Learned Observation Assertions is an abstraction designed to find errors in ML deployment pipelines with as little manual specification of error types as possible. Fixy is a system for finding errors in perception data, implementing the LOA abstraction. It leverages existing organizational resources, such as existing (possibly noisy) labeled datasets or previously trained ML models, to learn a probabilistic model for finding errors in human- or model-generated labels.