How does Biz-Tech Analytics ensure high-quality labels?

Our Basics

Rohan Khanna

Last Update 2 anni fa

At Biz-Tech Analytics we believe a data-centric approach to data annotation and labeling makes your AI system easier to train and more capable of producing consistent and accurate results. We take steps to ensure high-quality labels by:


- Before each project, customers are prompted to submit a small collection of samples with instructions, which are annotated by us and approved by you with a degree of back and forth. Annotation instructions that we get from you are further refined based on this exchange and the sample annotations are included as a part of the instructions.


- Furthermore, we make our labels consistent by defining rules and deterministic functions clearly in the instruction set, using your data scientists’ and domain experts’ insights. We are also constantly iterating on our instruction set and adding examples to further reduce inconsistencies.


- We focus on consensus labeling to spot inconsistencies. Each instance of data is labeled by at least two labelers, and disagreements are handled by a third reviewer while providing feedback to annotators. Additionally, the edge cases that are causing ambiguity and inconsistency are added to the instruction set.


- Our labelers rely on their intuition to flag noisy examples which are sent for review.


- At the time of submission of a set of labeled data, a project manager randomly selects a certain percentage of the labeled data for spot-checking and makes any final changes to this data. Depending on the precision and recall rate the whole cycle may start again.

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