Badgers: generating data quality deficits with Python

TL;DR

Badgers is an open-source Python library designed to generate various data quality deficits across multiple data modalities, aiding in the experimental evaluation of AI and ML systems' robustness.

Contribution

The paper introduces badgers, a versatile tool for creating controlled data quality issues, which is extensible and supports different data types for testing AI models.

Findings

Supports multiple data modalities including tabular, time-series, and text.

Enables systematic testing of AI models against data quality deficits.

Open-source with comprehensive documentation.

Abstract

Generating context specific data quality deficits is necessary to experimentally assess data quality of data-driven (artificial intelligence (AI) or machine learning (ML)) applications. In this paper we present badgers, an extensible open-source Python library to generate data quality deficits (outliers, imbalanced data, drift, etc.) for different modalities (tabular data, time-series, text, etc.). The documentation is accessible at https://fraunhofer-iese.github.io/badgers/ and the source code at https://github.com/Fraunhofer-IESE/badgers