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# data-centric-ml Create tools to explore data before and after training to identify artifacts. These can be outliers, subsets of the data. The goal is to help identify them automatically to understand the limitations of the ML models. Papers: 1. [Hidden Stratification Causes Clinically Meaningful Failures in Machine Learning for Medical Imaging](https://arxiv.org/pdf/1909.12475.pdf) 2. [Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics](https://arxiv.org/pdf/2009.10795.pdf) 3. [Metadata Archaeology: Unearthing Data Subsets by Leveraging Training Dynamics](https://arxiv.org/pdf/2209.10015.pdf) 4. [TRIAGE: Characterizing and auditing training data for improved regression](https://arxiv.org/pdf/2310.18970.pdf) 5. [Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data](https://arxiv.org/pdf/2210.13043.pdf) 6. [Data Shapley: Equitable Valuation of Data for Machine Learning](https://arxiv.org/pdf/1904.02868.pdf)

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