DeMix: Debugging Training Data with Mixed Data Error Types by Investigating Influence Vectors
arXiv:2606.11616v1 Announce Type: new Abstract: High-quality training data is essential for the success of machine learning models. However, real-world datasets often contain mixed types of errors arising from systematic flaws in data preparation pipelines, including label errors, feature errors, and spurious correlations. Effective debugging of training data requires both detecting erroneous samples and identifying their specific error types to enable targeted repair, yet existing data cleaning and attribution methods fail to adequately address this dual requirement. In this paper, we propose
The increasing complexity and scale of AI models are exacerbating the challenges of data quality, making advanced debugging tools like DeMix critical for real-world deployment and reliability.
Effective debugging of training data is a foundational layer for robust AI development, directly impacting model performance, trustworthiness, and the economic viability of AI applications.
This advancement provides a more systematic and granular approach to identifying and rectifying data errors, moving beyond general data cleaning to targeted repair based on specific error types.
- · AI developers
- · Data scientists
- · MLOps platforms
- · Industries deploying AI
- · Companies with low data quality standards
- · Inefficient manual data debugging processes
Machine learning models will become more reliable and performant due to cleaner training data.
The cost and time associated with deploying AI systems will decrease as data debugging becomes more efficient, accelerating AI adoption across sectors.
Higher data quality standards could lead to new regulatory frameworks for AI systems, emphasizing data provenance and error traceability.
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Read at arXiv cs.LG