arXiv:2606.06892v1 Announce Type: new Abstract: Scalable data attribution methods typically assign isolated utility scores to individual training examples. This prevalent additive assumption fundamentally fails to capture critical subset dynamics, including data redundancy and complementary coverage. In this work, we reframe attribution as subset-level counterfactual utility prediction and introduce GRASP, an interaction-aware surrogate. Grounded in a theoretical smoothness lower bound, GRASP explicitly models subset interactions through a quadratic geometric penalty. To achieve pretraining-sc
The increasing complexity and scale of AI models necessitate more advanced methods for understanding and attributing the impact of training data, particularly for ethical AI development and regulatory compliance.
This development allows for a more nuanced understanding of how data subsets contribute to AI model performance, moving beyond simplistic individual data point attribution.
AI data attribution will shift from isolated utility scores to more sophisticated, interaction-aware methods that consider data redundancy and complementary coverage.
- · AI developers
- · Ethical AI auditors
- · Data scientists
- · Compliance officers
- · Companies with opaque data pipelines
- · Developers relying on black-box attribution
Improved debugging and optimization of large-scale AI models due to better data insights.
Enhanced trustworthiness and explainability of AI systems, facilitating wider adoption in sensitive domains.
New regulatory frameworks may emerge, requiring explicit demonstration of dataset interaction and attribution for critical AI applications.
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Read at arXiv cs.LG