arXiv:2605.21033v1 Announce Type: new Abstract: Data valuation, the task of quantifying the contribution of individual data points to model performance, has emerged as a fundamental challenge in machine learning. Game-theoretic approaches, such as the Banzhaf value, offer principled frameworks for fair data valuation; however, they suffer from exponential computational complexity. We address this challenge by developing efficient algorithms specifically tailored for computing Banzhaf values in $k$-nearest neighbor ($k$NN) classifiers. We first establish the theoretical hardness of the problem
The increasing focus on fair and interpretable AI models, particularly in data valuation, drives the need for more efficient computational methods that were previously intractable.
Efficient data valuation techniques are critical for improving model performance, ensuring fairness, and managing data costs in machine learning, impacting all sectors using AI.
The development of efficient algorithms for Banzhaf values in k-NN classification makes game-theoretic data valuation more practically applicable, opening new avenues for data curation and model explainability.
- · Machine Learning Developers
- · Data Scientists
- · AI Ethics Researchers
- · Data-driven Enterprises
- · Inefficient Data Valuation Methods
- · Companies with Poor Data Hygiene
More accurate and fair attribution of data contributions to model outcomes becomes feasible.
Improved data quality and curation processes emerge as enterprises can better identify and prioritize valuable data points.
New data marketplaces and economic models could develop based on transparent and auditable data valuation.
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Read at arXiv cs.LG