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      "statement": "# Data Shapley: learner-dependent data valuation and practical estimators\n\nApplies cooperative-game marginal contribution valuation to training data for a fixed learner and utility, with Monte Carlo, truncation and gradient-based approximation strategies.\n\n## Scientific scope\n\nInherits the Shapley uniqueness result rather than inventing it; values depend on dataset, learner and utility, not universal monetary value. Exact computation is exponential; truncation and gradient shortcuts are not exact unbiased Shapley values. Only general ML/image/synthetic scope is curated, excluding biomedical applications.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nAmirata Ghorbani; James Zou\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [Data Shapley: Equitable Valuation of Data for Machine Learning](https://arxiv.org/pdf/1904.02868v1)\n",
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