{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/data-shapley-equitable-valuation-of-data-for","title":"Data Shapley: Equitable Valuation of Data for Machine Learning","arxiv_id":"1904.02868","date":"2019-04-05","proceeding":null,"authors":["Amirata Ghorbani","James Zou"],"abstract":"As data becomes the fuel driving technological and economic growth, a fundamental challenge is how to quantify the value of data in algorithmic predictions and decisions. For example, in healthcare and consumer markets, it has been suggested that individuals should be compensated for the data that they generate, but it is not clear what is an equitable valuation for individual data. In this work, we develop a principled framework to address data valuation in the context of supervised machine learning. Given a learning algorithm trained on $n$ data points to produce a predictor, we propose data Shapley as a metric to quantify the value of each training datum to the predictor performance. Data Shapley value uniquely satisfies several natural properties of equitable data valuation. We develop Monte Carlo and gradient-based methods to efficiently estimate data Shapley values in practical settings where complex learning algorithms, including neural networks, are trained on large datasets. In addition to being equitable, extensive experiments across biomedical, image and synthetic data demonstrate that data Shapley has several other benefits: 1) it is more powerful than the popular leave-one-out or leverage score in providing insight on what data is more valuable for a given learning task; 2) low Shapley value data effectively capture outliers and corruptions; 3) high Shapley value data inform what type of new data to acquire to improve the predictor.","url_abs":"https://arxiv.org/abs/1904.02868v2","url_pdf":"https://arxiv.org/pdf/1904.02868v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"data-shapley-equitable-valuation-of-data-for","repo_url":"https://github.com/amiratag/DataShapley","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"data-shapley-equitable-valuation-of-data-for","repo_url":"https://github.com/GISH123/Cathay-Holdings-CIP-Projects-for-Interpretable-Machine-Learning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"data-shapley-equitable-valuation-of-data-for","repo_url":"https://github.com/Weixin-Liang/HERALD","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"data-shapley-equitable-valuation-of-data-for","repo_url":"https://github.com/Weixin-Liang/dialog_evaluation_CMADE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"data-shapley-equitable-valuation-of-data-for","repo_url":"https://github.com/nathanieljevans/DVGS","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"data-shapley-equitable-valuation-of-data-for","repo_url":"https://github.com/aai-institute/pyDVL","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"LGPL-3.0"}}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"data-valuation","task_name":"Data Valuation"},{"task_slug":"task-2","task_name":"Task 2"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.02868","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.02868"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/aai-institute/pyDVL","reach":{"status":"ok","spdx":"LGPL-3.0"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/Weixin-Liang/dialog_evaluation_CMADE","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/Weixin-Liang/HERALD","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/GISH123/Cathay-Holdings-CIP-Projects-for-Interpretable-Machine-Learning","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/nathanieljevans/DVGS","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/amiratag/DataShapley","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":2},"by_repo_kind":{"official":{"samples":2,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"eb3cf95aab441565","entry":"is_fitted","repo":"amiratag/DataShapley","repo_kind":"official","path":"shap_utils.py","file_url":"https://github.com/amiratag/DataShapley/blob/HEAD/shap_utils.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"eb3cf95aab441565"}},{"code_sha256_prefix":"cd370e63cb94a43b","entry":"is_integer","repo":"amiratag/DataShapley","repo_kind":"official","path":"shap_utils.py","file_url":"https://github.com/amiratag/DataShapley/blob/HEAD/shap_utils.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"cd370e63cb94a43b"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}