Computing a Data Dividend
Quality data is a fundamental contributor to success in statistics and machine learning. If a statistical assessment or machine learning leads to decisions that create value, data contributors may want a share of that value. This paper presents methods to assess the value of individual data samples,...
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| creator | Bax, Eric |
| description | Quality data is a fundamental contributor to success in statistics and
machine learning. If a statistical assessment or machine learning leads to
decisions that create value, data contributors may want a share of that value.
This paper presents methods to assess the value of individual data samples, and
of sets of samples, to apportion value among different data contributors. We
use Shapley values for individual samples and Owen values for combined samples,
and show that these values can be computed in polynomial time in spite of their
definitions having numbers of terms that are exponential in the number of
samples. |
| doi_str_mv | 10.48550/arxiv.1905.01805 |
| format | Article |
| fullrecord | <record><control><sourceid>arxiv_GOX</sourceid><recordid>TN_cdi_arxiv_primary_1905_01805</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1905_01805</sourcerecordid><originalsourceid>FETCH-LOGICAL-a675-b44549829d2de40037c640dd646126f44ece745aabf23eb42fa63b7c816e1a6e3</originalsourceid><addsrcrecordid>eNotzrsOgjAYhuEuDga9ACa5AbCHvwVGg8eExIWd_KXFNBEkiETvXkWX792-PIT4jEaQSEnX2D_dGLGUyoiyhMo58bNb0z0G114CDLY4fMaNztjWLMisxuvdLv_1SLHfFdkxzM-HU7bJQ1SxDDWAhDThqeHGAqUirhRQYxQoxlUNYCsbg0TUNRdWA69RCR1XCVOWobLCI6vf7WQru9412L_Kr7GcjOINXsA0rw</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Computing a Data Dividend</title><source>arXiv.org</source><creator>Bax, Eric</creator><creatorcontrib>Bax, Eric</creatorcontrib><description>Quality data is a fundamental contributor to success in statistics and
machine learning. If a statistical assessment or machine learning leads to
decisions that create value, data contributors may want a share of that value.
This paper presents methods to assess the value of individual data samples, and
of sets of samples, to apportion value among different data contributors. We
use Shapley values for individual samples and Owen values for combined samples,
and show that these values can be computed in polynomial time in spite of their
definitions having numbers of terms that are exponential in the number of
samples.</description><identifier>DOI: 10.48550/arxiv.1905.01805</identifier><language>eng</language><subject>Computer Science - Computer Science and Game Theory ; Computer Science - Computers and Society ; Quantitative Finance - Economics ; Statistics - Computation</subject><creationdate>2019-05</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/1905.01805$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.1905.01805$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Bax, Eric</creatorcontrib><title>Computing a Data Dividend</title><description>Quality data is a fundamental contributor to success in statistics and
machine learning. If a statistical assessment or machine learning leads to
decisions that create value, data contributors may want a share of that value.
This paper presents methods to assess the value of individual data samples, and
of sets of samples, to apportion value among different data contributors. We
use Shapley values for individual samples and Owen values for combined samples,
and show that these values can be computed in polynomial time in spite of their
definitions having numbers of terms that are exponential in the number of
samples.</description><subject>Computer Science - Computer Science and Game Theory</subject><subject>Computer Science - Computers and Society</subject><subject>Quantitative Finance - Economics</subject><subject>Statistics - Computation</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotzrsOgjAYhuEuDga9ACa5AbCHvwVGg8eExIWd_KXFNBEkiETvXkWX792-PIT4jEaQSEnX2D_dGLGUyoiyhMo58bNb0z0G114CDLY4fMaNztjWLMisxuvdLv_1SLHfFdkxzM-HU7bJQ1SxDDWAhDThqeHGAqUirhRQYxQoxlUNYCsbg0TUNRdWA69RCR1XCVOWobLCI6vf7WQru9412L_Kr7GcjOINXsA0rw</recordid><startdate>20190505</startdate><enddate>20190505</enddate><creator>Bax, Eric</creator><scope>ADEOX</scope><scope>AKY</scope><scope>EPD</scope><scope>GOX</scope></search><sort><creationdate>20190505</creationdate><title>Computing a Data Dividend</title><author>Bax, Eric</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a675-b44549829d2de40037c640dd646126f44ece745aabf23eb42fa63b7c816e1a6e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Computer Science - Computer Science and Game Theory</topic><topic>Computer Science - Computers and Society</topic><topic>Quantitative Finance - Economics</topic><topic>Statistics - Computation</topic><toplevel>online_resources</toplevel><creatorcontrib>Bax, Eric</creatorcontrib><collection>arXiv Economics</collection><collection>arXiv Computer Science</collection><collection>arXiv Statistics</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Bax, Eric</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Computing a Data Dividend</atitle><date>2019-05-05</date><risdate>2019</risdate><abstract>Quality data is a fundamental contributor to success in statistics and
machine learning. If a statistical assessment or machine learning leads to
decisions that create value, data contributors may want a share of that value.
This paper presents methods to assess the value of individual data samples, and
of sets of samples, to apportion value among different data contributors. We
use Shapley values for individual samples and Owen values for combined samples,
and show that these values can be computed in polynomial time in spite of their
definitions having numbers of terms that are exponential in the number of
samples.</abstract><doi>10.48550/arxiv.1905.01805</doi><oa>free_for_read</oa></addata></record> |
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| identifier | DOI: 10.48550/arxiv.1905.01805 |
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| language | eng |
| recordid | cdi_arxiv_primary_1905_01805 |
| source | arXiv.org |
| subjects | Computer Science - Computer Science and Game Theory Computer Science - Computers and Society Quantitative Finance - Economics Statistics - Computation |
| title | Computing a Data Dividend |
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