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FAT Forensics: A Python Toolbox for Implementing and Deploying Fairness, Accountability and Transparency Algorithms in Predictive Systems

Sokol, Kacper; Hepburn, Alexander; Poyiadzi, Rafael; Clifford, Matthew; Santos-Rodriguez, Raul; Flach, Peter


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  <identifier identifierType="DOI">10.5281/zenodo.3833199</identifier>
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    <creator>
      <creatorName>Sokol, Kacper</creatorName>
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      <affiliation>University of Bristol</affiliation>
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    <creator>
      <creatorName>Hepburn, Alexander</creatorName>
      <givenName>Alexander</givenName>
      <familyName>Hepburn</familyName>
      <affiliation>University of Bristol</affiliation>
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    <creator>
      <creatorName>Poyiadzi, Rafael</creatorName>
      <givenName>Rafael</givenName>
      <familyName>Poyiadzi</familyName>
      <affiliation>University of Bristol</affiliation>
    </creator>
    <creator>
      <creatorName>Clifford, Matthew</creatorName>
      <givenName>Matthew</givenName>
      <familyName>Clifford</familyName>
      <affiliation>University of Bristol</affiliation>
    </creator>
    <creator>
      <creatorName>Santos-Rodriguez, Raul</creatorName>
      <givenName>Raul</givenName>
      <familyName>Santos-Rodriguez</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-9576-3905</nameIdentifier>
      <affiliation>University of Bristol</affiliation>
    </creator>
    <creator>
      <creatorName>Flach, Peter</creatorName>
      <givenName>Peter</givenName>
      <familyName>Flach</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-6857-5810</nameIdentifier>
      <affiliation>University of Bristol</affiliation>
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  <titles>
    <title>FAT Forensics: A Python Toolbox for Implementing and Deploying Fairness, Accountability and Transparency Algorithms in Predictive Systems</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <subjects>
    <subject>Fairness</subject>
    <subject>Accountability</subject>
    <subject>Transparency</subject>
    <subject>Artificial Intelligence</subject>
    <subject>Machine Learning</subject>
    <subject>Software</subject>
    <subject>Python Toolbox</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2020-05-19</date>
  </dates>
  <resourceType resourceTypeGeneral="Software"/>
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    <relatedIdentifier relatedIdentifierType="URL" relationType="IsSupplementTo">https://github.com/fat-forensics/fat-forensics/tree/0.1.0</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsDocumentedBy" resourceTypeGeneral="JournalArticle">10.21105/joss.01904</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="arXiv" relationType="IsDocumentedBy" resourceTypeGeneral="Preprint">arXiv:1909.05167</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3833198</relatedIdentifier>
  </relatedIdentifiers>
  <version>0.1.0</version>
  <rightsList>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;&lt;a href="https://fat-forensics.org/"&gt;FAT Forensics&lt;/a&gt; is a Python toolbox for evaluating fairness, accountability and transparency of predictive systems. It is built on top of &lt;a href="https://www.scipy.org/"&gt;SciPy&lt;/a&gt; and &lt;a href="https://www.numpy.org/"&gt;NumPy&lt;/a&gt;, and is distributed under the 3-Clause BSD license (new BSD).&lt;/p&gt;</description>
  </descriptions>
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