Conference paper Open Access

Connecting diverse public sector values with the procurement of machine learning systems.

Veale, Michael

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  <identifier identifierType="DOI">10.5281/zenodo.571786</identifier>
      <creatorName>Veale, Michael</creatorName>
      <affiliation>University College London</affiliation>
    <title>Connecting diverse public sector values with the procurement of machine learning systems.</title>
    <subject>machine learning</subject>
    <subject>public policy</subject>
    <subject>public administration</subject>
    <subject>public procurement</subject>
    <date dateType="Issued">2017-05-04</date>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
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    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.603951</relatedIdentifier>
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    <rights rightsURI="">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
    <description descriptionType="Abstract">&lt;p&gt;Increasing interest in using machine learning systems for decision making and support in the public sector has raised questions as to how these technologies can be designed, implemented and managed responsibly. This short discussion paper describes some relevant social and technical potentials and perils of machine learning by relating them to different groups of public sector values outlined in the public administration literature. Practitioners may find this structure useful to help them understand different dimensions of responsibility they may wish to consider if they are considering using these technologies, and how they link to developing work and tools in the field. &lt;/p&gt;</description>
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