dc.contributor.author | O'Sullivan, Declan | en |
dc.contributor.author | Debruyne, Christophe | en |
dc.contributor.author | Lewis, David | en |
dc.date.accessioned | 2020-02-18T15:31:52Z | |
dc.date.available | 2020-02-18T15:31:52Z | |
dc.date.created | 30th January | en |
dc.date.issued | 2019 | en |
dc.date.submitted | 2019 | en |
dc.identifier.citation | Christophe Debruyne, Harshvardhan J. Pandit, David Lewis, Declan O'Sullivan, Towards Generating Policy-compliant Datasets, 13th IEEE International Conference on Semantic Computing, Long Beach, USA, 30th January, IEEE Computer Society, 2019, 199-203 | en |
dc.identifier.other | Y | en |
dc.description | PUBLISHED | en |
dc.description | Long Beach, USA | en |
dc.description.abstract | The development of intelligent (e.g., AI-based) applications
increasingly requires governance models and processes, as financial
legal sanctions are more and more being associated with
violation of policies. We propose an ontology representing the informed
consent that was collected by an organization and argue
how it can be used to assess a dataset prior its use in any type of
data processing activities. We demonstrate the utility of our ontology
using a particular scenario, where datasets are generated “just
in time” for a particular purpose such as sending newsletters. This
scenario shows how data processing activities can be managed to
in such a way as to support compliance verification. This paper
furthermore compares the contributions to related work and positions
it into prior work concerned with the broader problem of
prescribing and analyzing compliance. | en |
dc.format.extent | 199-203 | en |
dc.language.iso | en | en |
dc.publisher | IEEE Computer Society | en |
dc.rights | Y | en |
dc.subject | Governance | en |
dc.subject | Consent representation | en |
dc.subject | Compliance analysis | en |
dc.subject | Semantic mappings | en |
dc.title | Towards Generating Policy-compliant Datasets | en |
dc.title.alternative | 13th IEEE International Conference on Semantic Computing | en |
dc.type | Conference Paper | en |
dc.type.supercollection | scholarly_publications | en |
dc.type.supercollection | refereed_publications | en |
dc.identifier.peoplefinderurl | http://people.tcd.ie/osulldps | en |
dc.identifier.peoplefinderurl | http://people.tcd.ie/debruync | en |
dc.identifier.peoplefinderurl | http://people.tcd.ie/delewis | en |
dc.identifier.rssinternalid | 194490 | en |
dc.identifier.doi | https://doi.org/10.1109/ICOSC.2019.8665631 | en |
dc.identifier.doi | https://doi.org/10.5281/zenodo.3246448 | en |
dc.rights.ecaccessrights | openAccess | |
dc.subject.TCDTheme | Digital Engagement | en |
dc.subject.TCDTag | Knowledge and data engineering | en |
dc.identifier.rssuri | https://chrdebru.github.io/papers/2019-icsc-preprint.pdf | en |
dc.identifier.orcid_id | 0000-0003-1090-3548 | en |
dc.contributor.sponsor | Science Foundation Ireland (SFI) | en |
dc.contributor.sponsorGrantNumber | 13/RC/2106 | en |
dc.identifier.uri | https://chrdebru.github.io/papers/2019-icsc-preprint.pdf | |
dc.identifier.uri | http://hdl.handle.net/2262/91564 | |