Editable machine learning models? A rule-based framework for user studies of explainability

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61384399%3A31140%2F20%3A00055436" target="_blank" >RIV/61384399:31140/20:00055436 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/article/10.1007/s11634-020-00419-2" target="_blank" >https://link.springer.com/article/10.1007/s11634-020-00419-2</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s11634-020-00419-2" target="_blank" >10.1007/s11634-020-00419-2</a>

Result language
angličtina
Original language name
Editable machine learning models? A rule-based framework for user studies of explainability
Original language description
Main topics of the document: rule learning; user experiment; crowdsourcing; explainable artificial intelligence; cognitive computing; legal compliance
Czech name
—
Czech description
—

Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Result description