cv.bib

@InProceedings{Adhikari2022,
  author    = {Adhikari, Ajaya and Wenink, Edwin and van der Waa, Jasper and Bouter, Cornelis and Tolios, Ioannis and Raaijmakers, Stephan},
  booktitle = {Proceedings of the 15th International Conference on PErvasive Technologies Related to Assistive Environments},
  date      = {2022},
  title     = {Towards FAIR Explainable AI: A Standardized Ontology for Mapping XAI Solutions to Use Cases, Explanations, and AI Systems},
  doi       = {10.1145/3529190.3535693},
  isbn      = {9781450396318},
  location  = {Corfu, Greece},
  pages     = {562–568},
  publisher = {Association for Computing Machinery},
  series    = {PETRA '22},
  abstract  = {Several useful taxonomies have been published that survey the eXplainable AI (XAI) research field. However, these taxonomies typically do not show the relation between XAI solutions and several use case aspects, such as the explanation goal or the task context. In order to better connect the field of XAI research with concrete use cases and user needs, we designed the ASCENT (Ai System use Case Explanation oNTology) framework, which is a new ontology and corresponding metadata standard with three complementary modules for different aspects of an XAI solution: one for aspects of AI systems, another for use case aspects, and yet another for explanation properties. The descriptions of XAI solutions in this framework include whether the XAI solution has a positive, negative, inconclusive or unresearched relation with use case elements. Descriptions in ASCENT thus emphasize the (user) evaluation of XAI solutions in order to support finding validated practices for application in industry, as well as being helpful for identifying research gaps. Describing XAI solutions according to the proposed common metadata standard is an important step towards the FAIR (Findable, Accessible, Interoperable, Reusable) usage of XAI solutions.},
  address   = {New York, NY, USA},
  keywords  = {FAIR, XAI ontology, user-centered, ASCENT},
  numpages  = {7},
}

@Article{Wenink2020,
  author       = {Wenink, Edwin},
  date         = {2020},
  journaltitle = {Turning Magazine},
  title        = {Tech Giants will battle over your health data},
  pages        = {12-13},
  volume       = {2: AI \& Health},
}

@Article{Wenink,
  author       = {Wenink, Edwin},
  date         = {2013},
  journaltitle = {Splijtstof},
  title        = {Deconstructie in werking},
  editor       = {Leijssenaar, Bas},
  number       = {2},
  pages        = {49-58},
  url          = {https://adoc.pub/splijtstof-jaargang-42-nummer-2-kerstnummer-2013.html},
  volume       = {42},
}

@InProceedings{Wenink2023,
  author     = {Wenink, Edwin and Kwisthout, Johan and van Engers, Tom},
  booktitle  = {Legal Informatics as Science of Legal Methods. Proceedings of the 26th International Legal Informatics Symposium},
  date       = {2023},
  title      = {Punishment Extraction from Dutch criminal cases in Courts of First Instance},
  editor     = {Schweighofer, Eric and Zanol, Jakob and Eder, Stefan},
  eventtitle = {IRIS 2023},
  isbn       = {978-3-98595-714-9},
  location   = {Bern, Swiss},
  pages      = {233-241},
  publisher  = {Weblaw},
  venue      = {Salzburg, Austria},
  abstract   = {This paper explores the merits of a pattern- and rule-based approach for the automated extraction of punishments from Dutch criminal cases in courts of fi rst instance. Automated extraction of case outcomes leverages the increasing amount of information becoming available through digital technologies and aids the creation of big data sets for work in legal informatics and AI & Law. This work addresses domain-specifi c challenges, in particular that Dutch criminal case decisions may impose a single combined sentence for multiple facts or impose multiple sentences in the same decision. Manual evaluation of the developed method shows that the use of interpretable methods is a viable approach in the legal domain.},
  comment    = {Location uitgever is Bern; locatie van conferentie was Salzburg, Austria. Welke neem ik?},
}