Enhancing non-expert understanding of Artificial Intelligence solutions through an ontology-based approach
dc.contributor.author | Castratori, Sarah Graziella | |
dc.contributor.mentor | Laurenzi, Emanuele | |
dc.date.accessioned | 2024-12-03T19:39:14Z | |
dc.date.available | 2024-12-03T19:39:14Z | |
dc.date.issued | 2024 | |
dc.description.abstract | To drive AI understanding and adoption, the vast terms, concepts, and definitions around the many different dimensions and aspects of AI must be demystified for a broader non-tech-focused audience. Such as professionals with higher education who work in a digitalized business environment but have no education in computer science or similar fields. These in this context, called “non-experts” of AI, who are decision-makers and contributors in various industries, could profit from an easy, manageable, and trustworthy way to level up their own knowledge of the AI field. Specifically in the context of relevant business challenges and terms. | |
dc.identifier.uri | https://irf.fhnw.ch/handle/11654/49263 | |
dc.language.iso | en | |
dc.publisher | Hochschule für Wirtschaft FHNW | |
dc.spatial | Olten | |
dc.subject.ddc | 330 - Wirtschaft | |
dc.title | Enhancing non-expert understanding of Artificial Intelligence solutions through an ontology-based approach | |
dc.type | 11 - Studentische Arbeit | |
dspace.entity.type | Publication | |
fhnw.InventedHere | Yes | |
fhnw.StudentsWorkType | Master | |
fhnw.affiliation.hochschule | Hochschule für Wirtschaft FHNW | de_CH |
fhnw.affiliation.institut | Master of Science | |
relation.isMentorOfPublication | 4a2b6cad-6ed6-4355-a377-e408a177b079 | |
relation.isMentorOfPublication.latestForDiscovery | 4a2b6cad-6ed6-4355-a377-e408a177b079 |