Trust by Design: How Glass Box AI Shapes User Acceptance through Explainability
| dc.contributor.author | Da Silvia, Danilo Riberio | |
| dc.contributor.mentor | Karg, Jona | |
| dc.contributor.mentor | Misyura, Ilya | |
| dc.contributor.partner | Hochschule für Wirtschaft FHNW | |
| dc.date.accessioned | 2025-12-15T13:38:51Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | AI is increasingly used in high-stakes domains such as healthcare, yet decision processes are often opaque. This study asks whether explainability improves user trust and acceptance. Building on Karg’s Path Model of Trust in AI, it focuses on process-level explainability as a core design element. | |
| dc.identifier.uri | https://irf.fhnw.ch/handle/11654/54824 | |
| dc.language.iso | en | |
| dc.publisher | Hochschule für Wirtschaft FHNW | |
| dc.spatial | Basel | |
| dc.subject.ddc | 330 - Wirtschaft | |
| dc.title | Trust by Design: How Glass Box AI Shapes User Acceptance through Explainability | |
| dc.type | 11 - Studentische Arbeit | |
| dspace.entity.type | Publication | |
| fhnw.InventedHere | Yes | |
| fhnw.StudentsWorkType | Bachelor | |
| fhnw.affiliation.hochschule | Hochschule für Wirtschaft FHNW | de_CH |
| fhnw.affiliation.institut | Bachelor of Science | de_CH |
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