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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Mara, Graziani | - |
| dc.contributor.author | Lidia, Dutkiewicz | - |
| dc.contributor.author | Davide, Calvaresi | - |
| dc.date.accessioned | 2023-03-30T06:57:47Z | - |
| dc.date.available | 2023-03-30T06:57:47Z | - |
| dc.date.issued | 2023 | - |
| dc.identifier.uri | https://link.springer.com/article/10.1007/s10462-022-10256-8 | - |
| dc.identifier.uri | https://dlib.phenikaa-uni.edu.vn/handle/PNK/7335 | - |
| dc.description | CC BY | vi |
| dc.description.abstract | Since its emergence in the 1960s, Artificial Intelligence (AI) has grown to conquer many technology products and their fields of application. Machine learning, as a major part of the current AI solutions, can learn from the data and through experience to reach high performance on various tasks. This growing success of AI algorithms has led to a need for interpretability to understand opaque models such as deep neural networks. Various requirements have been raised from different domains, together with numerous tools to debug, justify outcomes, and establish the safety, fairness and reliability of the models. | vi |
| dc.language.iso | en | vi |
| dc.publisher | Springer | vi |
| dc.subject | Artificial Intelligence | vi |
| dc.subject | Machine learning | vi |
| dc.title | A global taxonomy of interpretable AI: unifying the terminology for the technical and social sciences | vi |
| dc.type | Book | vi |
| Appears in Collections | OER - Công nghệ thông tin | |
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