Advances in Explainable Artificial Intelligence
Machine Learning (ML)-based Artificial Intelligence (AI) algorithms have the capability to learn from known examples, creating various abstract representations and models. When applied to unfamiliar examples, these algorithms can perform a range of tasks, including classification, regression, and fo...
Zapisane w:
| Format: | Online |
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| Język: | angielski |
| Wydane: |
MDPI - Multidisciplinary Digital Publishing Institute
2024
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| Hasła przedmiotowe: | |
| Dostęp online: | ONIX_20240514_9783725802838_215 |
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| _version_ | 1869518884961255424 |
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| collection | Directory of Open Access Books |
| description | Machine Learning (ML)-based Artificial Intelligence (AI) algorithms have the capability to learn from known examples, creating various abstract representations and models. When applied to unfamiliar examples, these algorithms can perform a range of tasks, including classification, regression, and forecasting, to name a few. Frequently, these highly effective ML representations are challenging to comprehend, especially in the case of Deep Learning models, which may involve millions of parameters. However, in many applications, it is crucial for stakeholders to grasp the reasoning behind the system's decisions to utilize them more effectively. This necessity has prompted extensive research efforts aimed at enhancing the transparency and interpretability of ML algorithms, forming the field of explainable Artificial Intelligence (XAI). The objectives of XAI encompass: introducing transparency to ML models by offering comprehensive insights into the rationale behind specific decisions; designing ML models that are both more interpretable and transparent, while maintaining high levels of performance;, and establishing methods for assessing the overall interpretability and transparency of models, quantifying their effectiveness for various stakeholders. This Special Issue gathers contributions on recent advancements and techniques within the domain of XAI. |
| format | Online |
| id | doab-20.500.12854ir-137616 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1376162024-05-14T13:46:06Z Advances in Explainable Artificial Intelligence Gianini, Gabriele Portier, Pierre-Edouard activation function ReLU family activation function test psychological profiling predictive modeling behavioral data explainable artificial intelligence rule extraction counterfactual explanations fairness bias artificial intelligence machine learning psychiatry health mental health federated learning 6G vehicle-to-everything (V2X) quality of service quality of experience interactive machine learning decision tree classifiers transparent-by-design parallel coordinates natural language generation fact-checking explainable AI deep learning LSTM Arabic sentiment analysis Explainable AI text mining random forest multi-layer perceptron protein data bank neural network artificial neural networks knowledge representation source code analysis text classification uncertainty quantification efficiency electroencephalography convolutional variational autoencoder latent space interpretation spectral topographic maps thema EDItEUR::U Computing and Information Technology::UY Computer science Machine Learning (ML)-based Artificial Intelligence (AI) algorithms have the capability to learn from known examples, creating various abstract representations and models. When applied to unfamiliar examples, these algorithms can perform a range of tasks, including classification, regression, and forecasting, to name a few. Frequently, these highly effective ML representations are challenging to comprehend, especially in the case of Deep Learning models, which may involve millions of parameters. However, in many applications, it is crucial for stakeholders to grasp the reasoning behind the system's decisions to utilize them more effectively. This necessity has prompted extensive research efforts aimed at enhancing the transparency and interpretability of ML algorithms, forming the field of explainable Artificial Intelligence (XAI). The objectives of XAI encompass: introducing transparency to ML models by offering comprehensive insights into the rationale behind specific decisions; designing ML models that are both more interpretable and transparent, while maintaining high levels of performance;, and establishing methods for assessing the overall interpretability and transparency of models, quantifying their effectiveness for various stakeholders. This Special Issue gathers contributions on recent advancements and techniques within the domain of XAI. 2024-05-14T13:45:55Z 2024-05-14T13:45:55Z 2024 book ONIX_20240514_9783725802838_215 9783725802838 9783725802845 https://directory.doabooks.org/handle/20.500.12854/137616 eng application/octet-stream Attribution-NonCommercial-NoDerivatives 4.0 International https://mdpi.com/books/pdfview/book/8803 https://mdpi.com/books/pdfview/book/8803 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-0284-5 10.3390/books978-3-7258-0284-5 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725802838 9783725802845 208 open access |
| spellingShingle | activation function ReLU family activation function test psychological profiling predictive modeling behavioral data explainable artificial intelligence rule extraction counterfactual explanations fairness bias artificial intelligence machine learning psychiatry health mental health federated learning 6G vehicle-to-everything (V2X) quality of service quality of experience interactive machine learning decision tree classifiers transparent-by-design parallel coordinates natural language generation fact-checking explainable AI deep learning LSTM Arabic sentiment analysis Explainable AI text mining random forest multi-layer perceptron protein data bank neural network artificial neural networks knowledge representation source code analysis text classification uncertainty quantification efficiency electroencephalography convolutional variational autoencoder latent space interpretation spectral topographic maps thema EDItEUR::U Computing and Information Technology::UY Computer science Advances in Explainable Artificial Intelligence |
| title | Advances in Explainable Artificial Intelligence |
| title_full | Advances in Explainable Artificial Intelligence |
| title_fullStr | Advances in Explainable Artificial Intelligence |
| title_full_unstemmed | Advances in Explainable Artificial Intelligence |
| title_short | Advances in Explainable Artificial Intelligence |
| title_sort | advances in explainable artificial intelligence |
| topic | activation function ReLU family activation function test psychological profiling predictive modeling behavioral data explainable artificial intelligence rule extraction counterfactual explanations fairness bias artificial intelligence machine learning psychiatry health mental health federated learning 6G vehicle-to-everything (V2X) quality of service quality of experience interactive machine learning decision tree classifiers transparent-by-design parallel coordinates natural language generation fact-checking explainable AI deep learning LSTM Arabic sentiment analysis Explainable AI text mining random forest multi-layer perceptron protein data bank neural network artificial neural networks knowledge representation source code analysis text classification uncertainty quantification efficiency electroencephalography convolutional variational autoencoder latent space interpretation spectral topographic maps thema EDItEUR::U Computing and Information Technology::UY Computer science |
| topic_facet | activation function ReLU family activation function test psychological profiling predictive modeling behavioral data explainable artificial intelligence rule extraction counterfactual explanations fairness bias artificial intelligence machine learning psychiatry health mental health federated learning 6G vehicle-to-everything (V2X) quality of service quality of experience interactive machine learning decision tree classifiers transparent-by-design parallel coordinates natural language generation fact-checking explainable AI deep learning LSTM Arabic sentiment analysis Explainable AI text mining random forest multi-layer perceptron protein data bank neural network artificial neural networks knowledge representation source code analysis text classification uncertainty quantification efficiency electroencephalography convolutional variational autoencoder latent space interpretation spectral topographic maps thema EDItEUR::U Computing and Information Technology::UY Computer science |
| url | ONIX_20240514_9783725802838_215 |