An Early-Stage Workflow Proposal for the Generation of Safe and Dependable AI Classifiers
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913527640358912 |
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| author | Doran, Hans Dermot Veljanovska, Suzana |
| author_facet | Doran, Hans Dermot Veljanovska, Suzana |
| contents | The generation and execution of qualifiable safe and dependable AI models, necessitates definition of a transparent, complete yet adaptable and preferably lightweight workflow. Given the rapidly progressing domain of AI research and the relative immaturity of the safe-AI domain the process stability upon which functionally safety developments rest must be married with some degree of adaptability. This early-stage work proposes such a workflow basing it on a an extended ONNX model description. A use case provides one foundations of this body of work which we expect to be extended by other, third party use-cases. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_01850 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An Early-Stage Workflow Proposal for the Generation of Safe and Dependable AI Classifiers Doran, Hans Dermot Veljanovska, Suzana Machine Learning The generation and execution of qualifiable safe and dependable AI models, necessitates definition of a transparent, complete yet adaptable and preferably lightweight workflow. Given the rapidly progressing domain of AI research and the relative immaturity of the safe-AI domain the process stability upon which functionally safety developments rest must be married with some degree of adaptability. This early-stage work proposes such a workflow basing it on a an extended ONNX model description. A use case provides one foundations of this body of work which we expect to be extended by other, third party use-cases. |
| title | An Early-Stage Workflow Proposal for the Generation of Safe and Dependable AI Classifiers |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2410.01850 |