Engineering Trustworthy AI: A Developer Guide for Empirical Risk Minimization
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866909378840363008 |
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| author | Pfau, Diana Jung, Alexander |
| author_facet | Pfau, Diana Jung, Alexander |
| contents | AI systems increasingly shape critical decisions across personal and societal domains. While empirical risk minimization (ERM) drives much of the AI success, it typically prioritizes accuracy over trustworthiness, often resulting in biases, opacity, and other adverse effects. This paper discusses how key requirements for trustworthy AI can be translated into design choices for the components of ERM. We hope to provide actionable guidance for building AI systems that meet emerging standards for trustworthiness of AI. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_19361 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Engineering Trustworthy AI: A Developer Guide for Empirical Risk Minimization Pfau, Diana Jung, Alexander Artificial Intelligence I.2; K.4.1 AI systems increasingly shape critical decisions across personal and societal domains. While empirical risk minimization (ERM) drives much of the AI success, it typically prioritizes accuracy over trustworthiness, often resulting in biases, opacity, and other adverse effects. This paper discusses how key requirements for trustworthy AI can be translated into design choices for the components of ERM. We hope to provide actionable guidance for building AI systems that meet emerging standards for trustworthiness of AI. |
| title | Engineering Trustworthy AI: A Developer Guide for Empirical Risk Minimization |
| topic | Artificial Intelligence I.2; K.4.1 |
| url | https://arxiv.org/abs/2410.19361 |