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English(EN) From Failure to Alignment: A Requirements Engineering Framework for Machine Learning Systems

新框架REAL旨在提高ML系统的可信度和对齐度

作者 PulseAugur 编辑部 · [2 个来源] ·

一个名为REAL（Requirements Engineering for mAchines that Learn - and Fail）的新框架被提出，旨在增强机器学习系统的可信度和利益相关者对齐度。这个基于模型的框架整合了数据、模型和整体系统的需求，并利用系统故障来探索替代需求。该方法强调迭代和可追溯的改进，并通过自动驾驶的例子展示了与利益相关者需求的更好对齐。 AI

影响 该框架通过系统地解决需求和故障，可能带来更值得信赖和对齐的AI系统。

排序理由 该集群包含一篇详细介绍机器学习系统新框架的研究论文。

在 arXiv cs.LG 阅读 →

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新框架REAL旨在提高ML系统的可信度和对齐度

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该集群包含一篇详细介绍机器学习系统新框架的研究论文。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Amel Bennaceur, Gopi Krishnan Rajbahadur, Prince Mercy, Bashar Nuseibeh, Faeq Alrimawi ·

    从失败到对齐：机器学习系统的需求工程框架

    arXiv:2606.31589v1 Announce Type: cross Abstract: Organisations designing, developing, and deploying machine learning systems (MLS) need to be able to check that these systems are trustworthy, and communicate this clearly to their stakeholders, be they different categories of use…

  2. arXiv cs.LG TIER_1 English(EN) · Faeq Alrimawi ·

    从失败到对齐：机器学习系统的需求工程框架

    Organisations designing, developing, and deploying machine learning systems (MLS) need to be able to check that these systems are trustworthy, and communicate this clearly to their stakeholders, be they different categories of users, engineers, or wider society. By focusing on st…

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