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arXiv 2103.00964cs.SE
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工程可信机器学习应用实践

Practices for Engineering Trustworthy Machine Learning Applications

Alex Serban, Koen van der Blom, Holger Hoos, Joost Visser

摘要:

随着最近机器学习(ML)的采用激增,ML的不当使用对用户和社会的负面影响现在也得到了广泛的认可。为了解决这个问题,政策制定者和其他利益相关者,如欧盟委员会或NIST,已经提出了旨在促进可信ML的高级别指导方针(即,法律、道德和健全)。然而,这些指南并没有指定参与构建ML系统的人员要采取的行动。在本文中,我们认为,与可信赖的ML的发展相关的指导方针可以转化为操作实践,并应成为ML开发生存周期的一部分。为了实现这一目标,我们进行了多声文献综述,并从白色和灰色文献中挖掘操作实践。此外,我们发起了一项全球调查,以衡量实践的采用情况和这些实践的影响。总的来说,我们确定了14种新的实践,并使用它们来补充现有的ML工程实践目录。对调查结果的初步分析表明,到目前为止,可信ML的实践采用率相对较低。特别是,与确保ML组件安全性相关的实践采用率非常低。其他做法的采用率稍高,例如向用户提供解释。我们的扩展实践目录可供ML开发团队使用,以弥合高级指南与可信赖ML系统的实际开发之间的差距;它是开放的,可供审查和贡献

英文摘要:

Following the recent surge in adoption of machine learning (ML), the negative impact that improper use of ML can have on users and society is now also widely recognised. To address this issue, policy makers and other stakeholders, such as the European Commission or NIST, have proposed high-level guidelines aiming to promote trustworthy ML (i.e., lawful, ethical and robust). However, these guidelines do not specify actions to be taken by those involved in building ML systems. In this paper, we argue that guidelines related to the development of trustworthy ML can be translated to operational practices, and should become part of the ML development life cycle. Towards this goal, we ran a multi-vocal literature review, and mined operational practices from white and grey literature. Moreover, we launched a global survey to measure practice adoption and the effects of these practices. In total, we identified 14 new practices, and used them to complement an existing catalogue of ML engineering practices. Initial analysis of the survey results reveals that so far, practice adoption for trustworthy ML is relatively low. In particular, practices related to assuring security of ML components have very low adoption. Other practices enjoy slightly larger adoption, such as providing explanations to users. Our extended practice catalogue can be used by ML development teams to bridge the gap between high-level guidelines and actual development of trustworthy ML systems; it is open for review and contribution

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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