SaTC: CORE: Small: Understanding and Mitigating the Security Risks of AutoML

自动化机器学习（AutoML）代表了一种新的机器学习范式，它将从原始数据到可部署模型的管道自动化，使更广泛的人能够使用机器学习技术。然而，该管道的每个阶段都受到恶意攻击，这可能导致不准确或易受攻击的模型。这个项目的目标是了解AutoML的底层技术和采用AutoML的方式是如何改变机器学习的安全风险的，以及在使用AutoML时对这些风险的防御是如何改变的。该项目的成功不仅将提高AutoML的安全性，而且还将促进构建和操作机器学习系统的更多原则实践，同时为安全性，机器学习和人机交互领域的知识做出贡献。该项目有三个主要的子目标：考虑围绕AutoML产生的所有安全风险；了解导致此类风险的基本因素；为没有丰富专业知识的机器学习从业者设计。为了实现这些目标，团队将(i)通过用户研究和访谈更好地理解当前围绕AutoML的实践；（ii）通过在广泛使用的数据集上评估这些模型，实证地和分析地探索automl生成模型的安全漏洞；（iii）分析前两项活动的结果，对技术中算法选择的标准化或用户对自动化指标的过度依赖等潜在因素进行全面核算；（iv）开发新的原则、方法和工具来减轻上述风险。该团队还将把这项工作整合到一些大学课程中，并进行公众宣传，以提高人们对机器学习在日常生活中所扮演角色的认识。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Automated machine learning (AutoML) represents a new machine learning paradigm that automates the pipeline from raw data to deployable models, enabling a much wider range of people to use machine learning techniques. However, each stage of this pipeline is subject to malicious attacks, which can lead to inaccurate or vulnerable models. This project’s goal is to understand how both the technologies underlying AutoML and the ways it is adopted change security risks around machine learning and how possible defenses to them change when using AutoML. The success of this project will not only improve the security of AutoML but also promote more principled practices of building and operating machine learning systems in general, while contributing to knowledge in the areas of security, machine learning, and human-computer interaction. The project has three main sub-goals: accounting for the full spectrum of security risks that arise around AutoML; understanding the fundamental factors that drive such risks; and designing for machine learning practitioners without extensive expertise. To accomplish these goals, the team will (i) better understand current practices around AutoML through user studies and interviews; (ii) empirically and analytically explore the security vulnerabilities of AutoML-generated models through assessing these models on widely used datasets; (iii) analyze the results of the first two activities to develop a comprehensive accounting of underlying factors such as standardization of algorithmic choices in the technology or over-reliance on automated metrics by users; and (iv) developing new principles, methodologies, and tools to mitigate the aforementioned risks. The team will also integrate the work into a number of college courses and conduct public outreach to raise awareness of the role machine learning plays in everyday life.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.