Education DCL: EAGER: Developing Experiential Cybersecurity and Privacy Training for AI Practitioners

教育 DCL：EAGER：为人工智能从业者开发体验式网络安全和隐私培训

• 批准号: 2335700
• 负责人: Mohammed Abuhamad
• 金额: $ 29.99万
• 依托单位: Loyola University of Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-11-01 至 2025-10-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2335700&HistoricalAwards=false
• 关键词: Education; DCL; EAGER; Developing; Experiential

Artificial Intelligence (AI) and AI-powered tools have gained momentum in both development and usage and are becoming increasingly prevalent in the workplace. However, many AI practitioners are not aware of the cybersecurity and privacy risks associated with building AI-based systems, such as adversarial attacks on machine learning models, or privacy and ethics risks associated with using AI-based systems for decision-making around social issues. This project's goal is to raise AI workers' awareness of security and privacy risks by developing and evaluating a comprehensive 12-workshop experiential training program. The workshop series will provide knowledge and skills needed to build AI systems that are not only technically sound from an AI perspective but also secure, ethical, and privacy-preserving. Versions of the materials that have been improved after the evaluation will be made available to the wider community, giving the project the potential to widely increase the AI workforce's technical knowledge and cybersecurity awareness.The workshops will follow an experiential learning model and will be designed, organized, and delivered by experts to achieve the learning objectives. These objectives are grouped around five main modules designed to cover a wide space of security and privacy concerns around AI models: Fundamentals and Threats; Adversarial Attacks and Robustness; Privacy, Ethics, and Trust; Secure Development and Data Governance; and Case Studies. Each module will be covered in two to three two-hour workshop sessions. Each workshop starts with one hour of a webinar or a panel of experts debating or discussing the workshop's key topics, followed by one hour of experiential learning component. That second component will consist of either a demo using real-world examples or a hands-on activity asking participants to complete a lab activity, which builds on the materials covered in the first hour. Participants will then present a demonstration of their lab work to other participants or write a reflection on what they learned. The project team will run the workshop series three times, with an evaluation and iteration cycle after each series to improve the materials. Together, the work will lead to a better understanding of both building more trustworthy AI-based systems and how to incorporate security, privacy, and ethics training as part of technical curricula.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.

人工智能（AI）和AI驱动的工具在开发和用法方面都取得了动力，并且在工作场所变得越来越普遍。但是，许多AI从业人员不知道与建立基于AI的系统有关的网络安全性和隐私风险，例如对机器学习模型的对抗性攻击，或与使用基于AI的基于AI基于AI的系统进行社会问题的决策相关的隐私和道德风险。该项目的目标是通过制定和评估全面的12个工作室体验培训计划来提高AI工人对安全和隐私风险的认识。研讨会系列将提供构建AI系统所需的知识和技能，这些系统不仅从技术上讲是从AI的角度来看，而且还具有安全，道德和隐私性。评估后已改进的材料的版本将提供给更广泛的社区，使该项目有可能广泛提高AI劳动力的技术知识和网络安全意识。该讲习班将遵循体验式学习模型，并将设计，并将设计，并将设计，并将设计，并将设计为设计。组织并由专家实现学习目标。这些目标是围绕五个主要模块组成的，旨在涵盖AI模型的广泛安全和隐私问题：基本原理和威胁；对抗性攻击和鲁棒性；隐私，道德和信任；确保开发和数据治理；和案例研究。每个模块将在两个至三个小时的研讨会上覆盖。每个研讨会都从一个小时的网络研讨会或专家小组开始，辩论或讨论研讨会的主要主题，然后是一个小时的体验学习组成部分。第二部分将包括使用现实世界实例的演示或动手活动，要求参与者完成实验室活动，该活动建立在第一个小时内覆盖的材料上。然后，参与者将向其他参与者展示他们的实验室工作，或者对他们学到的知识进行反思。项目团队将三次运行研讨会系列，每个系列之后的评估和迭代周期以改进材料。这项工作一起将使人们更好地了解建立更值得信赖的AI系统，以及如何将安全，隐私和道德培训作为技术课程的一部分。该奖项反映了NSF的法定任务，并被认为是值得通过的支持。使用基金会的智力优点和更广泛的影响评估标准进行评估。