HDR TRIPODS: D4 (Dependable Data-Driven Discovery) Institute

数据驱动的发现正在渗透到社会的关键结构中。不可靠的发现会导致对社会、国防和个人产生深远和灾难性后果的决定。因此，产生发现和决策的数据科学生命周期的可靠性是一个关键问题，需要一个新的整体观点和正式的基础。该项目将在爱荷华州州立大学建立独立的数据驱动发现（D4）研究所，该研究所将推进基础研究，以确保数据驱动的发现具有高质量。D4研究所的活动将对数据科学生命周期的可靠性产生变革性影响。首先，问题定义本身将通过帮助学术界以外的未来创新产生重大影响。虽然可靠性的概念在计算机系统文献中得到了很好的研究，但数据科学的挑战将现有知识的边界推向了未知领域。该研究所的工作将定义D4，并通过提供D4的变革性理论来增加数据科学对社会的益处。第二个影响将来自该研究所促进的共享词汇发展过程，其结果将鼓励TRIPODS学科和领域专家就共同目标和挑战进行合作。第三，该研究所将通过为基础研究提供资金，为D4确定研究方向，这将产生一系列单独的影响。第四，该研究所将通过中西部大数据暑期学校和D4研讨会等活动，促进对不同数据科学家骨干的跨学科培训。该项目将通过促进基础研究来推进数据科学的理论基础，以了解数据科学生命周期可靠性的风险，形式化数据科学生命周期可靠性措施的严格数学基础，并确定创建可靠的数据科学生命周期的机制。该项目将风险定义为可能导致数据驱动发现失败的原因，以及计划，获取，管理，分析和推断数据的过程，统称为数据科学生命周期。例如，一个非常昂贵的推理过程可以向面临最后期限的人类操作员提供延迟信息（复杂性作为风险）;如果数据科学生命周期提供了一个没有不确定性度量的建议，人类操作员就无法确定是否信任该建议（不确定性作为风险）。与最近专注于机器学习算法的公平性，问责制和可信度问题的工作相比，该项目将采取整体视角并考虑整个数据科学生命周期。在项目的第一阶段，调查人员将重点关注四个指标：复杂性、资源限制、不确定性和数据新鲜度。在开发研究这些措施的框架时，这项工作将使研究人员准备将其活动扩展到第二阶段的其他措施，并解决数据科学生命周期的更大部分。每项措施的研究都带来了基础性的挑战，需要来自多个TRIPODS学科的专业知识来解决。该项目由HDR TRIPODS和刺激竞争研究的既定计划（EPSCoR）共同资助。该奖项反映了NSF的法定使命，并被认为值得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来支持。

Data-driven discoveries are permeating critical fabrics of society. Unreliable discoveries lead to decisions that can have far-reaching and catastrophic consequences on society, defense, and the individual. Thus, the dependability of data-science lifecycles that produce discoveries and decisions is a critical issue that requires a new holistic view and formal foundations. This project will establish the Dependable Data Driven Discovery (D4) Institute at Iowa State University that will advance foundational research on ensuring that data-driven discoveries are of high quality. The activities of the D4 Institute will have a transformative impact on the dependability of data-science lifecycles. First, the problem definition itself will have a significant impact by helping future innovations beyond academia. While the notion of dependability is well-studied in the computer-systems literature, challenges in data science push the boundary of existing knowledge into the unknown. This institute's work will define D4, and increase data science's benefit to society by providing a transformative theory of D4. The second impact will come from the process of shared vocabulary development facilitated by this institute, and its result that would encourage experts across TRIPODS disciplines and domain experts to collaborate on common goals and challenges. Third, the institute will set research directions for D4 by providing funding for foundational research, which will have a separate set of impacts. Fourth, the institute will facilitate transdisciplinary training of a diverse cadre of data scientists through activities such as the Midwest Big Data Summer School and the D4 workshop. The project will advance the theoretical foundations of data science by fostering foundational research to enable understanding of the risks to the dependability of data-science lifecycles, to formalize the rigorous mathematical basis of the measures of dependability for data science lifecycles, and to identify mechanisms to create dependable data-science lifecycles. The project defines a risk to be a cause that can lead to failures in data-driven discovery, and the processes that plan for, acquire, manage, analyze, and infer from data collectively as the data-science lifecycle. For instance, an inference procedure that is significantly expensive can deliver late information to a human operator facing a deadline (complexity as a risk); if the data-science lifecycle provides a recommendation without an uncertainty measure for the recommendation, a human operator has no means to determine whether to trust the recommendation (uncertainty as a risk). Compared to recent works that have focused on fairness, accountability, and trustworthiness issues for machine learning algorithms, this project will take a holistic perspective and consider the entire data-science lifecycle. In phase I of the project the investigators will focus on four measures: complexity, resource constraints, uncertainty, and data freshness. In developing a framework to study these measures, this work will prepare the investigators to scale up their activities to other measures in phase II as well as to address larger portions of the data-science lifecycle. The study of each measure brings about foundational challenges that will require expertise from multiple TRIPODS disciplines to address.This project is jointly funded by HDR TRIPODS and the Established Program to Stimulate Competitive Research (EPSCoR).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.