CRII: III: Pursuing Interpretability in Utilitarian Online Learning Models

CRII：III：追求功利在线学习模式的可解释性

• 批准号: 2245946
• 负责人: Yi He
• 金额: $ 17.5万
• 依托单位: Old Dominion University Research Foundation
• 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: 2023-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2245946&HistoricalAwards=false
• 关键词: CRII; III; Pursuing; Interpretability; Utilitarian

In today's world, the real-time generation of enormous amounts of data has become commonplace, spanning domains such as e-commerce, social media, environmental science, urban disaster and pandemic monitoring, and many others. Such streaming data necessitate data mining (DM) models that can analyze them in time as they emerge, derive actionable insights, and make adjustments on the fly. For instance, predicting crowd movement due to public events (such as concerts, games, parades, and protests) based on data streaming from social media and city sensors can aid in reducing the traffic by steering clear of overcrowded areas. However, as DM models become more prevalent in practice, interpretability has emerged as a vital issue. User comprehension and trust in DM model outputs are critical for their acceptance in daily routines and workflows. Nonetheless, existing research on data streams has focused mainly on model accuracy, producing models that are too complex for human interpretation. This gap between DM researchers and practitioners calls for new research that optimizes model accuracy and interpretability simultaneously. This project aims to bridge the gap by developing novel online algorithms that are transparent to human users and can provide a complete explanation of the logic behind each prediction, earning the trust of human operators and increasing legal defensibility when used to support decision-making in crucial domains such as healthcare, economy, security, and social goods.The overarching goal of this project is to advance interpretability research of online DM models through three research objectives: (1) understanding the dynamism of varying feature spaces and its impact on model structure; (2) quantifying model prediction uncertainty in the absence of adequate supervision labels; and (3) indexing and elucidating model inference paths. To achieve these objectives, the project will focus on four research thrusts. The first thrust will develop novel algorithms that capture and model the variation patterns of feature spaces through an expository feature correlation graph, allowing for joint learning of graphs and predictive models. The second thrust will focus on developing unsupervised methods to quantify the uncertainty of model predictions and identify geometric manifolds underlying data streams with memory-efficient structures. The third thrust will devise new systems to index, track, and illustrate the complete generation process of online predictions. The fourth thrust will establish evaluation metrics and protocols to standardize interpretability measurement in streaming data contexts. The project aims to contribute to interpretable data mining and machine learning research, which will help bridge the gap between data scientists and domain-specific forecasting experts. The educational component of the project will involve mentoring and educating researchers interested in pursuing DM careers in academia or industry, with a particular focus on underrepresented, financially disadvantaged, or disabled undergraduate students in computer science research. The project will also pioneer new classes at the forefront of data mining research and organize workshops at city libraries to engage with the broader public.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.

当今世界，实时生成海量数据已变得司空见惯，涉及电子商务、社交媒体、环境科学、城市灾害和流行病监测等领域。此类流数据需要数据挖掘 (DM) 模型，该模型可以在数据出现时及时对其进行分析，得出可操作的见解，并即时进行调整。例如，根据社交媒体和城市传感器的数据流预测公共活动（如音乐会、比赛、游行和抗议）引起的人群流动，可以通过避开过度拥挤的区域来帮助减少交通量。然而，随着 DM 模型在实践中变得越来越普遍，可解释性已成为一个至关重要的问题。用户对 DM 模型输出的理解和信任对于其在日常工作和工作流程中的接受度至关重要。尽管如此，现有的数据流研究主要集中在模型准确性上，产生的模型对于人类解释来说过于复杂。 DM 研究人员和从业者之间的差距需要新的研究来同时优化模型的准确性和可解释性。该项目旨在通过开发对人类用户透明的新型在线算法来弥合差距，这些算法可以对每个预测背后的逻辑提供完整的解释，赢得人类操作员的信任，并在用于支持关键决策时提高法律辩护性。该项目的总体目标是通过三个研究目标推进在线 DM 模型的可解释性研究：（1）了解不同特征空间的动态及其对模型结构的影响； （2）在缺乏足够监督标签的情况下量化模型预测的不确定性； (3)索引和阐明模型推理路径。为了实现这些目标，该项目将重点关注四个研究重点。第一个重点将开发新颖的算法，通过说明性特征相关图捕获和建模特征空间的变化模式，从而允许图和预测模型的联合学习。第二个重点将集中于开发无监督方法来量化模型预测的不确定性，并识别具有内存高效结构的数据流基础的几何流形。第三个重点将设计新系统来索引、跟踪和说明在线预测的完整生成过程。第四个重点将建立评估指标和协议，以标准化流数据环境中的可解释性测量。该项目旨在为可解释的数据挖掘和机器学习研究做出贡献，这将有助于弥合数据科学家和特定领域预测专家之间的差距。该项目的教育部分将涉及指导和教育有兴趣在学术界或工业界从事 DM 职业的研究人员，特别关注计算机科学研究中代表性不足、经济困难或残疾的本科生。该项目还将在数据挖掘研究的前沿开创新课程，并在城市图书馆组织研讨会，以吸引更广泛的公众参与。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势和更广泛的影响进行评估，被认为值得支持审查标准。