ML Basis for Intelligence Augmentation:Toward Personalized Modeling, Reasoning under Data-Knowledge Symbiosis, and Interpretable Interaction for AI-assisted Human Decision-making

智能增强的机器学习基础：面向人工智能辅助人类决策的个性化建模、数据知识共生下的推理和可解释的交互

• 批准号: 2040381
• 负责人: Eric Xing
• 金额: $ 73.89万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2040381&HistoricalAwards=false
• 关键词: ML; Basis; Intelligence; Augmentation; Toward

Much of the work people do today—in healthcare, business, scientific enterprises, and military operations—is performed in teams. Collaborative decision-making effort within a team is a complex and challenging process of integrating, understanding, and acting upon different types of information. This project aims to advance the use of artificial intelligence and machine learning as intelligence augmentation (IA) tools for facilitating and improving collaborative decision making in clinical teams, focusing on AI-assisted diagnosis and treatment. The focus of the investigators reflects the practical importance and impact of IA in healthcare, especially in the on-going fight with the pandemic where efficiency, validity, and cost-effectiveness of medical decision-making is critical. However, the proposed methods will apply to other forms and use-cases of IA, such as policy making, public health responses, intelligence and business operations, ultimately advancing national health, prosperity, and welfare.Although modern machine learning research has been widely involved in solving various pattern discovery and recognition tasks based on a wide spectrum of data—either in a fully autonomous fashion or in rudimentary human-AI collaborative settings such as crowdsourcing—effectively augmenting and assisting complex collaborative human decision-making efforts in the space of diagnosis, treatment, planning, logistics remains to be an open challenge. In clinical decision- making, understanding and treating the disease must rely on the vast knowledge and expertise and be based on evidence coming from heterogeneous sources of information, ranging from text (medical history), to imagery (radiograms), to time series data (vitals). Making sense of such multimodal information requires effective communication and collaboration within clinical teams. The investigators propose to study some of the key technical challenges in machine learning for IA: (1) modeling human decision-making processes; (2) incorporating background knowledge into data-driven systems; and (3) building human-AI interface for productive inter- and intra-team collaboration. To that end, the investigators will: (1) develop a machine learning framework based on modeling individual decision-makers that enables accurate detection of errors in medical diagnosis and can be used as a recommendation engine in collaborative decision-making settings; (2) develop principled strategies for integrating objective medical knowledge (e.g., automatically extracted from rapidly growing medical literature) with the clinical experience and expertise of a team of health professionals; (3) design human-interpretable interfaces that enable efficient communication in decision making within and across teams, including new tools for interpreting how the models arrived at each recommended decision and natural language interfaces that can facilitate human-AI collaboration.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，人们在医疗保健，商业，科学企业和军事行动中所做的许多工作都在团队中进行。团队中的协作决策工作是整合，理解和采取不同类型信息的复杂而挑战的过程。该项目旨在推动将人工智能和机器学习用作智力增强（IA）工具，以支持和改善临床团队中的协作决策，重点关注AI辅助诊断和治疗。研究人员的重点反映了IA在医疗保健中的实际重要性和影响，尤其是在与大流行的持续斗争中，医疗决策的有效性，有效性和成本效益至关重要。但是，所提出的方法将适用于IA的其他形式和用例，例如政策制定，公共卫生响应，智能和业务运营，最终提高了国家健康，繁荣和福利。尽管现代机器学习研究已广泛地涉及解决各种模式发现和基于全面的人类与人类的全面建立的模式发现和识别任务的涉及，例如人类或人类的整体建立。人群供词 - 在诊断，治疗，计划，后勤方面有效地增强和协助复杂的人类决策努力仍然是一个公开挑战。在临床决策中，理解和治疗疾病必须依赖于广泛的知识和专业知识，并基于来自异质信息来源的证据，从文本（病史）到图像（射线照相）到时间序列数据（生命值）。理解这种多模式信息需要在临床团队中有效的沟通和协作。研究人员建议研究IA机器学习中一些主要的技术挑战：（1）对人类决策过程进行建模； （2）将背景知识继承到数据驱动系统中； （3）构建人体界面，以实现富有成效的团队间和团队内协作。为此，调查人员将：（1）基于建模个体决策者建模的机器学习框架，该框架可以准确检测医学诊断中的错误，并可以在协作决策设置中用作建议引擎； （2）制定整合客观医学知识的主要策略（例如，从快速成长的医学文献中自动提取）与卫生专业人员团队的临床经验和专业知识； (3) design human-interpretable interfaces that enable efficient communication in decision making within and across teams, including new tools for interpreting how the models arrived at each recommended decision and natural language interfaces that can facilitate human-AI collaboration.This award reflects NSF's statutory mission and has been deemed honestly of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.