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Multi-disciplinary Use Cases for Convergent new Approaches to AI explainability

融合人工智能可解释性新方法的多学科用例

基本信息

批准号：
EP/V060422/1
负责人：
Monica D'Onofrio
金额：
$ 38.23万
依托单位：
University of Liverpool
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2021
资助国家：
英国
起止时间：
2021 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FV060422%2F1
关键词：
Multi disciplinary Use Cases Convergent

项目摘要

Developing and testing methodologies that allow to interpret the predictions of the AI algorithms in terms of transparency, interpretability, and explainability has become today one of the most important open questions in AI. In this proposal we bring together researchers from different fields with complementary skills, essential to be able to understand the behaviour of the AI algorithms, that will be studied with an interesting set of multidisciplinary use-cases in which explainable AI can play a crucial role and that will be used to quantify strengths and highlight, and possible solve, weaknesses of the available explainable AI methods in different applicative contexts. One aspect hindering so far substantial progress towards explainability is the fact that several proposed solutions in explainable AI proved to be effective after being tailored to specific applications, and frequently not easily transferred to other domains. In this project, we will test the same array of techniques for explainability to use-cases intentionally chosen to be quite heterogeneous with respect to the types of data, learning tasks, scientific questions. The proposed use-cases range from High Energy Physics AI applications, to applied AI in medical imaging, to AI applied for the diagnosis of pulmonary, tracheal and nasal airways diseases, to machine-learning techniques of explainability used to improve analysis and modelling in neuroscience. For each use-case, the research project will consist of three phases. In the first part, we will apply state-of-the-art explainability techniques, properly chosen based on the requirements, to the case under consideration. In the second part, shortcomings of the techniques will be identified. Most notably, issues of scalability to high-dimensional and raw data, where noise can be prevalent compared to the signal of interest, will be taken into consideration, as long as the level of certifiability afforded by each algorithm. In the final phase, algorithms and knowledge built in each use-case will be combined in order to document the results and to develop general procedures and engineering pipelines useful for the exploitation of xAI methods in general and different domains.

开发和测试方法，允许从透明度、可解释性和可解释性方面解释人工智能算法的预测，已成为当今人工智能领域最重要的开放问题之一。在这项提议中，我们将来自不同领域的具有互补技能的研究人员聚集在一起，这些技能对于能够理解人工智能算法的行为是必不可少的，我们将通过一组有趣的多学科用例来研究这些技能，在这些用例中，可解释的人工智能可以发挥关键作用，并将用于量化可解释的人工智能方法在不同应用环境中的优势和突出并可能解决的弱点。到目前为止，阻碍在可解释性方面取得实质性进展的一个方面是，可解释人工智能中提出的几个解决方案在为特定应用量身定做后被证明是有效的，而且往往不容易转移到其他领域。在这个项目中，我们将测试相同的技术阵列，以确保用例的可解释性--有意选择的用例在数据类型、学习任务和科学问题方面是相当不同的。建议的用例范围从高能物理人工智能应用程序，到医学成像中的人工智能应用程序，到应用于肺部、气管和鼻呼吸道疾病诊断的人工智能，再到用于改进神经科学分析和建模的可解释性机器学习技术。对于每个用例，研究项目将包括三个阶段。在第一部分中，我们将根据需求适当地选择最先进的可解释性技术来处理正在考虑的案例。在第二部分，将找出这些技术的不足之处。最值得注意的是，将考虑高维和原始数据的可伸缩性问题，其中与感兴趣的信号相比，噪声可能普遍存在，只要每种算法提供的可信性水平即可。在最后阶段，将结合在每个用例中构建的算法和知识，以便记录结果，并开发通用程序和工程管道，用于在一般和不同领域开发XAI方法。