Extending the explainability of machine learning models in policy decision making

扩展机器学习模型在政策决策中的可解释性

• 批准号: 2887425
• 金额: --
• 依托单位: University of Strathclyde
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2887425
• 关键词: Extending; explainability; machine; learning; models

Governments and policy makers are increasing their use of machine learning (ML) to support decision-making. The performance of ML algorithms generally improves with the increase of model complexity, which makes it harder for end-users to interrogate the model output. Lack of transparency and accountability in how the model is structured and the key variables underpinning predictions can lead to mistrust. Concerns about the rapidly expanding use of ML in making critical decisions have been voiced, especially for policies affecting marginalised sectors and communities. While the area of explainable ML has expanded in recent years, the existing methods are often "general-purpose" that fail to capture the specific needs of real-world end-users. As such, the effectiveness of the existing explainable ML approaches remains unclear without understanding the domain knowledge and specific requirements/goals. Understanding the specific needs for explainable ML is highly demanded in policy decision-making since the policy emerges as a compromise between people pursuing different goals.This PhD project aims to bridge the gap by developing a novel process and framework to ensure that ML models can be better understood, and therefore more readily adopted by policy makers. We focus on two aspects. First, we approach the problem by considering the application of ML as a decision-support tool, rather than a predictive tool. To do this, when developing the ML models, we explicitly capture the views of the decision-maker and make sure it is formally captured in the models in a way they can understand, modify and interrogate. Second, we use visual tools commonly deployed for wicked problems such as causal maps to capture the overarching ML process for a decision-maker so that the overall process is better understood and explained. By mixing these two approaches, quantitative and qualitative, meaningful progress will be achieved in developing a framework on explainable ML.

政府和政策制定者越来越多地使用机器学习 (ML) 来支持决策。机器学习算法的性能通常随着模型复杂性的增加而提高，这使得最终用户更难以询问模型输出。模型的构建方式和支持预测的关键变量缺乏透明度和问责制可能会导致不信任。人们对机器学习在关键决策中迅速扩大的使用表示担忧，特别是对于影响边缘化部门和社区的政策。尽管近年来可解释的机器学习领域不断扩大，但现有方法通常是“通用的”，无法捕捉现实世界最终用户的特定需求。因此，如果不了解领域知识和具体要求/目标，现有可解释的机器学习方法的有效性仍然不清楚。在政策决策中，非常需要了解可解释的机器学习的具体需求，因为政策是追求不同目标的人们之间的妥协。这个博士项目旨在通过开发一种新颖的流程和框架来弥合差距，以确保机器学习模型可以被更好地理解，因此更容易被政策制定者采用。我们重点关注两个方面。首先，我们通过将机器学习的应用视为决策支持工具而不是预测工具来解决这个问题。为此，在开发机器学习模型时，我们明确捕获决策者的观点，并确保以他们可以理解、修改和询问的方式在模型中正式捕获这些观点。其次，我们使用通常针对棘手问题部署的可视化工具（例如因果图）来为决策者捕获总体的机器学习流程，以便更好地理解和解释整个流程。通过混合定量和定性这两种方法，在开发可解释的机器学习框架方面将取得有意义的进展。