Developing interactive notebooks to support algorithm transparency in data-driven government

数据科学技术正越来越多地被部署在政府中，以支持针对政策、资源和业务活动的目标。要真正采用这种活动，需要应用技术的分析人员和领域从业人员之间的密切合作，他们在沟通决策或提出政策建议时必须解释他们的输出。例如，为了生成理论上和生态上有效的数据驱动模型，在选择相关输入数据集、培训和评估不同的模型规格以及将模型的输出传达给决策者时，需要特定主题的专业知识。该项目将与莱斯特郡议会数据科学家和专家政策制定者目前在成人社会护理、青年服务和交通领域制定的工作计划相联系。该计划是开发、构建和评估笔记本支持的可视化工具，将决策者插入数据科学过程(见Wtenberg等人。2019年是一个典型的例子)：揭示模型背后的潜在机制，并使用现代不确定性可视化技术促进围绕模型概率的直觉。该项目将产生透明的、生态上有效的数据驱动的模式，并将解决学术界确定的各种挑战以及关于数据驱动的政府的算法透明度这一主题的众多政府白皮书。项目产出包括以技术为重点的学术论文，报告披露和传达数据驱动的产出的工具的设计和评价；以及注重经验的论文，详细说明通过使用这些工具产生的模型。为了从更广泛的数据科学界建立参与和批评，该项目将有一个附带的GitHub组织页面，并为独立的项目提供单独的存储库。这些可能侧重于不同的领域--成人社会护理、青年、犯罪和交通--或数据分析过程的不同阶段。例如，一些工具的目标将是建立模型--支持模型参数化和查询--另一些工具将用于模型输出的沟通--稳健性检查和不确定性沟通。笔记本电脑设计的一般性方面将被打包到软件库中。通过这样做，我们的雄心是用令人信服的例子展示一个工作流程，以实现有意义的数据驱动的政策制定。

Data Science techniques are increasingly being deployed in government to support the targeting of policy, resources and operational activities. For such activity to be genuinely adopted, close collaboration is needed between analysts applying techniques and domain practitioners who must interpret their outputs when communicating decisions or making policy recommendations. For example, in order to generate data-driven models that are theoretically and ecologically valid, subject-specific expertise is required in the selection of relevant input datasets, when training and evaluating different model specifications and when communicating the outputs of models to decision-makers.This project will connect with a programme of work currently being developed by data scientists and expert policy makers at Leicestershire County Council in the domains of Adult Social Care, Youth Services and Transport. The plan is to develop, build and evaluate notebook-enabled visualization tools that insert policy-makers into the data science process (see Wattenberg et al. 2019 for a characteristic example): exposing the underlying mechanisms behind models and promoting intuition around model probabilities using modern techniques for uncertainty visualization. The project will lead to data-driven models that are transparent and ecologically valid and will address challenges identified variously in academia and numerous government white papers on the theme of algorithm transparency in data-driven government.Project outputs include technically-focused academic papers reporting on the design and evaluation of tools for exposing and communicating data-driven outputs; and empirically-focussed papers detailing models generated through the use of such tools. In order to build engagement and critique from the wider data science community, the project will have an accompanying github organisation page, with separate repositories for discrete projects. These might focus on different domain areas -- Adult Social Care, Young People, Crime and Transport -- or different stages of data analysis process. For example, some tools will be targeted at model-building -- supporting model parameterisation and query - others at the communication of model outputs - robustness checks and uncertainty communication. Generalisable aspects of notebook design will be packaged into software libraries. Through this, the ambition is to demonstrate a workflow, with compelling examples, for effecting meaningful data-driven policy development.