Governments using data analytics will be increasingly drawn into creating social licence for these applications. The need will be greatest where two conditions are present. First, where data analytics are used to predict rather than describe or prescribe. Second, where such prediction is used by governments when exercising their coercive powers. Two examples of using predictive risk modelling are identifying children at risk of neglect and abuse; and assessing recidivism risk in the criminal justice system. Each has drawn criticism, precipitating discussion around the requirements for social licence. Much of this discussion focusses on transparency as both an intrinsic virtue and an instrumental virtue for achieving social licence. The paper contends that transparency is unachievable as an intrinsic virtue for such purposes and that its conceptions as an instrumental virtue fall short of that required for users and subjects as well as the public to have “sufficient to approve or disapprove of the algorithm's performance”. The conclusion is that unconventional democratic forms, including deliberative and direct democracy, are likely to prove more successful than representative democracy in establishing that licence and thereby realising more fully the potential contribution of data analytics to better government.

中文翻译：

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公共部门数据分析社会许可的新方法

使用数据分析的政府将越来越多地被吸引为这些应用程序创建社会许可。当存在两个条件时，需求最大。首先，数据分析用于预测而不是描述或规定。其次，政府在行使强制权力时使用了这种预测。使用预测风险模型的两个例子是识别有被忽视和虐待风险的儿童；评估刑事司法系统中的累犯风险。每个人都受到批评，引发了围绕社会许可要求的讨论。大部分讨论都集中在透明度作为获得社会许可的内在美德和工具美德上。该论文认为，透明度作为此类目的的内在美德是无法实现的，并且其作为工具美德的概念不符合用户和主体以及公众“足以批准或反对算法性能”的要求。结论是，非常规民主形式，包括协商民主和直接民主，在建立这种许可方面可能比代议制民主更成功，从而更充分地实现数据分析对更好政府的潜在贡献。