A new approach toward social licensing of data analytics in the public sector

Abstract

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.

Introduction to the Special Issue on Big Data and Social Policy in Australia

“…As O'Loughlin and Bukowitz (2021) argue in the present issue, civil society has an important role to play in the generation of social licence for controversial technologies. They argue that in debates about social licence too much faith has been placed in the virtue of transparency.…”

Ethics and Trustworthiness of AI for Predicting the Risk of Recidivism: A Systematic Literature Review

“…Trust is a crucial component of success for risk assessment tools for recidivism to thrive [11,15]. Trust is a complex area of focus that has drawn the attention of many practitioners and scholars across different disciplinary fields [34].…”

“…The current literature on risk assessment tools has no consensus definition of fairness when predicting offenders' recidivism risk [11,56]. Today's available definitions of fairness in the literature are not optimized enough to affirm what fairness in predicting recidivism should be.…”

“…These tools have predicted the possible risk of recidivism of more than a million offenders in the United States. However, criminal justice system stakeholders have questioned and scrutinized the recidivism risk assessment tools in the past few years due to the ethical impact on convicted individuals and their trustworthiness [11][12][13]. In particular, fairness, transparency, privacy, and accountability have been the subjects of concern for stakeholders [14].…”

Information Resilience: the nexus of responsible and agile approaches to information use

“…engaging stakeholders, through an iterative process [91]. A participatory/democratic approach that puts emphasis on people and communities engaging directly with developers and governments in the social licensing process could be a means to ensure social licence [111], but implies that organisational mechanisms for encouraging interaction between developers, researchers, public sector and the society should be established [111], along with external independent oversight [91]. Further research is needed to investigate if a participatory approach is the right framework.…”