Special edition Information Polity
In this special edition of Information Polity there is a focus on the transparency challenges of using algorithms in government in decision-making procedures at the macro-, meso-, and micro-levels.
- Sarah Giest & Alex Ingrams
- 04 December 2020
The use of algorithms in government is transforming the way bureaucrats work and make decisions in different areas, such as healthcare or criminal justice. In this special issue of Information Policy Alex Ingrams wrote an article and Sarah Giest wrote an editorial about algorithmic decision-making.
Alex Ingrams: A machine learning approach to open public comments for policymaking
In this paper, Ingrams argues that the conflict between the copious amount of digital data processed by public organisations and the need for policy-relevant insights to aid public participation constitutes a ‘public information paradox’. Machine learning (ML) approaches may offer one solution to this paradox through algorithms that transparently collect and use statistical modelling to provide insights for policymakers. The analysis results in salient topic clusters that could be used by policymakers to understand large amounts of text such as in an open public comments process. The results are compared with the actual final proposed TSA rule, and the author reflects on new questions raised for transparency by the implementation of ML in open rule-making processes.
Sarah Giest: Introduction to special issue algorithmic transparency in government: Towards a multi-level perspective
The editorial sets the stage for the special issue on algorithmic transparency in government. The papers in the issue bring together transparency challenges experienced across different levels of government, including macro-, meso-, and micro-levels. This highlights that transparency issues transcend different levels of government – from European regulation to individual public bureaucrats. With a special focus on these links, the editorial sketches a future research agenda for transparency-related challenges. Highlighting these linkages is a first step towards seeing the bigger picture of why transparency mechanisms are put in place in some scenarios and not in others. Finally, this introduction present an agenda for future research, which opens the door to comparative analyses for future research and new insights for policymakers.