Algo-Political Brokering in the Face of Informational Injustice: The Case of an Algorithmic Decision-Making System

Presenter

Tapani Rinta-Kahila
Senior Lecturer of Business Information Systems and ARC DECRA Fellow
The University of Queensland

Abstract

While organisations are quick to deploy algorithmic decision-making (ADM) in pursuit of efficiency gains, there is mounting evidence that ADM systems can perpetuate injustice. One form of this injustice occurs when information asymmetry between such systems and individuals subject to their decisions creates negative impacts on individuals. We characterise this type of injustice as informational injustice and examine it using a rich case study of a digital grassroots initiative (#NotMyDebt) that emerged in response to an ADM system deployed by the Australian Government. Our study introduces the notion of algo-political brokering as a form of work intended to address informational injustice. Algo-political brokering consists of three interrelated processes: revealing, reverse-engineering, and refuting. As algo-political brokering unfolds, the ADM system’s opacity decreases while the informational capacity of people affected by its decisions increases. This study contributes to the scholarly discussion on ADM systems, informational justice, and how practitioners and policymakers can develop systems and policies to rectify such injustice..

Biography

Tapani Rinta-Kahila is a senior lecturer of business information systems and an ARC DECRA Fellow at The University of Queensland Business School. Dr Rinta-Kahila’s research focuses on issues around implementing and managing AI systems in organisations, unintended consequences of AI/IT use, and decommissioning of organisational IT systems, among other topics. His research has appeared in leading IS journals, including MIS Quarterly, Journal of the Association for Information Systems, European Journal of Information Systems, Journal of Information Technology, Information & Management, and MIS Quarterly Executive. His work has been recognised with several awards, such as the AIS Early Career Award, AIS Best Publication Award, and Stafford Beer Medal.