What is New About the New Public Analytics? Unpacking the New Public Management Causes of Algorithmic Injustice

Abstract

Karen Yeung’s lecture makes a very valuable, and high critical, analysis of the problematic and potentially dangerous pathologies of the New Public Analytics (NPA). Public sector decision making is increasingly data driven as well as outsourced. This has raised concerns regarding the fairness, transparency and even lawfulness of these decisions. As scandals accumulate globally it is important to take the time to consider what has gone wrong, and what should be done. Yeung addresses New Public Management (NPM) as a conceptual predecessor to the NPA. In this response, I push Yeung’s criticism even further, allocating even more of the blame for the NPA problems to NPM. Despite NPM’s oft proclaimed death, it still refuses to lie down. It is important to recognize how the NPM logic still lies at the base of many of the issues we face today if we want to solve these problems at their root. In this article I provide evidence that many of the problems described as problems of data analytics, are caused by NPM tenets such as disaggregation, competition, incentivization, and evidence-based policy making. Secondly, I discuss several actants (in the Latourian sense of the word) that are not (fully) to blame for the problems we face. I conclude with a way forward that relies less on blaming new data analytics and more on blaming old political systems that enact these technologies in anti-democratic ways.

Keywords

• data analytics
• new public management
• ai
• algorithm