Artificial Discretion as a Tool of Governance: A Framework for Understanding the Impact of Artificial Intelligence on Public Administration

Abstract

Public administration research has documented a shift in the locus of discretion away from street-level bureaucrats to “systems-level bureaucracies” as a result of new information communication technologies that automate bureaucratic processes, and thus shape access to resources and decisions around enforcement and punishment. Advances in artificial intelligence (AI) are accelerating these trends, potentially altering discretion in public management in exciting and in challenging ways. We introduce the concept of “artificial discretion” as a theoretical framework to help public managers consider the impact of AI as they face decisions about whether and how to implement it. We operationalize discretion as the execution of tasks that require nontrivial decisions. Using Salamon’s tools of governance framework, we compare artificial discretion to human discretion as task specificity and environmental complexity vary. We evaluate artificial discretion with the criteria of effectiveness, efficiency, equity, manageability, and political feasibility. Our analysis suggests three principal ways that artificial discretion can improve administrative discretion at the task level: (1) increasing scalability, (2) decreasing cost, and (3) improving quality. At the same time, artificial discretion raises serious concerns with respect to equity, manageability, and political feasibility.

The agency of algorithms: Understanding human-algorithm interaction in administrative decision-making

“…Landsbergen, 2004). When 'digital discretion' (Busch & Henriksen, 2018), 'artificial discretion' (Young et al, 2019) or 'automated discretion' (Zouridis et al, 2020) replaces human discretion, there is a risk that organizations hide their responsibility for individual decisions behind technological arguments, such as algorithmic complexity, system design flaws or lack of access to data (Fosch-Villaronga, 2019; Zalnieriute et al, 2019). Furthermore, a human eye is also deemed necessary to allow for tailormade solutions or exceptions to prevent disproportional negative outcomes in individual cases .…”

Digital Discretion: Unpacking Human and Technological Agency in Automated Decision Making in Sweden’s Social Services

“…Digital discretion can be evaluated based on frameworks emphasizing criteria such as effectiveness, efficiency, equity, and manageability (Young et al, 2019) or task complexity (Bullock, 2019).…”

Algorithmic transparency and bureaucratic discretion: The case of SALER early warning system

“…Up to now, there is limited empirical research about decisions of bureaucrats based on algorithms (Meijer & Wessels, 2019). AI and algorithms in public agencies seem to evoke the Weberian iron cage, now transformed into a digital fortress, where rules are not readily understood or even available for study (algorithmic opacity) and automated decisions might lead to unintended effects (algorithmic biases) (Young et al, 2019). Besides, it is also expected that algorithms will change the way knowledge-based public employees work and take decisions, so that all aspects of individuals' performance are quantified, compared to others, and managed against algorithm models, that will carry on important changes requiring deeper understanding (Orlikowski & Scott, 2016).…”

“…Then, we focus on decisions where AI may either augment or replace human discretion. Following Young et al (2019), we argue that the relationship of AI to discretion is unique because of three AI' design features: (a) it is built for automating learning and decision-making processes through abstract mathematical representation of problems; (b) it can utilize input data with speed and dimensionality that vastly outstrip human cognition; and (c) as more data become available, it can "learn" and adjust its behavior by updating its decision heuristics' (Young et al, 2019: 2).…”

“…SALER system does not make a direct decision about a specific procedure. At this state, this approach makes civil servants perceive that their decision-making capacities remain unaltered, which will probably reduce side or non-desired effects (Young et al, 2019). In the long term it is expected that results of algorithms might gradually be taken into Table 2 Factors for positive effect of SALER algorithms on discretionary power…”