Digital discretion and public administration in Africa: Implications for the use of artificial intelligence

Abstract

African public administrations are in an especially difficult position with respect to the adoption of artificial intelligence (AI), in both how they harness its benefits but also in managing potential harms. Through a narrative review of existing research, this paper synthesises findings from previous digital government implementations on the African continent, and considers the implications for AI use. The analysis is centred on the way public officials are bypassed or empowered to work with emerging technologies, and the influence of public service values on their adoption. Four concerns emerge as relevant: Integrity of recommendations provided by decision-support systems, including how they are influenced by local organisational practices and the reliability of underlying infrastructures; Inclusive decision-making that balances the (assumed) objectivity of data-driven algorithms and the influence of different stakeholder groups; Exception and accountability in how digital and AI platforms are funded, developed, implemented and used; and an expectation of Complete understanding of people and events through the integration of traditionally dispersed data sources and systems, and how policy actors seek to mitigate the risks associated with this aspiration.

Organisational Enablers of Artificial Intelligence Adoption in Public Institutions: A Systematic Literature Review

“…The following tables include the facets within the five elements of the Despite the technology (AI) and its adoption being discussed in the study, people (i.e., employees -public managers, public servants, external AI experts) must be put first while discussing ICT novelties/transformations that are being introduced into public organisations. Several authors (Alshahrani et al, 2022;Campion et al, 2022;Chen et al, 2023;Criado et al, 2022;Effendi and Pribadi, 2021;Ishengoma et al, 2022;Mikalef et al, 2019;Mikalef et al, 2022;Neumann et al, 2022;Noymanee et al, 2022;Ojo et al, 2019;Peretz-Andersson et al, 2021;Plantinga, 2022;Schaefer et al, 2021;van Noordt and Misuraca, 2020a;van Noordt and Misuraca, 2020b;Wirtz and Müller, 2019) studied the enablers of successful AI adoption related to the human factor. Many of these authors stress that, first, top management's understanding and positive perception of the application and value of AI are the key enablers of AI adoption, followed by top management's support in terms of developing a strategy, providing resources, and change management activities.…”

“…(2) inter-and cross-organisational alignment between project interests and expectations surrounding the data sharing; and (3) engagement within the organisational hierarchy, leading to the unification of expectations on the top and bottom levels of the organisation -Internal collaboration: data and knowledge from different departments along with a common understanding of the aims, benefits and goals of AI projects -Clarifying roles and responsibilities within the collaboration (e.g., by appointing champions) -Project-oriented measures, agile project management Alshahrani et al, 2022, Campion et al, 2022, Chen et al, 2019, Chen et al, 2023, Ishengoma et al, 2022, Mikalef et al, 2019, Mikalef et al, 2022, Plantinga, 2022, Schaefer et al, 2021, van Noordt and Misuraca, 2020a, van Noordt and Misuraca, 2020b Source: authors' elaboration.…”

“…While examining the changes in organisational structure that enable AI adoption, researchers (Alshahrani et al, 2022;Campion et al, 2022;Chen et al, 2019;Chen et al, 2023;Ishengoma et al, 2022;Mikalef et al, 2019;Mikalef et al, 2022;Plantinga, 2022;Schaefer et al, 2021;van Noordt and Misuraca, 2020a;van Noordt and Misuraca, 2020b) detected the following sets of enablers: (1) introducing new organisational structures, e.g., projects along with agile management approaches; (2) inter-organisational collaboration, e.g., public-private partnerships, joint projects etc. ; (3) intra-governmental digital service units that attract talents and accelerate implementation; (4) knowledge transfer and data sharing between and within organisations (on all hierarchical levels); and (5) intra-organisational collaboration -between the organisational units (departments) -to ensure a common understanding of the goals, purpose and benefits of the AI adoption.…”

“…Accordingly, the key enablers concerning the organisational structure are the collaboration between and within public institutions and other stakeholders, reorganisations, and the introduction of agile management. financial and other incentives along with a push from higher levels -Innovations that all stakeholders perceive as 'value adding' -Innovations regarded as easy to use and to experiment with -Innovations compatible with the organisational values -IT managers guided by public values will implement more ethical AI technologies -IT managers developing an organisation-wide readiness perspective, not merely infrastructure investments and pools of data -Senior management allowing experimenting with new ideas and technologies -Methods of agile project management and a culture that allows a degree of failure -Individual motivation -identification of employees interested in AI and thinking flexibly and innovatively Alshahrani et al, 2022, Campion et al, 2022, Criado et al, 2022, Ishengoma et al, 2022, Mikalef et al, 2022, Neumann et al, 2022, Ojo et al, 2019, Plantinga, 2022, van Noordt and Misuraca, 2020a, van Noordt and Misuraca, 2020b.…”

Toward achieving smart cities in Africa: challenges to data use and the way forward

“…Consequently, the rapid adoption of digital technologies in Africa occurs organically and often without public intervention. This phenomenon, exemplified by the “datafication of cities” (Oksman and Raunio, 2018; Bibri and Krogstie, 2020; Plantinga, 2022), raises concerns about the potential detachment of local authorities from the evolving digital landscape. Such detachment may manifest in the development of alternative digital services by external entities, disrupting or competing with locally provided public services.…”