Digital Governance Drivers and Challenges for Resilience in AI-Driven Innovation of Public Sector

Digitalization is not anymore an emergent phenomenon but the actual shape of everyday life interactions and transactions (Degryse 2016; Tilson et al. 2010; Yoo 2013). Compared to the private sector, where businesses have deployed initiatives to change their infrastructure, governance, and business models to create and exploit value from their digital assets, the public sector is still tied up to a consideration of technology as something separated to public sector reform and policy making. Accordingly, it is still preeminent in the public sector the focus on what can be considered the e-government rhetoric legacy, namely the provision of information and communication technology (ICT) enabled services mainly involving the public administration alone and the translation of administrative procedures in digital format. This view is still present in most countries having officially claimed the adoption of an open government stance towards their initiatives: the result is, in the best case, an efficient public administration, yet still neither inclusive nor fully open, in terms of transparency and accountability (Misuraca and Viscusi 2014). Moreover, the rhetoric is now encompassing the debate on the use of artificial intelligence (AI) solutions for innovating public sector services and decision-making (Androutsopoulou et al. 2019; van Noordt and Misuraca 2019; Sun and Medaglia 2019) with a new emphasis on the need for a governance of AI and not by AI (Sun and Medaglia 2019, p. 378) that resonates the former claims about governance of ICTs vs. governance by ICTs (Misuraca and Viscusi 2015). Thus, although some kind of public value can be gained (Benington 2011; Benington and Moore 2011), the switch to social value from co-production (Alford 2011; Cordella et al. 2018) by involving citizens and external actors is still an ongoing challenge for public administrations willing to have a role in social innovation through the appropriate exploitation of the unprecedented amount of data available from information production, inside and outside public sector information systems (Viscusi & Batini, 2016). This is particularly relevant when thinking about the use of crowdsourcing for deliberation, regulatory reviews, and policy initiatives (Aitamurto and Landemore 2016; Brabham 2009; Lodge and Wegrich 2015), the development of open innovation in the public sector (Viscusi et al. 2015) as well as the emergent challenges of using, e.g., machine learning for deciding on welfare issues (Reisman et al. 2018; West et al. 2019). Taking these issues into account, this article aims to contribute to the theoretical debate on the relationships between digital governance and social innovation, and their impact on policy making for creating and capturing value from effective solutions addressing societal challenges. In particular, we question the drivers and challenges specifically considering growing number of national strategies for innovation driven by artificial intelligence (AI) and the consequent wave of investments. To this end a conceptual model is presented aiming, on the one hand, to connect key dimensions and value drivers of digital governance for Pre-ICIS Workshop on E-Government, Munich, Germany 2019 1 Digital Governance in AI-Driven Innovation of Public Sector social innovation and complex systems methods for policy making; on the other hand, to position AI national initiatives with regard to welfare state initiatives. The model is expected to be applied to the analysis of case studied of AI-driven innovation initiatives in Europe to assess their impact on social innovation. To this end, first a conceptual model is presented aiming, on the one hand, to connect key dimensions and value drivers of digital governance for social innovation (Misuraca, Pasi, et al. 2018; Misuraca and Viscusi 2015) and complex systems methods for policy making formerly introduced by one of the authors in a framework called i-Frame (Misuraca, Geppert, et al. 2018); on the other hand, to position AI national initiatives with regard to welfare state initiatives for an appropriate analysis of their premises and effects. The paper concludes outlining future research directions and policy implications.