The IPL newsletter: Volume 24, Issue 491

Automation Nation? AI Adoption in Canadian Businesses

Angus Lockhart, The Dais
The promise of artificial intelligence has long been its potential to make an economy more productive, increasing wages and living standards for all. For Canada to realize these benefits and remain competitive with our global peers, we will need to responsibly harness the potential of AI. Unfortunately, Canada lags behind. And yet, little is understood about how Canadian businesses are adopting this newest type of technology, from how prevalent its use is, how it is being deployed and what barriers exist to prevent further adoption. This study uses a high quality survey administered by Statistics Canada to look at these issues.

The experimentation–accountability trade-off in innovation and industrial policy: are learning networks the solution?

Slavo Radosevic, Despina Kanellou, George Tsekouras, Science and Public Policy
The exact nature of industrial/innovation (I/I) policy challenges and the best way to address them are unknown ex ante. This requires a degree of experimentation, which can be problematic in the context of an accountable public administration and leaves the question of how to reconcile the experimental nature of I/I policy with the need for public accountability, a crucial but unresolved issue. The trade-off between experimentation and accountability requires a governance model that will allow continuous feedback loops among the various stakeholders and ongoing evaluation of and adjustments to activities as programmes are implemented. This article proposes an ‘action learning’ approach, incorporating the governance mechanism of ‘learning networks’ to handle the problems of implementing experimental governance of new and untried I/I policies. The authors resolve the issue of accountability by drawing on the literature on network governance in public policy. By integrating control and learning dimensions of accountability, this approach enables us to resolve conceptually and empirically trade-offs between the need for experimentation and accountability in I/I policy.

Section 2 defines the challenges of a trade-off between accountability and experimentation. The authors note that a conventional I/I policy approach cannot accommodate experimentation as there is a trade-off between the need for experimentation in I/I policy and the demand for public accountability. Also, the collective or multi-stakeholder nature of the I/I policy requires network governance as an accountability mechanism and a learning and mutual adjustment mechanism.

Section 3 compares how different existing approaches try to address or avoid the trade-off between experimentation and accountability. The authors summarize the strengths and weaknesses of the literature on smart specialization, experimental governance, Experimentation–feedback–adaptation, Problem-driven iterative adaptation, Directed improvisation (variation–selection–niche creation), and Transformative innovation policy.

Section 4 builds on network governance and action learning literature to propose a governance mechanism that resolves this trade-off. Experimentation in I/I policy rests on learning, but learning alone can be justified only if the process and its outcomes are accountable. The authors  differentiate between ‘Deliberative’ (process) accountability (how a particular decision is delivered) and Substantive’ (outcome) accountability (about the outcomes of decisions, i.e. whether they have led to the goals sought initially). The authors propose 'Learning Networks' as a governance mechanism to resolve quickly issues that arise during implementation and will provide feedback into the design process. Specifically, LNs:

1. include all stakeholders in the I/I policy process, including small and medium enterprises (SMEs), contributing as designers, implementers, and beneficiaries;

2. are formal arrangements with clear and well-defined thresholds for participation;

3. have an explicit operational structure that includes regular processes and actions;

4. have a primary target—specific learning/new knowledge about the experiential I/I policy implementation process enabled by the network, e.g. examining each other viewpoints and sharing expertise; and

5. assess learning outcomes that provide feedback on network operation

EVENTS

Wick #11, PhD Workshop in Economics of Innovation, Complexity, and Knowledge

December 13-14, 2023, the Collegio Carlo Alberto, Turin, Italy.
This workshop is organized by students of the Vilfredo Pareto Doctoral Program in Economics - University of Turinjointly with Brick, Collegio Carlo Alberto and INET-YSI. The aim of the workshop is to bring together young researchers from different disciplines and provide them with an opportunity to discuss their works. The main topics of the workshop broadly fall on the Economics of Knowledge and Innovation, Science and Complexity. The event will feature keynote contributions from Prof. Tom Broekel (University of Stavanger Business School), Prof. Stan Metcalfe (University of Manchester) and Prof. Reinhilde Veugelers (KULeuven, Bruegel, PIIE). The application deadline will be on October 10th.

Subscriptions & Comments

Please forward this newsletter to anyone you think will find it of value. We look forward to collaborating with you on this initiative. If you would like to comment on, or contribute to, the content, subscribe or unsubscribe, please contact us at ipl.munkschool@utoronto.ca .

This newsletter is prepared by Travis Southin.
Project manager is David A. Wolfe