APPRAISE: a governance framework for innovation with AI systems

TL;DR

This paper introduces the APPRAISE governance framework for AI innovation, integrating strategic variables and audit mechanisms to ensure responsible AI development in line with EU regulations.

Contribution

It presents a novel governance framework that links strategic organizational factors with responsible AI value creation, supported by empirical research and validation.

Findings

Organization size significantly moderates AI compliance strategies.

Organizations experience four main pressures when innovating with AI.

The framework effectively guides organizations in aligning AI innovation with regulatory requirements.

Abstract

As artificial intelligence (AI) systems increasingly impact society, the EU Artificial Intelligence Act (AIA) is the first serious legislative attempt to contain the harmful effects of AI systems. This paper proposes a governance framework for AI innovation. The framework bridges the gap between strategic variables and responsible value creation, recommending audit as an enforcement mechanism. Strategic variables include, among others, organization size, exploration versus exploitation -, and build versus buy dilemmas. The proposed framework is based on primary and secondary research; the latter describes four pressures that organizations innovating with AI experience. Primary research includes an experimental setup, using which 34 organizations in the Netherlands are surveyed, followed up by 2 validation interviews. The survey measures the extent to which organizations coordinate technical elements of AI systems to ultimately comply with the AIA. The validation interviews generated additional in-depth insights and provided root causes. The moderating effect of the strategic variables is tested and found to be statistically significant for variables such as organization size. Relevant insights from primary and secondary research are eventually combined to propose the APPRAISE framework.