Identifying relevant indicators for monitoring a National Artificial Intelligence Strategy

TL;DR

The paper tackles how to monitor National Artificial Intelligence Strategies by proposing a two-step method to (i) identify relevant monitoring indicators from international and national AI policies and (ii) assess their alignment with a specific government’s strategy to reveal monitoring gaps and the strategy’s structural quality. It formalizes a three-stage process (indicator identification, indicator–strategy correspondence, and pattern detection) and demonstrates it through the Brazilian EBIA case, uncovering misalignments and ethical/governance implications. The work contributes a transferable framework for indicator selection, alignment analysis, and feedback to improve NAIS design and governance, enabling better tracking of AI’s social and economic impacts. The findings show how indicator alignment can reveal blind spots, inform adjustments to strategy structure, and support ongoing monitoring efforts across diverse national contexts.

Abstract

How can a National Artificial Intelligence Strategy be effectively monitored? To address this question, we propose a methodology consisting of two key components. First, it involves identifying relevant indicators within national AI strategies. Second, it assesses the alignment between these indicators and the strategic actions of a specific government's AI strategy, allowing for a critical evaluation of its monitoring measures. Moreover, identifying these indicators helps assess the overall quality of the strategy's structure. A lack of alignment between strategic actions and the identified indicators may reveal gaps or blind spots in the strategy. This methodology is demonstrated using the Brazilian AI strategy as a case study.

Figures (3)

• Figure 1: Structure of EBIA and its nine thematic axes.
• Figure 2: Number of proposed indicators found in the strategies of the analyzed countries.
• Figure 3: Number of countries using each of the proposed indicators.