Critical Analysis of AI Indicators in Terms of Weighting and Aggregation Approaches

• Renata Pelissari UNICAMP https://orcid.org/0000-0002-1721-9602
• Betania Campello UNICAMP https://orcid.org/0000-0001-9609-8724
• Guilherme Dean Pelegrina UNICAMP https://orcid.org/0000-0001-7301-6167
• Ricardo Suyama UFABC https://orcid.org/0000-0002-8398-5268
• Leonardo Tomazeli Duarte UNICAMP https://orcid.org/0000-0003-0290-0080

Resumo

National Artificial Intelligence (AI) strategies have been implemented by several countries worldwide. These strategies aim to guide AI policy priorities and foster research, innovation, and development in AI. Alongside the development of national AI strategies, AI indices have emerged as tools to compare countries’ AI development levels. This study focuses on a specific AI indicator, the Global AI Index (GAII) proposed by Tortoise Media, which ranks 62 countries based on their level of investment, innovation, and implementation of AI. The GAII computes a ranking of countries by aggregating sub-dimensions within these categories using a weighted sum approach, with subjective weight assignments. This paper critically analyzes the weighting and aggregation approaches used in the GAII, employing two techniques. Firstly, the Stochastic Multicriteria Acceptability Analysis (SMAA) is used to explore changes in rankings by varying the weights. Secondly, a non-additive aggregation model known as the Choquet integral is applied to consider potential interactions among dimensions. The findings indicate that the weights assigned to criteria strongly influence the final ranking. Additionally, there are interactions between AI dimensions that should be taken into account to address unbalanced achievements across dimensions. This study contributes to the development of more robust and objective methodologies for comparing countries’ AI development levels.