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

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Intelligent Systems (BRACIS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14196))

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Abstract

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.

Work supported by São Paulo Research Foundation (FAPESP) under the grants #2020/09838-0 (BI0S - Brazilian Institute of Data Science), #2020/10572-5 and #2023/04159-6. L. T. Duarte and R. Suyama would like to thank the National Council for Scientific and Technological Development (CNPq, Brazil) for the financial support.

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Notes

  1. 1.

    https://hai.stanford.edu/news/state-ai-10-charts. Accessed date: 16 May 2023.

  2. 2.

    https://aiindex.stanford.edu/vibrancy/. Accessed date: 16 May 2023.

  3. 3.

    https://www.tortoisemedia.com/intelligence/global-ai/. Accessed date: 16 May 2023.

  4. 4.

    https://www.tortoisemedia.com/intelligence/global-ai/ - Accessed date: 16 May 2023.

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Author information

Authors and Affiliations

  1. School of Electrical Engineering and Computation at the University of Campinas, Campinas, Brazil

    Renata Pelissari

  2. School of Applied Sciences at the University of Campinas, Limeira, Brazil

    Betania Campello, Guilherme Dean Pelegrina & Leonardo Tomazeli Duarte

  3. Center for Engineering, Modeling and Applied Social at Federal University of ABC, Santo André, Brazil

    Ricardo Suyama

Authors
  1. Renata Pelissari
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  2. Betania Campello
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  3. Guilherme Dean Pelegrina
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  4. Ricardo Suyama
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  5. Leonardo Tomazeli Duarte
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Correspondence to Guilherme Dean Pelegrina .

Editor information

Editors and Affiliations

  1. Federal University of São Carlos, São Carlos, Brazil

    Murilo C. Naldi

  2. Centro Universitario da FEI, São Bernardo do Campo, Brazil

    Reinaldo A. C. Bianchi

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Pelissari, R., Campello, B., Pelegrina, G.D., Suyama, R., Duarte, L.T. (2023). Critical Analysis of AI Indicators in Terms of Weighting and Aggregation Approaches. In: Naldi, M.C., Bianchi, R.A.C. (eds) Intelligent Systems. BRACIS 2023. Lecture Notes in Computer Science(), vol 14196. Springer, Cham. https://doi.org/10.1007/978-3-031-45389-2_26

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  • DOI: https://doi.org/10.1007/978-3-031-45389-2_26

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  • Online ISBN: 978-3-031-45389-2

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