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Applying AI for social good: Aligning academic journal ratings with the United Nations Sustainable Development Goals (SDGs)

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This paper offers three contributions to the burgeoning movements of AI for Social Good (AI4SG) and AI and the United Nations Sustainable Development Goals (SDGs). First, we introduce the SDG-Intense Evaluation framework (SDGIE) that aims to situate variegated automated/AI models in a larger ecosystem of computational approaches to advance the SDGs. To foster knowledge collaboration for solving complex social and environmental problems encompassed by the SDGs, the SDGIE framework details a benchmark structure of data-algorithm-output to effectively standardize AI approaches to the SDGs. Second, as a specific instantiation of the SDGIE framework, the SDG Impact Intensity Model (SDGIIM) is theoretically and operationally established. SDGIIM embeds expert decision-making and SDG keyword banks in textual data processing to determine overall SDG “impact intensity.” Ideally, SDGIIM can be applied to textual data sets from any sector or discipline: academia, business, government, non-profit, civil society, etc. Third, the SDGIIM instantiation is applied to the specific domain of academic journal rating systems as a case study. Traditionally, academic journals have been evaluated on loosely conceived and empirically shaky notions of ‘quality.’ Aligned with the trend of AI4SG and broader calls to action, ‘impact’ is rapidly becoming the primary normative consideration for assessing academic journals. We hypothesize and demonstrate that SDGIIM is capable of producing evaluations aligned with experts’ expectations of SDG impact intensity; the consistent analysis and rating of textual data sets that embody the SDGs with varying degrees of meaning and, ultimately, promote positive impact on the actual material conditions of the world.

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  1. “UN SDG related issues for management” is a sub-gateway of the more encompassing Responsible Management Gateway.

  2. To this end, and in the spirit of SDG #17: Partnerships for the Goals, the authors are collaborating with researchers from Guelph University’s Lang School of Business (Rodenburg et al. 2021) and the Rotterdam School of Management (webpage, 2021), exchanging ideas and testing outcomes for convergent/discriminant validity of our respective techniques.

  3. It is publishing data analytics industry practice to utilize titles, abstracts, and keywords as a composite data set for analyzing textually derived themes for academic journals in the aggregate and individual articles. Full-text searches of articles are impractical and inconsistent because of paywalls. In future iterations of the SDGIIM we intend to include article keywords to enrich our data set and fortify results. It is a promising development that several academic publishers are offering an SDG keyword selection function for submitting authors.


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Portions of this publication and research effort are made possible through the financial support of the Johnson & Johnson Foundation. We thank Simon Linacre and Cabells for our fruitful collaboration on the development and deployment of this research.


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Correspondence to David Steingard.

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Steingard, D., Balduccini, M. & Sinha, A. Applying AI for social good: Aligning academic journal ratings with the United Nations Sustainable Development Goals (SDGs). AI & Soc 38, 613–629 (2023).

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