The AI ESG protocol: Evaluating and disclosing the environment, social, and governance implications of artificial intelligence capabilities, assets, and activities

Sætra, H. S. (2022). The AI ESG protocol: A tool for evaluating and disclosing impacts and risks of AI and data capabilities, assets, and activities. Sustainable Development. https://doi.org/10.1002/SD.2438

11 Pages Posted: 9 Aug 2022 Last revised: 2 Nov 2022

See all articles by Henrik Skaug Sætra

Henrik Skaug Sætra

University of Oslo - Department of Informatics

Date Written: October 31, 2022

Abstract

AI and data are key strategic resources and enablers of the digital transition. Artificial Intelligence (AI) and data are also intimately related to a company's environment, social, and governance (ESG) performance and the generation of sustainability related impacts. These impacts are increasingly scrutinized by markets and other stakeholders, as ESG performance impacts both valuation and risk assessments. It impacts an entity's potential to contribute to good, but it also relates to risks concerning, for example, alignment with current and coming regulations and frameworks. There is currently limited information on and a lack of a unified approach to AI and ESG and a need for tools for systematically assessing and disclosing the ESG related impacts of AI and data capabilities. I here propose the AI ESG protocol, which is a flexible high-level tool for evaluating and disclosing such impacts, engendering increased awareness of impacts, better AI governance, and stakeholder communication.

Keywords: ESG, AI, data, big data, reporting, sustainability

Suggested Citation

Sætra, Henrik Skaug, The AI ESG protocol: Evaluating and disclosing the environment, social, and governance implications of artificial intelligence capabilities, assets, and activities (October 31, 2022). Sætra, H. S. (2022). The AI ESG protocol: A tool for evaluating and disclosing impacts and risks of AI and data capabilities, assets, and activities. Sustainable Development. https://doi.org/10.1002/SD.2438 , Available at SSRN: https://ssrn.com/abstract=4179536 or http://dx.doi.org/10.2139/ssrn.4179536

Henrik Skaug Sætra (Contact Author)

University of Oslo - Department of Informatics ( email )

Norway

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