A Framework for Evaluating and Disclosing the ESG Related Impacts of AI with the SDGs

Abstract

Artificial intelligence (AI) now permeates all aspects of modern society, and we are simultaneously seeing an increased focus on issues of sustainability in all human activities. All major corporations are now expected to account for their environmental and social footprint and to disclose and report on their activities. This is carried out through a diverse set of standards, frameworks, and metrics related to what is referred to as ESG (environment, social, governance), which is now, increasingly often, replacing the older term CSR (corporate social responsibility). The challenge addressed in this article is that none of these frameworks sufficiently capture the nature of the sustainability related impacts of AI. This creates a situation in which companies are not incentivised to properly analyse such impacts. Simultaneously, it allows the companies that are aware of negative impacts to not disclose them. This article proposes a framework for evaluating and disclosing ESG related AI impacts based on the United Nation’s Sustainable Development Goals (SDG). The core of the framework is here presented, with examples of how it forces an examination of micro, meso, and macro level impacts, a consideration of both negative and positive impacts, and accounting for ripple effects and interlinkages between the different impacts. Such a framework helps make analyses of AI related ESG impacts more structured and systematic, more transparent, and it allows companies to draw on research in AI ethics in such evaluations. In the closing section, Microsoft’s sustainability reporting from 2018 and 2019 is used as an example of how sustainability reporting is currently carried out, and how it might be improved by using the approach here advocated.

A Review and Categorization of Artificial Intelligence-Based Opportunities in Wildlife, Ocean and Land Conservation

“…The framework can be built upon for further exploration as its application is largely transferable to other SDGs. Moreover, we support Saetra's conclusion [17] that there is a need to go beyond simplistic analyses of AI applications per specific SDG. Hence, the framework can be used to further investigate the deterioration of natural habitat and decrease in biological diversity-that is, the biotic crisis leading to mass extinction of species.…”

“…For instance, despite being focused on two SDGs goals and selected related targets, its application is largely transferable to other SDGs. We support Saetra's conclusion [17] that there is a need to go beyond simplistic analyses of AI applications per specific SDG. Hence, our M 3 framework can be applied to further investigate the grand challenge of increasing deterioration of natural habitat and decreasing biological diversity.…”

“…Realistically, harmful impacts of AI related to SDGs must be further explored, building from prior work [1,4,7] and aiming at producing more responsible AI innovation. Future research directions could also extend to ESG factors to consider AI-related ESG impacts [17]. In closing, it is our hope that our M 3 framework will become a helpful tool in the quest to benefit from AI advances to combat what are conceivably the greatest challenges of the current century to our natural world.…”

“…Saetra [1] broadened the investigation of AI and SDGs by factoring in a larger context, interlinkages, and AI's ethical implications and sustainability to avoid AI hype. Other studies have investigated specific aspects of AI related to SDGs such as global governance, policy perspectives, the role of business, ethics, knowledge spillovers, Green AI, AI in Industry 4.0, and environmental, social, and governance (ESG)-related impacts of AI, among others [8,9,[11][12][13][14][15][16][17][18].…”

What about investors? ESG analyses as tools for ethics-based AI auditing

“…First, we contribute to the ethics-based AI auditing literature (Sandvig et al 2014;Koshiyama et al 2021;) by exploring the role of ESG criteria in providing metrics for AI auditing and highlighting the role of investors as a source of stakeholder pressure. Second, we contribute to the ESG literature (van Duuren et al 2016;Saetra 2021) by advancing the understanding of AI impacts and AI governance as potential domains in future ESG analyses.…”