Predictive Machine Learning in Assessing Materiality: The Global Reporting Initiative Standard and Beyond

Jan Svanberg (), Peter Öhman (), Isak Samsten, Presha Neidermeyer (), Tarek Rana () and Natalia Berg ()
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Jan Svanberg: University of Gävle
Peter Öhman: Mid Sweden University
Isak Samsten: Stockholm University
Presha Neidermeyer: West Virginia University
Tarek Rana: Royal Melbourne Institute of Technology University
Natalia Berg: Linnaeus University

Chapter 6 in Artificial Intelligence for Sustainability, 2024, pp 105-131 from Springer

Abstract: Abstract Sustainability reporting standards state that material information should be disclosed, but materiality is not easily nor consistently defined across companies and sectors. Research finds that materiality assessments by reporting companies and sustainability auditors are uncertain, discretionary, and subjective. This chapter investigates a machine learning approach to sustainability reporting materiality assessments that has predictive validity. The investigated assessment methodology provides materiality assessments of disclosed as well as non-disclosed sustainability items consistent with the impact materiality GRI (Global Reporting Initiative) reporting standard. Our machine learning model estimates the likelihood that a company fully complies with environmental responsibilities. We then explore how a state-of-the-art model interpretation method, the SHAP (SHapley Additive exPlanations) developed by Lundberg and Lee (A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 2017-December, pp 4766–4775, 2017), can be used to estimate impact materiality.

Keywords: Sustainability reporting; Materiality assessment; Machine learning; Predictive validity (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-49979-1_6

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DOI: 10.1007/978-3-031-49979-1_6

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