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

In: Artificial Intelligence for Sustainability

Author

Listed:
  • 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)

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.

Suggested Citation

  • Jan Svanberg & Peter Öhman & Isak Samsten & Presha Neidermeyer & Tarek Rana & Natalia Berg, 2024. "Predictive Machine Learning in Assessing Materiality: The Global Reporting Initiative Standard and Beyond," Springer Books, in: Thomas Walker & Stefan Wendt & Sherif Goubran & Tyler Schwartz (ed.), Artificial Intelligence for Sustainability, chapter 6, pages 105-131, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-49979-1_6
    DOI: 10.1007/978-3-031-49979-1_6
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