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Predictive Machine Learning in Assessing Materiality: The Global Reporting Initiative Standard and Beyond
University of Gävle, Sweden.
Mid Sweden University, Sweden.
Stockholm University, Sweden.
West Virginia University, USA.
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2024 (English)In: Artificial Intelligence for Sustainability: Innovations in Business and Financial Services / [ed] Thomas Walker;Stefan Wendt;Sherif Goubran;Tyler Schwartz, Palgrave Macmillan, 2024, p. 105-131Chapter in book (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Palgrave Macmillan, 2024. p. 105-131
Keywords [en]
Sustainability reporting, Materiality assessment, Machine learning, Predictive validity
National Category
Business Administration
Research subject
Economy, Business administration
Identifiers
URN: urn:nbn:se:lnu:diva-127476DOI: 10.1007/978-3-031-49979-1_6ISBN: 9783031499784 (print)ISBN: 9783031499791 (electronic)OAI: oai:DiVA.org:lnu-127476DiVA, id: diva2:1834385
Available from: 2024-02-04 Created: 2024-02-04 Last updated: 2024-02-26Bibliographically approved

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Berg, Natalia

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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf