Sustainability reporting standards, e.g. the Global Reporting Initiative, require a broader definition of materiality than is traditionally used in financial reporting. Double materiality expands the material information concept to include information about companies' environmental and social impact relevant to society at large. A problem for reporting companies as well as auditors (even though accounting firms invest resources in establishing themselves as reliable service providers) is that the assessment of double materiality is uncertain. The chapter utilises machine learning methods to suggest a method to determine double materiality in sustainability reporting by examining what type of information can predict environmental issues resulting from companies' operations. It represents a proposal to use a structured and quantitative approach for sustainability auditors to determine double materiality, thereby potentially facilitating sustainability reporting and assurance in accordance with future regulation.