CC BY 4.0Hillebrand, Lars PatrickLars PatrickHillebrandPielka, MarenMarenPielkaLeonhard, DavidDavidLeonhardDeußer, TobiasTobiasDeußerDilmaghani, TimTimDilmaghaniKliem, BerndBerndKliemLoitz, RüdigerRüdigerLoitzMorad, MiladMiladMoradTemath, ChristianChristianTemathBell Felix de Oliveira, ThiagoThiagoBell Felix de OliveiraStenzel, Marc RobinMarc RobinStenzelSifa, RafetRafetSifa2024-02-022024-02-022023-09-07https://publica.fraunhofer.de/handle/publica/459618https://doi.org/10.24406/publica-255010.1145/3594536.359513110.24406/publica-25502-s2.0-85177816236We present sustain.AI, an intelligent, context-aware recommender system that assists auditors and financial investors as well as the general public to efficiently analyze companies’ sustainability reports. The tool leverages an end-to-end trainable architecture that couples a BERT-based encoding module with a multi-label classification head to match relevant text passages from sustainability reports to their respective law regulations from the Global Reporting Initiative (GRI) standards. We evaluate our model on two novel German sustainability reporting data sets and consistently achieve a significantly higher recommendation performance compared to multiple strong baselines. Furthermore, sustain.AI is publicly available for everyone at https://sustain.ki.nrw/.ennatural language processinrecommender systemsustainabilityconference paper