sustain.AI: a Recommender System to analyze Sustainability Reports
We 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/.
- Veröffentlicht in:
ICAIL '23: Proceedings of the Nineteenth International Conference on Artificial Intelligence and Law - Typ:
Inproceedings - Autoren:
- Jahr:
2023 - Source:
https://dl.acm.org/doi/10.1145/3594536.3595131
Informationen zur Zitierung
: sustain.AI: a Recommender System to analyze Sustainability Reports, ICAIL '23: Proceedings of the Nineteenth International Conference on Artificial Intelligence and Law, 2023, https://dl.acm.org/doi/10.1145/3594536.3595131, Hillebrand.etal.2023a,
@Inproceedings{Hillebrand.etal.2023a,
author={Hillebrand, Lars; Pielka, Maren; Leonhard, David; Deußer, Tobias; Dilmaghani, Tim; Kliem, Bernd; Loitz, Rüdiger; Morad, Milad; Temath, Christian; Bell, Thiago; Stenzel, Robin; Sifa, Rafet},
title={sustain.AI: a Recommender System to analyze Sustainability Reports},
booktitle={ICAIL '23: Proceedings of the Nineteenth International Conference on Artificial Intelligence and Law},
url={https://dl.acm.org/doi/10.1145/3594536.3595131},
year={2023},
abstract={We 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...}}
