# Explainable Natural Language Processing for Corporate Sustainability Analysis arXiv: https://arxiv.org/abs/2407.17487 ยท HTML: https://mlsanity.com/paper/2407.17487/ ## Key facts - Cited by 9 papers in the map (data through 14 Oct 2026). - Builds on 25 of its 104 references that are in the map. - First posted on arXiv 3 Jul 2024. ## Builds on (most cited first, 25 of 25) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023) - [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://mlsanity.com/paper/2110.08207/) (2021) - [Unified Language Model Pre-training for Natural Language Understanding and Generation](https://mlsanity.com/paper/1905.03197/) (2019) - [A Benchmark for Interpretability Methods in Deep Neural Networks](https://mlsanity.com/paper/1806.10758/) (2018) - [Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices](https://mlsanity.com/paper/1906.09208/) (2019) - [GPTEval: A Survey on Assessments of ChatGPT and GPT-4](https://mlsanity.com/paper/2308.12488/) (2023) - [Evaluation of post-hoc interpretability methods in time-series classification](https://mlsanity.com/paper/2202.05656/) (2022) - [Rethinking Attention-Model Explainability through Faithfulness Violation Test](https://mlsanity.com/paper/2201.12114/) (2022) - [CHATREPORT: Democratizing Sustainability Disclosure Analysis through LLM-based Tools](https://mlsanity.com/paper/2307.15770/) (2023) - [How Interpretable are Reasoning Explanations from Prompting Large Language Models?](https://mlsanity.com/paper/2402.11863/) (2024) - [Human Interpretation of Saliency-based Explanation Over Text](https://mlsanity.com/paper/2201.11569/) (2022) - [Strongly Incremental Constituency Parsing with Graph Neural Networks](https://mlsanity.com/paper/2010.14568/) (2020) - [Hierarchical Attention Network for Explainable Depression Detection on Twitter Aided by Metaphor Concept Mappings](https://mlsanity.com/paper/2209.07494/) (2022) - [ClimateBERT-NetZero: Detecting and Assessing Net Zero and Reduction Targets](https://mlsanity.com/paper/2310.08096/) (2023) - [The Memory Perturbation Equation: Understanding Model's Sensitivity to Data](https://mlsanity.com/paper/2310.19273/) (2023) - [ESGBERT: Language Model to Help with Classification Tasks Related to Companies Environmental, Social, and Governance Practices](https://mlsanity.com/paper/2203.16788/) (2022) - [Environmental Claim Detection](https://mlsanity.com/paper/2209.00507/) (2022) - [Global Greedy Dependency Parsing](https://mlsanity.com/paper/1911.08673/) (2019) - [ProtoS-ViT: Visual foundation models for sparse self-explainable classifications](https://mlsanity.com/paper/2406.10025/) (2024) - [Explain the Black Box for the Sake of Science: the Scientific Method in the Era of Generative Artificial Intelligence](https://mlsanity.com/paper/2406.10557/) (2024) - [A Survey on Semantic Processing Techniques](https://mlsanity.com/paper/2310.18345/) (2023) - [Explainable automatic industrial carbon footprint estimation from bank transaction classification using natural language processing](https://mlsanity.com/paper/2405.14505/) (2024) - [Automatic ESG Assessment of Companies by Mining and Evaluating Media Coverage Data: NLP Approach and Tool](https://mlsanity.com/paper/2212.06540/) (2022) - [Using word embedding for environmental violation analysis: Evidence from Pennsylvania unconventional oil and gas compliance reports](https://mlsanity.com/paper/2112.01224/) (2021) ## Built on by (most cited first, 9 of 9) - [Towards Faithful Natural Language Explanations: A Study Using Activation Patching in Large Language Models](https://mlsanity.com/paper/2410.14155/) (2024) - [The challenge of uncertainty quantification of large language models in medicine](https://mlsanity.com/paper/2504.05278/) (2025) - [Towards Robust ESG Analysis Against Greenwashing Risks: Aspect-Action Analysis with Cross-Category Generalization](https://mlsanity.com/paper/2502.15821/) (2025) - [Clinical Insights: A Comprehensive Review of Language Models in Medicine](https://mlsanity.com/paper/2408.11735/) (2024) - [Deriving Strategic Market Insights with Large Language Models: A Benchmark for Forward Counterfactual Generation](https://mlsanity.com/paper/2505.19430/) (2025) - [OmniSapiens: A Foundation Model for Social Behavior Processing via Heterogeneity-Aware Relative Policy Optimization](https://mlsanity.com/paper/2602.10635/) (2026) - [Why Bonds Fail Differently? Explainable Multimodal Learning for Multi-Class Default Prediction](https://mlsanity.com/paper/2509.10802/) (2025) - [Enhancing Language Models for Robust Greenwashing Detection](https://mlsanity.com/paper/2601.21722/) (2026) - [Self-Explainability in Self-Adaptive and Self-Organising Systems: Status and Research Directions](https://mlsanity.com/paper/2606.09568/) (2026) ## Abstract (arXiv) Sustainability commonly refers to entities, such as individuals, companies, and institutions, having a non-detrimental (or even positive) impact on the environment, society, and the economy. With sustainability becoming a synonym of acceptable and legitimate behaviour, it is being increasingly demanded and regulated. Several frameworks and standards have been proposed to measure the sustainability impact of corporations, including United Nations' sustainable development goals and the recently introduced global sustainability reporting framework, amongst others. However, the concept of corporate sustainability is complex due to the diverse and intricate nature of firm operations (i.e. geography, size, business activities, interlinks with other stakeholders). As a result, corporate sustainability assessments are plagued by subjectivity both within data that reflect corporate sustainability efforts (i.e. corporate sustainability disclosures) and the analysts evaluating them. This subjectivity can be distilled into distinct challenges, such as incompleteness, ambiguity, unreliability and sophistication on the data dimension, as well as limited resources and potential bias on the analyst dimension. Put together, subjectivity hinders effective cost attribution to entities non-compliant with prevailing sustainability expectations, potentially rendering sustainability efforts and its associated regulations futile. To this end, we argue that Explainable Natural Language Processing (XNLP) can significantly enhance corporate sustainability analysis. Specifically, linguistic understanding algorithms (lexical, semantic, syntactic), integrated with XAI capabilities (interpretability, explainability, faithfulness), can bridge gaps in analyst resources and mitigate subjectivity problems within data.