mlsanityData to 7 Oct 2026

Explainable Natural Language Processing for Corporate Sustainability Analysis

arXiv 2407.17487 ↗First posted 3 Jul 2024cs.CY cs.CLcited by 9

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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.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 25 of its 104 references that are in the map. First posted on arXiv 3 Jul 2024.

Builds on

25 of 104 references.

References · 25

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2023GPT-4 Technical Reportcited by 18,132
  3. 2021Multitask Prompted Training Enables Zero-Shot Task Generalizationcited by 1,462
  4. 2019Unified Language Model Pre-training for Natural Language Understanding and Generationcited by 668
  5. 2018A Benchmark for Interpretability Methods in Deep Neural Networkscited by 467
  6. 2019Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practicescited by 170
  7. 2023GPTEval: A Survey on Assessments of ChatGPT and GPT-4cited by 73
  8. 2022Evaluation of post-hoc interpretability methods in time-series classificationcited by 53
  9. 2022Rethinking Attention-Model Explainability through Faithfulness Violation Testcited by 43
  10. 2023CHATREPORT: Democratizing Sustainability Disclosure Analysis through LLM-based Toolscited by 29
  11. 2024How Interpretable are Reasoning Explanations from Prompting Large Language Models?cited by 27
  12. 2022Human Interpretation of Saliency-based Explanation Over Textcited by 25
13 more
  1. 2020Strongly Incremental Constituency Parsing with Graph Neural Networkscited by 23
  2. 2022Hierarchical Attention Network for Explainable Depression Detection on Twitter Aided by Metaphor Concept Mappingscited by 23
  3. 2023ClimateBERT-NetZero: Detecting and Assessing Net Zero and Reduction Targetscited by 16
  4. 2023The Memory Perturbation Equation: Understanding Model's Sensitivity to Datacited by 16
  5. 2022ESGBERT: Language Model to Help with Classification Tasks Related to Companies Environmental, Social, and Governance Practicescited by 11
  6. 2022Environmental Claim Detectioncited by 11
  7. 2019Global Greedy Dependency Parsingcited by 8
  8. 2024ProtoS-ViT: Visual foundation models for sparse self-explainable classificationscited by 8
  9. 2024Explain the Black Box for the Sake of Science: the Scientific Method in the Era of Generative Artificial Intelligencecited by 5
  10. 2023A Survey on Semantic Processing Techniquescited by 4
  11. 2024Explainable automatic industrial carbon footprint estimation from bank transaction classification using natural language processingcited by 3
  12. 2022Automatic ESG Assessment of Companies by Mining and Evaluating Media Coverage Data: NLP Approach and Toolcited by 2
  13. 2021Using word embedding for environmental violation analysis: Evidence from Pennsylvania unconventional oil and gas compliance reportscited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 4; 2026: 3.

9 papers.

  1. 2024Towards Faithful Natural Language Explanations: A Study Using Activation Patching in Large Language Modelscited by 14
  2. 2025The challenge of uncertainty quantification of large language models in medicinecited by 14
  3. 2025Towards Robust ESG Analysis Against Greenwashing Risks: Aspect-Action Analysis with Cross-Category Generalizationcited by 7
  4. 2024Clinical Insights: A Comprehensive Review of Language Models in Medicinecited by 6
  5. 2025Deriving Strategic Market Insights with Large Language Models: A Benchmark for Forward Counterfactual Generationcited by 1
  6. 2025Why Bonds Fail Differently? Explainable Multimodal Learning for Multi-Class Default Predictioncited by 0
  7. 2026Enhancing Language Models for Robust Greenwashing Detectioncited by 0
  8. 2026OmniSapiens: A Foundation Model for Social Behavior Processing via Heterogeneity-Aware Relative Policy Optimizationcited by 0
  9. 2026Self-Explainability in Self-Adaptive and Self-Organising Systems: Status and Research Directionscited by 0