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.

中文翻译：

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用于企业可持续性分析的可解释自然语言处理


可持续性通常是指对环境、社会和经济产生无害（甚至积极）影响的实体，例如个人、公司和机构。随着可持续性成为可接受和合法行为的代名词，它的要求和监管越来越多。已经提出了几个框架和标准来衡量公司的可持续性影响，包括联合国的可持续发展目标和最近推出的全球可持续发展报告框架等。然而，由于公司运营的多样性和复杂性（即地理、规模、业务活动、与其他利益相关者的相互联系），企业可持续发展的概念很复杂。因此，企业可持续发展评估在反映企业可持续发展工作的数据（即企业可持续发展披露）和评估它们的分析师中都受到主观性的困扰。这种主观性可以归结为不同的挑战，例如数据维度的不完整、模糊、不可靠和复杂性，以及分析师维度的资源有限和潜在偏见。综上所述，主观性阻碍了对不符合普遍可持续发展期望的实体进行有效的成本归因，从而可能使可持续发展工作及其相关法规变得徒劳无功。为此，我们认为可解释自然语言处理 （XNLP） 可以显着增强企业可持续性分析。 具体来说，语言理解算法（词汇、语义、句法）与 XAI 功能（可解释性、可解释性、忠实度）集成，可以弥合分析师资源中的差距并缓解数据中的主观性问题。