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.

翻译：可持续性通常指个体、公司及机构等实体对环境、社会和经济不产生损害性（甚至具有积极）影响。随着可持续性逐渐成为可接受且合法行为的代名词，其正受到日益严格的监管要求。目前已提出若干衡量企业可持续性影响的框架与标准，包括联合国可持续发展目标及近期推出的全球可持续性报告框架等。然而，由于企业运营具有多样性与复杂性特征（涉及地域、规模、业务活动、与其他利益相关方的关联等），企业可持续性概念本身极为复杂。这导致企业可持续性评估在反映企业可持续性实践的数据（即企业可持续性披露信息）和评估分析师两个层面均存在主观性问题。此类主观性可具体表现为数据维度的不完整性、模糊性、不可靠性与复杂性，以及分析师维度的资源有限性与潜在偏见。综合而言，主观性会阻碍对不符合主流可持续性期望的实体进行有效的成本归因，可能使可持续性实践及相关监管措施失去效力。为此，我们认为可解释自然语言处理（XNLP）能显著提升企业可持续性分析效能。具体而言，融合可解释人工智能能力（可解释性、可说明性、忠实性）的语言理解算法（词汇、语义、句法层面），能够弥补分析师资源缺口并缓解数据中的主观性问题。