ESGlass: Glass-Box ESG and Sustainability Reports
ESGlass: ガラスボックス型ESG・サステナビリティレポート (AI 翻訳)
Chaoyue He, Xin Zhou, Di Wang, Hong Xu, Wei Liu, Chunyan Miao
🤖 gxceed AI 要約
日本語
本研究は、ESG報告の単位を「開示クレーム」に据え、証拠・プロセス・不確実性を紐付けるガラスボックス型システムESGlassを提案。マルチモーダルな証拠と生成AI時代の課題に対応し、透明性の高い報告を実現するための研究課題とプロトタイプを示す。
English
ESGlass redefines ESG reporting units as interactive evidence objects, binding multimodal observations, provenance, and stakeholder-specific views. It addresses generative AI's narrative cheapness, proposing graph-based claim-evidence structures, minimal sufficient evidence, challenge retrieval, and selective abstention, with a prototype in energy disclosure.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本ではSSBJ基準の適用が迫られ、実証可能な開示が求められる。ESGlassのガラスボックス型アプローチは、企業が有報や統合報告書で主張を裏付ける証拠管理の枠組みとして参考になる。
In the global GX context
Globally, as ISSB and CSRD demand auditable sustainability disclosures, ESGlass offers a concrete system for transparent, evidence-backed reporting, directly combating greenwashing and aligning with regulatory trends toward data provenance and drill-down capabilities.
👥 読者別の含意
🔬研究者:A novel framework for ESG disclosure systems, defining task families and evaluation criteria for glass-box reporting.
🏢実務担当者:Provides a blueprint for building evidence-based reporting systems that can withstand scrutiny and support assurance.
🏛政策担当者:Highlights the need for disclosure standards that require provenance and minimal sufficient evidence, not just narrative.
📄 Abstract（原文）
We introduce ESGlass, a glass-box paradigm for ESG and sustainability reports. Instead of treating the report page, file, or tagged fact as the native unit, ESGlass treats each material disclosure claim as an interactive evidence object that binds multimodal observations, derived computations, provenance, uncertainty, and stakeholder-specific renderings. This shift matters because sustainability evidence is increasingly dispersed across invoices, tables, sensor streams, forms, maps, satellite imagery, facility video, and in ternal workpapers, while generative AI makes polished but weakly supported narrative cheap to produce. Building on progress in ESG benchmarks, sustainability knowledge infrastructure, document AI, multimodal retrieval, agents, geospatial foundation models, and provenance standards, we formalize the report as a policy conditioned view over claim–evidence–provenance graphs and distinguish asset provenance from claim provenance. We argue that glass-box reporting demands stronger targets than citation-style grounding, including minimal sufficient evidence sets, challenge retrieval, replayable transformations, omission semantics, and selective abstention. We outline a one-company energy-disclosure prototype, define task families and evaluation criteria, and surface governance issues such as selective disclosure, privacy-preserving drill-down, and false completeness. ESGlass reframes ESG and sustainability reports from polished narrative artifacts into inspectable multimedia disclosure objects, offering a concrete multimedia research agenda for systems that must not only generate disclosure, but also defend it.
🔗 Provenance — このレコードを発見したソース
- openaire https://doi.org/10.20944/preprints202603.2187.v1first seen 2026-05-14 22:07:59
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