ID: 1691
Presenting Author: Bulgan Batdorj
Session: 675 - Transparency to Trust: Communicating Complex Science and Data Effectively
Status: pending
The paper retheorizes the transparency framework by synthesizing four transparency frameworks, complements them with expert interviews, and identifies AI/NLP intervention points to reframe how transpa
Transparency is widely recognized as a pathway to accountability and trust in the extractive industries, yet the mechanisms linking disclosure to these outcomes remain fragmented. This paper re-theorizes the causal mechanism of transparency by synthesizing four established frameworks in environmental governance and resource disclosure, complemented by sixteen expert interviews with practitioners and analysts of mining disclosures. Grounded in IAIA principles of inclusivity, scientific rigor, and ethical practice, the study explores how artificial intelligence (AI), specifically natural language processing (NLP), can intervene at key stages of this causal chain to enhance how information is processed, accessed, and communicated.
The revised framework identifies strategic intervention points where AI tools can strengthen transparency processes, through tools of NLP such as data classification, summarization, extraction, and synthesis of complex reporting for diverse audiences. Rather than replacing human judgment, AI is positioned as a technical complement that enhances clarity, accessibility, and efficiency—reducing the time required for these tasks and improving the preconditions for building trust. The resulting framework offers reframe of how transparency can foster trust and provides practitioners with a structured logic for responsibly integrating AI into future disclosure and impact assessment systems.
The paper acknowledges some of the pitfalls of AI, recognizing that these aspects require further work to ensure that AI applications deliver positive net benefits.
Bulgan Batdorj is a PhD candidate at Institute of Resources, Environment and Sustainability, researching mining governance, sustainability performance and AI-driven transparency.
Coauthor 1: Nadja Kunz