Generating a Global Industry Standard on Tailings Management knowledge base: harnessing artificial intelligence for enhanced decision-making in tailings management

Authors: Gibbons, S; Tomlin, C

Open access courtesy of:

DOI https://doi.org/10.36487/ACG_repo/2515_66

Cite As:
Gibbons, S & Tomlin, C 2025, 'Generating a Global Industry Standard on Tailings Management knowledge base: harnessing artificial intelligence for enhanced decision-making in tailings management ', in S Knutsson, AB Fourie & M Tibbett (eds), Mine Closure 2025: Proceedings of the 18th International Conference on Mine Closure, Australian Centre for Geomechanics, Perth, pp. 1-19, https://doi.org/10.36487/ACG_repo/2515_66

Download citation as:   ris   bibtex   endnote   text   Zotero


Abstract:
The modern world is accustomed to seeing news articles either praising the use of artificial intelligence (AI) or lamenting the dangers it poses to humanity. Large corporations integrate AI into workflows to facilitate faster and more in-depth analyses of vast data sets. But how does general AI fit into everyday work lives? Is one confined to asking generic or censored questions to public AI tools, or, if fortunate, accessing some form of in-house AI support? The good news is that AI use is not limited, and a multi-million-dollar implementation is not needed to embark on this journey. This paper explores how AI tools have been utilised, and specifically how it can support a range of requirements under the Global Industry Standard on Tailings Management (GISTM) by helping collate and curate knowledge on tailings, as well as reviewing archived documents and studies. It also investigates how that information can be interrogated, free from the realm of misinformation on the wider web, as well as provide an analysis of how portfolios of sites can share data and, when necessary, make comparisons with publicly available information. The intent of these tools is to enable gathering of information that aids in making better decisions and identifying areas requiring deeper understanding.

Keywords: GISTM, tailings, knowledge base, artificial intelligence, mining

References:
Anthropic 2023, Claude 2, large language model, 
Barnewold, L & Lottermoser, BG 2020, ‘Identification of digital technologies and digitalisation trends in the mining industry’, International Journal of Mining Science and Technology, vol. 30, no. 6, pp. 747–757.
Barros, DA, Pereira, JAA, Ferreira, MM, Silva, RA & Nascimento, GO 2020, ‘Integrated management of mining enterprises to achieve the Sustainable Development Goals’, Sustainability, vol. 12, no. 5.
Bommasani, R, Hudson, DA, Adeli, E, Altman, R, Arora, S, von Arx, S, … Liang, P 2022, ‘On the opportunities and risks of foundation models’, Journal of Machine Learning Research, vol. 24, no. 166, pp. 1–212.
Chalkidis, I & Kampas, D 2019, ‘Deep learning in law: early adaptation and legal word embeddings trained on large corpora’, Artificial Intelligence and Law, vol. 27, no. 2, pp. 171–198, 
Chiesa, C, Robertson, A, Waygood, C & Tingey, S 2021, ‘An industry guide to the Global Industry Standard on Tailings Management’, Mine Environment Management, special issue on tailings management.
Chui, M, Manyika, J, Miremadi, M, Henke, N, Chung, R, Nel, P & Malhotra, S 2018, Notes from the AI frontier: Insights from hundreds of use cases, McKinsey Global Institute.
Davenport, TH & Ronanki, R 2018, ‘Artificial intelligence for the real world’, Harvard Business Review, vol. January–February 2018, pp. 108–117.
Deloitte 2022, The AI Dossier: Managing Risk and Trust for the Top Uses for AI in Every Major Industry, London, viewed 16 June 2025, 
Ghasemaghaei, M & Calic, G 2020, ‘Assessing the impact of big data on firm innovation performance: Big data is not always better data’, Journal of Business Research, vol. 108, pp. 147–162.
International Council on Mining and Metals, United Nations Environment Programme & Principles for Responsible Investment 2020, Global Industry Standard on Tailings Management, 
global-industry-standard-on-tailings-management.pdf
Goebel, M & Fülöp, L 2020, ‘The value of information management in the mining industry’, Mining, Metallurgy and Exploration, vol. 37, pp. 1825–1835.
Jha, SK, Dwivedi, A, Tiwari, MK & Rana, NP 2022, ‘AI-enabled knowledge management for sustainable mining: a research agenda’, Journal of Cleaner Production, vol. 330.
Johnson, AE, Bulgarelli, L & Pollard, TJ 2021, ‘Artificial intelligence in pharmaceutical research and development: a review’, Drug Discovery Today, vol. 26, no. 1, pp. 80–88.
Katic, M & Majstorović, V 2022, ‘Knowledge management in high-risk industries: a quantitative analysis’, Safety Science, vol. 146.
McKinsey & Company 2023, The State of AI in 2023: Generative AI’s Breakout Year, McKinsey Global Survey,
Morgenstern, NR, Vick, SG, Viotti, CB & Watts, BD 2016, Report on the Immediate Causes of the Failure of the Fundão Dam, Fundão Tailings Dam Review Panel.
Mikalef, P, Pappas, IO, Krogstie, J & Giannakos, M 2018, ‘Big data analytics capabilities: A systematic literature review and research agenda’, Information Systems and e-Business Management, vol. 16, no. 3, pp. 547–578.
OpenAI 2023, GPT-4, large language model, 
Prabhakar, A, Mosur, V, Cox, D 2021, Rethinking your approach to AI, IBM Institute for Business Value, Armonk, 
Samimi, A & Najafzadeh, M 2021, ‘Return on investment for digital technologies in mining: A quantitative analysis’, Resources Policy, vol. 71.
Schoenberger, E 2016, ‘Environmentally sustainable mining: the case of tailings storage facilities’, Resources Policy, vol. 49, pp. 119–128.
Touvron, H, Lavril, T, Izacard, G, Martinet, X, Lachaux, M-A, Lacroix, T, ... Scialom, T 2023, LLaMA 2: Open Foundation and Fine-Tuned Chat Models, Large language model, Meta,
Vergara, D, Rubio, MP & Lorenzo, M 2021, ‘On the use of digital twins in mining operations’, Applied Sciences, vol. 11, no. 14.
Vidal-Silva, C, Paredes-Belmar, G, Serón, J, Madariaga, E & Rodríguez, R 2021, ‘Knowledge management systems and analytics in the mining industry: a systematic literature review’, Sustainability, vol. 13, no. 4.




© Copyright 2025, Australian Centre for Geomechanics (ACG), The University of Western Australia. All rights reserved.
View copyright/legal information
Please direct any queries or error reports to repository-acg@uwa.edu.au