With the ever-expanding use of artificial intelligence ("AI") across a wide range of industries, its application in the mining sector is one to watch. In a recent report, Deloitte named AI as one of the top five trends to be shaping the mining and metals sector.
In this bulletin, we provide an overview of how AI is currently being used in the mining sector, as well as how it may be used in the future, and key legal considerations that may arise from its deployment in the mining industry.
Current uses of AI in Mining
We have set out below some of the primary categories in which there are material developments for the use of AI in the mining sector.
Exploration and Economic Analyses
AI, and deep learning models in particular, are currently being used to analyze and process geoscience data obtained during exploration of mineral properties, such as geologic, geophysical, mapping and mineralogical data. Such AI-driven analyses may assist in making complex predictions regarding the nature of mineral deposits located on mining properties. AI has further been deployed to combine outputs of such data with analyses on the economic feasibility of extraction. AI may also inform decisions on effective mine-site designs that allow for the optimization of profit.1
The public availability of precompetitive geoscience data, which consists of "geological, geophysical, geochemical and other types of data" is crucial for such AI analyses in exploration and feasibility studies.2 The government of Canada's Critical Minerals Geoscience and Data Initiative (CMGD), currently funds multiple projects aimed at enhancing access to data regarding the location, quality and economic feasibility of critical minerals and resources. These efforts are expected to improve the accessibility and quality of geoscience data, which may further enable the use of AI in mineral exploration, site planning, and the evaluation of economic feasibility.3
Operations and Safety
The use of AI is being explored as a way to improve collision-avoidance technology on mine sites. Current collision-avoidance systems employ sensors that signal vehicle-operators based on proximity alone, often resulting in frequent false alarms. Integrating AI systems may allow for collision-avoidance systems to distinguish proximity arising out of normal operations from dangerous proximity, and sound alarms that are more accurate.4 Some companies have implemented AI for the development of autonomous mining haulage trucks, which may also reduce the risks of human error and improve overall operational efficiency.5
In addition to avoiding collisions, AI tools are being developed to limit workers' exposure to hazardous materials and dangerous situations. For example, AI systems have been connected with sensor systems to autonomously monitor atmospheric conditions and identify dangerous conditions.6
Environmental Compliance
AI has the potential to assist with environmental compliance by monitoring and identifying incidents at an early stage. For example, computer vision, a subtype of AI that combines camera and sensor systems with an autonomous AI monitoring system, can allow for real-time detection of mining sites for environmental incidents, as well as general waste monitoring.7 Computer vision can also be used to track surrounding wildlife population and biodiversity metrics, helping reduce costs associated with environmental monitoring requirements.8
AI-assisted analytics can also assist in optimizing resource utilization to reduce waste, monitor and reduce greenhouse gas emissions, and improve water conservation systems in the mineral processing stage.9
In sum, AI's ability to quickly and efficiently analyze large datasets, optimize efficiencies and minimize risk across various stages of the mining process (exploration, extraction, processing, logistics and reclamation) makes it a promising tool for cutting the costs associated with exploration and mining projects.
Possible Legal Considerations
The use of AI in the mining sector does require consideration of the relevant implications from a legal perspective across many areas of law, including data privacy, technology and mining law. The expansion of the application of AI has also been met with new and anticipated regulatory changes to address certain risks unique to AI, such as bias, lack of transparency and accountability and misleading or "hallucinated" results.
Data, Confidentiality and Privacy Considerations
Mining transactions frequently involve exchanges of confidential information. If such confidential information or data is to be processed by AI tools, parties will want to be careful to implement robust confidentiality provisions and conduct necessary cybersecurity diligence in order to ensure safe processing of sensitive information and specify where responsibilities should lie in the event of data breaches. Data breaches may have significant financial implications, especially if they impact critical infrastructures in major projects. Additionally, should the data being collected or processed include any personal information (for example, images or geographic locations of employees), a data breach may further trigger liabilities and reporting requirements associated with provincial or federal privacy laws.10
Further, AI systems may generate new data derived from such collected or otherwise inputted data. The ownership of such data can become a material issue among the various parties if not addressed at the outset and therefore, when deploying such tools, it is crucial for parties to specify who will retain ownership and/or usage rights to such generative data.
Liability for Safety, Quality and Accuracy
Despite the convenience it offers, autonomous decision making is not immune to errors. In particular, serious questions regarding responsibility may arise where an error by an AI tool leads to harm to individuals, equipment or the environment. As such, for manufacturers, distributors and any entities using AI to provide services in the mining sector, appropriately worded liability waivers will be essential to mitigating the risks of such forms of liability. As a risk-mitigation measure for automated decisions with significant consequences, parties may wish to contractually oblige a "human-in-the-loop" model, where there is human oversight of such automated decisions.
Even in situations where there are no risks to personal and environmental safety, vendors and providers of AI tools will want to consider including disclaimers of representations and warranties regarding the quality and accuracy of the output and ensure all parties understand how AI tools may not always yield consistent results. The mining companies, on the other hand, will want to have provisions which give them fairly robust protections and remedies against the vendors and providers of AI tools in the event of damages arising due to the use of an AI tool.
AI Legislation and the Voluntary Code
At the time of the publication of this bulletin, there is no legislation in Canada that specifically regulates the use of AI. However, prior to the 2025 federal election, the federal government had introduced the Artificial Intelligence and Data Act (AIDA) as a possible legislative approach to address the risks associated with the use of AI, such as imposing risk-mitigation measures for "high-impact systems." 11 In particular, the government had released the Artificial Intelligence and Data Act (AIDA) – Companion document, which provided an overview of possible regulatory requirements which would be addressed in the legislation, such as (i) human oversight and monitoring, (ii) transparency, (iii) fairness and equity, (iv) safety, (v) accountability and (vi) validity and robustness.12 These requirements mirror the Voluntary Code of Conduct on the Responsible Development and Management of Advanced Generative AI Systems ("Voluntary Code") that was implemented by Innovation, Science and Economic Development Canada in September 2023.13
AIDA was one of many pieces of legislation that "died on the order table" as a result of the prorogation of Parliament in January that followed the resignation of former Prime Minister Justin Trudeau. Given that the federal Liberal party was successfully re-elected as the government of Canada, there is reason to believe that AIDA or something similar with respect to the legislation and regulation of AI in Canada may be reintroduced. The Voluntary Code remains the only set of rules specifically addressing issues arising from AI although, as the name indicates, participation is voluntary and so there are no material consequences for failing to comply. As of June 2025, the Voluntary Code has 46 signatories, who have undertaken to commit to achieve the above list of outcomes when developing AI systems. AI developers in the mining sector may wish to become a signatory of the Voluntary Code to demonstrate their commitment to responsible development of AI systems.14
Public Company Disclosure Requirements in the Mining Sector
On December 5, 2024, the Canadian Securities Administrators (CSA) published Staff Notice and Consultation 11-348, Applicability of Canadian Securities Laws and the Use of Artificial Intelligence Systems in Capital Markets (the "CSA Staff Notice").15 Among other guidance related to the intersection of AI and capital markets, the CSA Staff Notice states that public issuers who are using or intending to use AI as part of their business must meet their continuous disclosure obligations by facilitating an investor's understanding of the use of AI and its risks, such as:
- Describing how the issuer defines "artificial intelligence" and whether the use of AI is material or expected to be material, including whether AI is developed internally or supplied by a third party;
- Disclosing the material risks that the use or intended use of AI presents to the issuer, and any measures taken to manage or mitigate such risks;
- The impact that the use or intended use of AI is likely to have on the issuer's business, operations and financial position; and
- The material factors or assumptions used to develop forward-looking information about the prospective or future use of AI and any updates to previously disclosed forward-looking information.
Public companies in the mining sector who wish to use or develop AI as part of their business should be attuned to these unique requirements for continuous disclosure. It would also be prudent for them to proactively assess whether any third-party AI systems integrated into operations may introduce risks or dependencies that warrant disclosure, particularly where such systems impact key functions like resource estimation, environmental monitoring, or operational decision-making.
Disclosure for Mineral Projects
In addition to the above, public companies that engage in mining exploration and development are required to meet disclosure requirements pursuant to National Instrument 43-101 Standards of Disclosure for Mineral Projects and Form 43-101F1 Technical Report.16
At the time of the publication of this bulletin, Canadian securities regulators have not provided formal guidance on how the use of AI in mining would affect such disclosure requirements. However, as the use of AI in mining continues to expand, we can expect to see further guidance, especially with respect to the Technical Report, regarding how the use of AI may affect disclosure requirements specific to mining. In the meantime, public companies in the mining sector should consider whether the use of AI in resource modeling, data interpretation, or other material aspects of project evaluation warrants voluntary disclosure to ensure transparency and maintain investor confidence.
Conclusion
As the use and prevalence of AI continues to grow, regulations on how those AI tools can be used will likely also continue to become more common and more prescriptive. Given the highly regulated nature of the mining sector and how AI tools specific to the mining sector will continue to develop and expand, it is going to become important for mining companies and vendors and suppliers to the mining industry to stay on top of all legal developments to ensure full compliance. McMillan's lawyers in both the Technology and Mining industry groups are available to provide key guidance to navigate this evolving regulatory landscape.
Footnotes
1. Caitlin C. Corrigan and Svetlana A. Ikonnikova, "A review of the use of AI in the mining industry: Insights and ethical considerations for multi-objective optimization" (2024), The Extractive Industries and Society 17: 101440 [Corrigan and Ikonnikova].
2. "Tracking the trends 2025: Leading through transformational change in mining and metals" (2024), Deloitte, available here.
3. "Critical Minerals Geoscience and Data Initiative – Call for proposals" (2024), Government of Canada, available here.
4. Andrew Tunnicliffe, "Can AI enhance mining's collision avoidance technology?" (2025), Mining Technology, available here.
5. Corrigan and Ikonnikova, supra note 1.
6. Corrigan and Ikonnikova, supra note 1.
7. "Artificial Intelligence in Mining: From digging to data" (2025), Canadian Mining Journal, available here.
8. Corrigan and Ikonnikova, supra note 1.
9. Anthony Milewski, "Embracing AI And Emerging Technologies Can Help Transform The Mining Industry" (21 Jan 2025), Forbes, available here.
10. For example, under the Personal Information Protection and Electronic Documents Act, SC 2000, c 5 organizations must report data breaches involving personal information to the Privacy Commissioner of Canada where there is a "real risk of significant harm" to individuals and notify individuals affected.
11. Bill C-27, An Act to enact the Consumer Privacy Protection Act, the Personal Information and Data Protection Tribunal Act and the Artificial Intelligence and Data Act and to make consequential and related amendments to other Acts, 1st Sess, 44th Parl, 2021, (second reading 24, April 2023) available here.
12. "The Artificial Intelligence and Data Act (AIDA) – Companion document" (2025), Government of Canada, available here.
13. "Voluntary Code of Conduct on the Responsible Development and Management of Advanced Generative AI Systems" (2023), Innovation, Science and Economic Development Canada, available here.
14. For further information related to how certain regulatory principles may apply to AI technologies, see our following bulletin: Kristen Pennington, Robert C. Piasentin, Robbie Grant & Stephen Johnson, "Developing, Offering and Using Generative AI Technologies: Canadian Privacy Regulators Weigh In", McMillan LLP, (3 January, 2024), available here.
15. Applicability of Canadian Securities Laws and the Use of Artificial Intelligence Systems in Capital Markets, CSA Staff Notice 11-348, (5 Dec 2024) available here; for more information on the CSA Staff Notice, see our recent bulletin, "AI and Canadian Capital Markets: CSA Guidance for Non-Investment Fund Reporting Issuers," available here.
16. NI 43-101 (25 Jul 2023), British Columbia Securities Commission, available here; Form 43-101F1 (24 Jun 2011), British Columbia Securities Commission, available here.
The foregoing provides only an overview and does not constitute legal advice. Readers are cautioned against making any decisions based on this material alone. Rather, specific legal advice should be obtained.
© McMillan LLP 2025
