Introduction
Robotics, and hence artificial intelligence, has been widely used in the mining industry in recent years. The automation of tasks is making considerable progress in the mining industry. This extensive use of AI is raising questions in the scientific community, particularly as regards the ethical and operational aspects. In today’s mines, effective decision-making is heavily dependent on the increased visibility and accuracy of predictive analysis tools based on Machine Learning. The analysis of large volumes of data in record time by these tools helps mining companies to improve their processes and manage resources efficiently. In this article, we discuss the ethical and operational challenges associated with the adoption of AI in the mining industry.
Understanding ethics in AI
AI is intelligence demonstrated by machines, unlike the natural intelligence displayed by humans. AI refers to systems that display intelligent behaviour by analysing and interpreting the data, learning patterns in data, provide reasoning and recommendations, and optionally take actions with some degree of autonomy to achieve trained goals. AI systems work very well at use cases where they involve recognising patterns with large quantities of data. AI systems work best together with people, and it is important to understand that AI requires reskilling people, not replacing them. There are many AI techniques like supervised learning, unsupervised learning, reinforcement learning, transfer learning, knowledge graphs, reasoning systems, and more (Writer, 2021).
Ethical challenges in AI adoption in mining
The use of AI in the mining industry poses enormous ethical challenges that deserve special attention to ensure responsible use.
Bias in AI models
Artificial intelligences are trained with precise databases depending on the objective and the information the designer wishes to disseminate. For example, if an AI is trained and tested with data relating exclusively to Dumpers, it is highly likely to return incorrect information if a user ever requests information about articulated lorries.
Displacement of workers
The extensive use of AI in the mining sector is calling into question the place of humans in this sector, which until now has been a major employer. This question is particularly relevant in countries such as South Africa and Australia, where mining activities are significant.
Privacy and Surveillance
The use of AI for monitoring and surveillance raises ethical questions about workers’ privacy and autonomy. Striking a balance between safety and privacy is essential to maintain trust among employees.
Accountability and transparency
AI systems often operate as “black boxes,” making it difficult to understand how decisions are made. This lack of explainability can lead to mistrust and challenges in accountability when errors occur.
Operational challenges in AI adoption in mining
Apart from the ethical challenges, the adoption of AI in the mining industry poses a number of problems from an operational point of view.
High initial costs
Setting up an effective AI system in a mining company requires substantial resources for the acquisition of hardware and infrastructure, particularly robust hardware and high-performance software. For some companies, AI remains a luxury reserved for high-income companies.
Data quality and availability
To make good predictions, it is important to have well-designed databases. Many companies do not necessarily have the capacity to collect and process data. This remains a constant challenge, especially for junior mining companies.
Resistance to change
The mining industry has long-standing practices that may hinder the adoption of new technologies. A cultural shift is often necessary, requiring comprehensive training programs to equip workers with the skills needed to utilize AI effectively.
Long development cycles
The asset development cycle in mining can take 10-20 years, which complicates the timely integration of AI solutions into existing processes. Shortening this cycle through AI-enhanced methods could significantly benefit the industry but requires careful planning and execution (P.Eng, 2024).
Conclusion
The successful adoption of AI in mining hinges on addressing both ethical and operational challenges. Companies must ensure that their AI applications are designed with fairness, transparency, and accountability in mind while also investing in the necessary infrastructure and training to overcome operational barriers. By doing so, they can harness the potential of AI to enhance efficiency and sustainability within the industry while safeguarding their workforce’s interests.
Reference
P.Eng, N. H. (2024, May 8). How to successfully adopt AI technology in mining. The Intelligent Miner. https://theintelligentminer.com/2024/05/08/how-to-successfully-adopt-ai-technology-in-mining/
Writer, S. (2021, June 7). Ethical considerations of artificial intelligence in mining. Australian Resources & Investment. https://www.australianresourcesandinvestment.com.au/2021/06/07/ethical-considerations-of-artificial-intelligence-in-mining/
