By IBM data insights and AI practice leader, Abhishek Kaul, and Vale global AI projects leader, Ali Soofastaei
In recent years, there has been increased attention on the possible impact of future robotics and artificial intelligence (AI) systems.
Prominent thinkers have publicly warned about the risk of a dystopian future when the complexity of these systems progresses further. These warnings stand in contrast to the current state-of-the-art robotics and AI technology.
Digital transformation and applied automation are growing fast in the mining industry. It is essential to adapt to the mining industry with the related innovations, which play critical roles in the digital revolution.
The core of these innovations is applied machine learning (ML) and AI across the mining value chain.
Many of us would assume that the mining industry would have driven advances in robotics, automation, AI and ML due to the remote mine sites, the hazardous nature of the work and the high costs of labour and transport.
However, it is the manufacturing sector that has spearheaded most of the technological developments, but it is now the mining sector that is taking advantage of those proven technologies to help boost its recovery after a significant downturn.
In today’s highly efficient mining operations, making the right decisions depends on their 360-degree visibility of the business and the market, combined with accurate demand forecasting.
With huge footprints in remote locations, diverse labour forces, and complex and time-consuming projects, mining companies are using enterprise resource planning (ERP) and advanced analytics systems as the technology backbone to their businesses.
ML and AI are the main part of an advanced data analytics approach, which is increasingly being relied on to make decisions about people, processes and technologies, be it accessing worker productivity to explore the next mine site or predict to schedule equipment for maintenance.
Although AI and ML-based analytics are delivering results, its recommendations for people-based decisions are subject to ethical considerations. Issues arise if AI and ML models have a bias based on gender, age or ethnicity and is not fair in providing recommendations.
There are multiple AI policy guidelines available from the United States, Europe and Asia to help organisations ensure that they build and use ethical AI.
This article discusses AI use cases in the mining industry with ethical considerations, reviews critical challenges and potential bias mitigation strategies.
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.
Many of these techniques depend on ML. For example, the ability to automatically learn from historical patterns in data and improve performance over time.
The difference between AI and ML can be a little confusing. Figure 1 (below) illustrates the general boundaries between these concepts.
AI in the mining industry
Mining is a complex and fluctuating industry that is fraught with uncertainty around resource pricing, unpredictable resource fields and major projects that need to be managed right through their lifecycle.
Controlling costs for mineral exploration, construction and operation right through to project completion is a monumental challenge, but if the financial elements are managed well, it can help mining companies to be both competitive and profitable.
The key to increasing profits is knowing the precise time to increase production when there is strong demand using resource planning, improving the reliability of machinery with predictive and condition-based maintenance monitoring, delivering clarity with precise financial and operational reporting, and at the same time, providing actionable insights using real-time data extracted from every part of the organisation.
There are considerable benefits of using an AI system to improve the quality of work at a mine site and reduce the human failures and hazards.
AI use cases have been applied across the complete mining industry value chain from exploration, mine management, extraction, processing and transportation.
Data is fed into the AI systems. The data comes from a variety of sources like equipment, shift log, operator manuals, operator wearable, CCTV cameras, HR systems, shift rosters and more.
Although ML and AI, by their very nature, are always a form of statistical discrimination, the discrimination becomes objectionable when it places certain privileged groups at the systematic advantage and certain unprivileged groups at a systematic disadvantage.
Objectionable discriminations arise due to multiple reasons in the mining industry. The main reasons are:
Defining the business objective of the machine learning problem – for example, if the business objective is defined as maximum throughput without consideration for maintenance or safety aspects.
Unrepresentative data or data with existing prejudice for training – for example, if the AI model training data has been selected from a mine site where the demographic of the population is from the older age group.
Selecting the attributes or features for the ML model – for example, if in building the AI model, operator ethnicity has been included as a data point mobile mine equipment operator behaviour.
This article focuses on three main use cases of AI in mining:
Energy: ethics in reducing fuel consumption
Maintenance: ethics in predictive maintenance
Safety: ethics in using surveillance video (CCTV) for safety.
Policy guidelines for ethical AI
Many countries have published AI policy guidelines. These guidelines provide a broad level objective for the use of AI – to ensure human-centric, safe and trustworthy AI.
Most guidelines make the organisation using AI responsible and accountable for their decisions and ask for the same ethical standards in AI-driven decisions as in human-driven decisions.
General key points achieved from global guidelines are:
It should be lawful, complying with all applicable laws and regulations.
It should be ethical, ensuring adherence to ethical principles and values.
It should be robust, both from a technical and social perspective since, even with good intentions, AI systems can cause unintentional harm.
To ensure compliance with ethical guidelines, AI models need three capabilities.
Explainability – An ability to explain the behaviour of the black box AI model. Multiple algorithms help to explain the model. For example, decision tree rules if A > 50, then stop else continue, are easily understood by people.
Fairness – The ability of AI models to report and mitigate discrimination and bias. Depending on the application of the AI model, the appropriate bias metrics should be reported. For example, for hiring, a false-positive (someone unfit for the job is employed) is less harmful than a false negative (someone fit for the job is denied). Further, bias mitigation algorithms can be applied to improve the fairness metrics by modifying the training data, the learning algorithm, the predictions, the optimisation or the making decision models.
Transparency – The ability of the model to be transparent on training data, accuracy and performance, bias and fairness metrics so that users can understand how AI was trained and deployed.
AI use cases with ethical consideration for the mining industry
In this section, three use case details are presented with ethical consideration for AI in the areas of energy, maintenance and safety.
It is important to understand that AI is not about replacing people, but reskilling people and deployment of AI applications will improve the quality of work at a mine and reduce the human failures, hazards at the site.
Use Case 1: AI application for energy efficiency – ethics in reducing fuel consumption (consideration – operator demographics)
Fuel is an important cost contributor for haul trucks in surface mining. Multiple parameters affect fuel consumption like the type of truck, payload, distance, hours, weather and operator behaviour (includes speed, manoeuvring, acceleration and braking).
AI techniques like Artificial Neural Networks (ANNs) are generally applied to data to understand the top factors influencing fuel consumption and recommend changes to controllable factors, thereby reducing fuel consumption per tonne of ore mined by using a genetic algorithm (GA) in the optimisation phase of the project.
One of the predictors for haul truck fuel consumption is the operator (driver) behaviour. If demographic data points of a driver are included as attributes or features, then the AI model will identify patterns in demographic data that influence operator behaviour.
Depending on the training data set, for example, country, mine site, or number of operators, this analysis may have bias and may not hold true for the general case. Maybe the model can get biased to predict low fuel consumption for older male workers based on one mine site operation.
In such applications, it is recommended not to include demographic data in the analysis and rely solely on the unique mine equipment operator identifier. Unless essential, if the use of demographic data is needed, de-biasing techniques like reweighing, adversarial de-biasing should be applied with visibility on fairness metrics like statistical parity difference, Thiel index, and more should be enforced.
Ideally, the business objective of the AI model should be finetuned to provide guidelines to an operator to influence their behaviour – like speed, acceleration, etc, and provide a mechanism to monitor for deviations in operator actions to AI recommendations.
Use case 2: AI application in maintenance – Ethics in predictive maintenance (consideration – operator shift logs / NLP)
Equipment downtime significantly affects the productivity and safety of mining operations. The main goal of predictive maintenance is to shift the unplanned breakdowns to planned maintenance activities, increase the equipment lifetime, optimise maintenance schedules and ensure safe operations.
AI techniques in machine learning like cox regression, logistic regression, gradient boosting, neural nets are applied to predict the remaining useful life (RUL) and predict the health score of equipment using historical maintenance data.
Further, natural language processing (NLP) techniques like Word2Vec, BERT are applied on operator shift logs to gain a deeper understanding of operational events like faults, trips, overriding, noise, resetting observed, actioned and documented by the operator, which are then co-related to maintenance failures to provide deeper insights.
One of the data points used in the analysis to discuss further is the operator shift logs. Operator privacy is one of the considerations for analysing logs. However beyond privacy, when analysing operator logs, if language linguistics analysis is used for deciphering personal traits, personal attributes – like modelling and then to co-relate maintenance failures – inclusion of bias becomes a relevant topic and it is subject to ethical considerations.
In such applications, it is recommended to use the co-relation of events (nouns, verbs) with maintenance failures for providing slack time in operation for operators to do necessary maintenance inspections.
Further, it is recommended to de-bias the NLP models, which may cause some drop-in accuracy points but helps to keep the recommendations fair.
Use case 3: AI application in safety – Ethics in using surveillance video (CCTV) for safety (consideration – surveillance video data)
Video surveillance data (CCTV cameras) are used in many work areas to ensure the security and safety of the site. Typically, hundreds of cameras feed data to the site security/safety office.
Since it is not possible to review the feed from all cameras in real-time by the human operator, the use case for video surveillance trends towards post-facto video retrieval and analysis for historical incidents, disputes.
With advancements in computer vision technologies, AI models are trained with the surveillance video feed to perform automated analysis for object detection, object classification, object tracking, and raise proactive alerts in real-time to detect and mitigate any safety or security violations.
Surveillance is itself an ethically neutral concept. What determines the ethical nature of a particular instance of surveillance will be the considerations which follow, such as justified cause, the means employed and questions of proportionality.
While it can be argued that monitoring remotely via a camera is no different from historical times when security personnel were physically present at the worksite, there are local privacy laws and regulations which must be complied with while using surveillance, face recognition technologies.
AI technologies in computer vision are leapfrogging every few months, like being able to understand the Spatio-temporal relationships of objects, which can be used to monitor people’s behaviour based on the change of posture, time of day, relationship with equipment, movement between areas, and more.
Ethical considerations when employing AI for surveillance monitoring should have a balance between workers privacy-trust-autonomy and workers’ safety-security-behaviour.
Understanding the implications of ethics in AI is important for mining companies to remain fair to their workforce as they are in human-driven decisions.
Mining companies should adopt and build AI solutions that follow leading policy guidelines and are explainable, transparent and fair. Further, when evaluating the AI solution, they should understand how the solution has built the data used in training the AI models.
In order to enforce ethical considerations for AI, mining companies can appoint an AI ethics officer or committee for the review of each AI application being developed or purchased from vendors and request disclosure on the fairness, explainability and transparency metrics.
Soofastaei Ali, and et al. “Reducing Fuel Consumption of Haul Trucks in Surface Mines Using Genetic Algorithm,” Applied Soft Computing Journal, Under Press (2020).
Soofastaei Ali, and et al. “The Effect of Average Truck Speed on Fuel Consumption in Surface Mines,” Mining Journal, Volume 4, Issue 2, (2016), P:92-94.
Everyday Ethics for Artificial Intelligence – IBM