IMPLEMENTATION OF AI IN MINING

The project aims to leverage lessons learned and case study examples from experience applying AI in mining to develop an implementation guideline and develop a roadmap for the industry so that AI applications can be scalable.

KEY TOPICS

  • Business case: defining a decision-making process, the implications of AI and more
  • Setting the approach, business model and structure, project management, KPIs, and more
  • Change management and human factors: Defining the journey and resources needed, key stakeholders’ involvement, adoption cycle for workforce, training plan and more
  • Ethics: Understanding the risk of bias and the potential implications of AI
  • Data considerations: data models, data reconciliation, leveraging data analytics methodologies, intellectual property and more
  • Business operations: risk management, safeguards considerations and decision-making
  • Defining acceptance criteria, testing frameworks, infrastructure, cybersecurity, scalability and more

PROJECT HISTORY

2020 Sept | Project calls and guideline development

Four project calls were held during September, in which volunteers were hands on the guideline, constantly contributing content and sharing resources. Key topics include components and structure of a business case (implementation plan, KPIs, risk assessment, organizational alignment, executive summary, etc.), AI strategy, capturing value, knowledge transfer process using mentoring/reverse mentoring for education opportunities, etc.

2020 Aug | Project team meeting and enrollment to subgroups

The project team gathered and begin with the initial distribution of sections of the guideline. GMG divided the project into four subgroups, and reached out to those with an interest in participating to enroll to the subgroup(s) of their expertise.

2020 Jun | Virtual Workshop

Workshop participants filled out tables about priorities for the sections and worked on content. Two new sections have been added: Ethics and Education; as it was a recurring topic in all workshops. Ethics will focus on frameworks to address bias in the data and understanding of ethical implications. Education will include training all employees with AI basics, leverage existing domain knowledge, learn to collaborate, cultural commitment, among other. 

2020 May | Virtual Workshop #2

Workshop participants filled out tables about priorities for the sections and worked on content. To access the outcomes, please click here.

2020 May | Virtual Workshop #1

The first virtual workshop defined the context-setting activity for the project. From a business perspective, key topics were around understanding the value of data, trusting AI throughout the model lifecycle, proof of concept, among others. From a technical standpoint, key topics were around data model governance, human machine interface, bias, reliability and testing, and other. Click here to access the outcomes from the workshop.

2020 Feb | Workshop in Denver, CO

During the Denver workshop on Feb 27, the project team identified the stakeholder matrix, suggested topics, defined the action items and strategized on the future of AI in mining. Click here to access the full summary.

2019 Oct | Project Launch

Workshops were held in October to develop a table of contents and start defining content. Currently, the document is still in its early stages of development. If you are interested in becoming an active contributor, please contact any GMG staff.

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