Mary-Anne Hildebrandt, P. Geo., FGC Energy and minerals power our lives, and while we Canadians live in a land of abundance, global projections of natural resource consumption far exceed what is readily available. Economic geologists are increasingly challenged to locate near-surface mineral resources that are feasible to extract. Today’s evolving business models focus on creating more precise, surgical methods of extraction, not only to reduce the environmental impact of mining, but also to unlock economic potential in deposits that, in the past, would not have met the threshold for Reasonable Prospects for Eventual Economic Extraction (RPEEE). In parallel, we are working in the era of Big Data. In 2020, NASA reported that its Earth Science data collection had reached 40 petabytes (PB), a unit 1000 times the size of a terabyte (TB) and that this collection was expected to expand to 250 PB within six years. Geoscience data in the mineral and mining industry follows a similar trend. Big data and increased complexity of the deposits make it essential that Professional Geoscientists (P. Geos) leverage Artificial Intelligence (AI) because traditional methods of analysis are often insufficient to handle the scale and complexity of modern datasets. There are many companies and software developers trying to deliver solutions that produce high-quality results in real-time such as geochemistry, mineralogy, and structural measurements collected and analyzed in real-time at a drill rig using either powerful downhole tools or field core scanners. Some experts in the field believe that these advancements may reduce the necessity for geoscience professionals; however, it is far more likely that AI will strengthen our ability to understand, analyze, and model vast amounts of data, giving us insights that would otherwise be unattainable in a short amount of time. Consider society’s approach to earthquakes and how advanced we have become in creating predictive models over the last century. There are AI applications in use today that allow P. Geos the ability to analyze large datasets that aid in the creation of robust, predictive models. These detailed models allow P. Geos to have a better understanding of risk to the public and offer greater guidance to inform early warning systems. By integrating AI into our professional toolkit, we will be able to make more informed decisions, streamline complex analyses, and better allocate limited resources.  Professional regulators, such as Professional Geoscientists Ontario (PGO), also stand to benefit from AI. For example, AI could be used to identify patterns and anomalies that would allow regulators to detect unethical behaviour more effectively. Rather than waiting for another Bre-X-type incident, where a company reported falsified assay results that inflated the gold reserves to attract investors, it is in the public’s best interest for our profession—and its regulators—to develop advanced tools to stay ahead. It is certain that bad actors will also leverage AI to attempt to bypass existing legal frameworks, making it essential to reinforce our defences. However, as powerful as AI is, its use must be carefully guided. As a profession, we bear the responsibility to harness AI in ways that ensure its development and applications remain ethical, transparent, and fair. We need greater dialogue and a robust framework to guide this evolving area. Inherently, AI lacks an understanding of ethical intentions. It makes decisions based on the algorithms and code designed by humans, as well as the dataset on which it is trained. However, developers have the ability to instil AI with ethical or unethical subroutines either intentionally or unintentionally. Developers might choose to design it with an underlying malicious intent, such as spreading misinformation, or creating harmful automated decisions that negatively affect individuals, groups, or the natural world. Unintentional biases could also emerge if the AI is trained on a limited or biased dataset, leading to skewed decisions or outputs. For example, a mining company using AI to identify mineral exploration targets might train the system on a flawed dataset that lacks sufficient diversity in the geological dataset used for training. As a result, the AI could misunderstand key aspects of the mineralization controls if its training dataset is incomplete or biased. This could lead to the AI relying on surface-level features like rock type or mineral traces that resemble those found in resource-rich areas. However, without accounting for other critical factors such as depth, geological history, geochemistry, alteration, or other favourable conditions needed for mineral formation, the AI might incorrectly predict the presence of valuable deposits in barren areas. This misinterpretation could lead to the company and its shareholders to invest millions in unproductive exploration, wasting time and money. Meanwhile, the AI might overlook other areas with better prospects due to the skewed training dataset. Without the critical and well-trained eye of a P. Geo. guiding AI and validating its outputs, AI has the potential to cause not only financial losses, but also unnecessary environmental damage. PGO’s Code of Ethics calls on P. Geos to demonstrate “integrity, competence and devotion to service and to the advancement of human welfare”, and it is imperative that AI systems embody these principles as part of the original code. Responsible integration of AI is critical to upholding the public’s trust in our profession, and a balanced approach is needed to ensure that we seize opportunities while safeguarding the public and the natural world from harm. After using the wrong ingredients to bake a cake, Anne Shirley reflects “…isn’t it nice to think that tomorrow is a new day with no mistakes in it yet?” (Anne of Green Gables, Ch. 21, by L. M. Montgomery). In the age of AI, geoscientists must remain committed to continuous learning and ethical practice. AI will inevitably become more integrated in our daily activities. To ensure that AI has the right ingredients that will prevent harm to society or the natural environment, we need greater discussion and more collaboration with each other and our peers in data science to build a framework that guides the use of AI in geoscience practice. A proactive approach focusing on the development of an