The "green transition" towards renewable energy and industrial processes will require a significant increase in the consumption of metals and minerals, which will involve expanding mining operations. The article explores the use of AI to extract valuable resources and mitigate the negative impacts of mining on local communities. AI can aid companies and governments in monitoring mine site violations, issues in mineral processing, and raw material supply chains. An interactive Multi Objective Optimization (iMOO) approach that captures the linkages between investment decisions and environmental, social, and economic outcomes is being developed and piloted. By including outcomes along environmental and social criteria, iMOO can promote more sustainable decision making, while allowing for the consideration of complex data and its linkages. However, information equality and the transparency of those results and access to these tools for companies, communities, and governments is critical. Computing the Pareto fronts that include social and environmental sustainability criteria helps communication among decision-makers and can help in addressing ethical concerns around AI use.

The adoption of artificial intelligence (AI) in financial services, particularly in syndicated lending, can offer benefits such as reducing costs, improving documentation production and completion, and improving risk management. However, lenders and other market participants adopting AI must consider the regulatory landscape across different jurisdictions, which can vary in their approaches to regulating AI. To operationalize the use of AI in a compliant manner, institutions must develop internal guidelines encompassing AI governance policy, AI model lifecycle management policy, policy on compliant and ethical use of AI and risk identification and management policy. While concerns exist about the impact of AI on jobs, its adoption in syndicated lending can ultimately help employees focus on higher-value tasks.

Artificial general intelligence (AGI) has the potential to revolutionize science and technology, but responsible management is crucial to ensure that it aligns with human values and does not harm human interests. AI alignment focuses on the internal workings of AI systems, while AI governance focuses on the broader regulatory and policy framework governing AI’s integration into society. Misalignment can bring various risks, including safety, ethical, economic, employment, and security risks. Several approaches to AI alignment address different aspects of the problem, with varying degrees of effectiveness and scalability. The major challenge of AI alignment is scalable alignment, which requires evolving methodologies to manage increasingly complex and capable AI systems while preserving human control and promoting their utilization for societal benefit.