5
industry practitioners to establish reasonable and scalable AI Risk management programs based
on the recommendations and examples noted in this document.
2. AI Risk Categories
AI usage can introduce a diverse set of risks that extend beyond traditional model risk. In the
context of commodity trading, these risks can impact decision-making, market stability,
regulatory compliance, and operational integrity. The CCRO recommends companies define AI
Risk categories in keeping with their functional needs.
• Ethical Risks
AI Systems and Tools often lack embedded ethical reasoning, which can lead to
unintended consequences. Ethical risks include bias in decision-making, privacy
violations, and societal impacts stemming from opaque or unexplainable outputs. These
risks are particularly concerning when AI is used in high-stakes environments without
adequate human oversight.
• Input Risks
Poor data quality, unstructured sources, and poor data lineage can compromise the
integrity of AI Systems and Tools. Input risks arise when models are trained on
incomplete, outdated, or biased datasets, leading to unreliable or misleading outputs.
• Output Risks
Output risks include Hallucinations, lack of explainability, and over-reliance on AI-
generated decisions. These risks are amplified when users treat AI outputs as definitive
without understanding their underlying assumptions, limitations, or ramifications.
• Regulatory Risks
The evolving regulatory landscape introduces uncertainty for firms deploying AI
Systems and Tools. Regulatory frameworks impose requirements around transparency,
accountability, and data protection, and may change from region-to-region. Changes in
the regulatory landscape and non-compliance can result in penalties, reputational
damage, or disallowed cost recovery.
• Systemic Risks
Systemic risks include AI herding behavior, monopolistic access to data, and capital cost
implications. These risks can destabilize markets if multiple firms rely on similar AI
models or data sources, leading to correlated actions and reduced market diversity.
• Personnel Risks
Skill gaps, unclear accountability, and insufficient oversight contribute to AI Risk in the
personnel space. These arise when AI Systems and Tools are used without proper
training, governance, or understanding. Misclassification of AI Tools can also lead to
risk underestimation and inadequate controls. Rogue development of AI Tools without
centralized oversight can also create risks.
• Model Performance &Validation Risk
industry practitioners to establish reasonable and scalable AI Risk management programs based
on the recommendations and examples noted in this document.
2. AI Risk Categories
AI usage can introduce a diverse set of risks that extend beyond traditional model risk. In the
context of commodity trading, these risks can impact decision-making, market stability,
regulatory compliance, and operational integrity. The CCRO recommends companies define AI
Risk categories in keeping with their functional needs.
• Ethical Risks
AI Systems and Tools often lack embedded ethical reasoning, which can lead to
unintended consequences. Ethical risks include bias in decision-making, privacy
violations, and societal impacts stemming from opaque or unexplainable outputs. These
risks are particularly concerning when AI is used in high-stakes environments without
adequate human oversight.
• Input Risks
Poor data quality, unstructured sources, and poor data lineage can compromise the
integrity of AI Systems and Tools. Input risks arise when models are trained on
incomplete, outdated, or biased datasets, leading to unreliable or misleading outputs.
• Output Risks
Output risks include Hallucinations, lack of explainability, and over-reliance on AI-
generated decisions. These risks are amplified when users treat AI outputs as definitive
without understanding their underlying assumptions, limitations, or ramifications.
• Regulatory Risks
The evolving regulatory landscape introduces uncertainty for firms deploying AI
Systems and Tools. Regulatory frameworks impose requirements around transparency,
accountability, and data protection, and may change from region-to-region. Changes in
the regulatory landscape and non-compliance can result in penalties, reputational
damage, or disallowed cost recovery.
• Systemic Risks
Systemic risks include AI herding behavior, monopolistic access to data, and capital cost
implications. These risks can destabilize markets if multiple firms rely on similar AI
models or data sources, leading to correlated actions and reduced market diversity.
• Personnel Risks
Skill gaps, unclear accountability, and insufficient oversight contribute to AI Risk in the
personnel space. These arise when AI Systems and Tools are used without proper
training, governance, or understanding. Misclassification of AI Tools can also lead to
risk underestimation and inadequate controls. Rogue development of AI Tools without
centralized oversight can also create risks.
• Model Performance &Validation Risk
