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Appendices
Appendix A: Glossary
AI Risk The potential for adverse outcomes arising from the design, deployment,
and operation of artificial intelligence within trading and risk management
environments. AI Risk includes ethical, operational, regulatory, and
systemic risks arising from the design, deployment, and use of AI Systems
and Tools.
AI Tools Computational models, algorithms, and vendor-provided applications that
process data to generate predictions, classifications, decisions, language,
intelligent automation, or work-reducing outcomes.
AI Systems Integrated architectures that combine artificial intelligence models, data
pipelines, computational infrastructure, and operational workflows to
perform complex tasks traditionally requiring human judgment. Unlike
standalone AI Tools, which typically address discrete functions, AI Systems
operate as end-to-end solutions by ingesting data, generating insights, and
executing actions within trading and risk management environments.
Algorithm An Algorithm is a structured, step-by-step set of instructions designed to
perform a specific task or solve a defined problem. In the context of trading
and risk management, algorithms are implemented as executable logic
within software systems to process data, make calculations, and generate
outputs based on predefined rules or adaptive learning techniques.
Artificial
Intelligence
(“AI”)
The field of computer science focused on creating systems capable of
performing tasks that traditionally require human intelligence. These tasks
include learning from data, recognizing patterns, making decisions, and
adapting to changing conditions without explicit programming for every
scenario.
Black Box
Risk
Uncertainty and potential hazards associated with using complex, opaque
models whose internal logic and decision-making processes are not easily
interpretable by humans. These systems often operate as “black boxes,”
producing outputs without transparent reasoning, which creates significant
challenges for validation, governance, and regulatory compliance.
Edge Case A scenario that occurs at the extreme boundaries of a system’s expected
operating conditions. In the context of AI-driven trading and risk
management, Edge Cases typically involve market conditions, data
anomalies, or operational events that fall outside the range of historical
patterns on which algorithms and models were trained.
Appendices
Appendix A: Glossary
AI Risk The potential for adverse outcomes arising from the design, deployment,
and operation of artificial intelligence within trading and risk management
environments. AI Risk includes ethical, operational, regulatory, and
systemic risks arising from the design, deployment, and use of AI Systems
and Tools.
AI Tools Computational models, algorithms, and vendor-provided applications that
process data to generate predictions, classifications, decisions, language,
intelligent automation, or work-reducing outcomes.
AI Systems Integrated architectures that combine artificial intelligence models, data
pipelines, computational infrastructure, and operational workflows to
perform complex tasks traditionally requiring human judgment. Unlike
standalone AI Tools, which typically address discrete functions, AI Systems
operate as end-to-end solutions by ingesting data, generating insights, and
executing actions within trading and risk management environments.
Algorithm An Algorithm is a structured, step-by-step set of instructions designed to
perform a specific task or solve a defined problem. In the context of trading
and risk management, algorithms are implemented as executable logic
within software systems to process data, make calculations, and generate
outputs based on predefined rules or adaptive learning techniques.
Artificial
Intelligence
(“AI”)
The field of computer science focused on creating systems capable of
performing tasks that traditionally require human intelligence. These tasks
include learning from data, recognizing patterns, making decisions, and
adapting to changing conditions without explicit programming for every
scenario.
Black Box
Risk
Uncertainty and potential hazards associated with using complex, opaque
models whose internal logic and decision-making processes are not easily
interpretable by humans. These systems often operate as “black boxes,”
producing outputs without transparent reasoning, which creates significant
challenges for validation, governance, and regulatory compliance.
Edge Case A scenario that occurs at the extreme boundaries of a system’s expected
operating conditions. In the context of AI-driven trading and risk
management, Edge Cases typically involve market conditions, data
anomalies, or operational events that fall outside the range of historical
patterns on which algorithms and models were trained.
