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Modifications to training data composition, quality, and filtering that affect what the model learns.
Also in Model
Targeted Risk: Inappropriate content Applicable Life Cycle Phase: Data preparation
Reasoning
Reduces training data volume to limit information model learns, modifying training corpus composition.
Detection
1.2.3 Monitoring & DetectionIntervention
2.1.2 Roles & AccountabilityWatermarking
1.2.5 Provenance & WatermarkingControl-flow integrity
1.2.4 Security InfrastructureMonitoring of utilization
2.3.3 Monitoring & LoggingHardware error correction
1.2.4 Security InfrastructureA FRAMEWORK FOR ARTIFICIAL INTELLIGENCE RISK MANAGEMENT
Jin, David Lau Keat; Samy, Ganthan Narayana; Rahim, Fiza Abdul; Maarop, Nurazean; Selvananthan, Mahiswaran; Ali, Mazlan; Raman, Valliappan (2024)
Artificial Intelligence (AI) affords tremendous benefits to multiple sectors and businesses as its capabilities extend to different domain of activities. Notwithstanding the benefits that it brings, there are also potential risks which cause concerns by its users and those impacted by its use. Effective risk management is thus essential for organizations planning to deploy AI in high-risk applications. This study introduced a framework developed using a knowledge graph that stores and manages information on risk management, the AI life cycle, and stakeholder involvement, adhering to established standards. The framework facilitated the retrieval and generation of insights that support decision-making related to risk management, as it can represent interrelationships between entities more effectively than relational databases or typographies. The insights that can be generated include distribution of risks according to AI life cycle phases, the countermeasure that could treat the greatest number of risks and the countermeasure that produced the greatest change in terms of impact and probability to the identified risk. In this study, Cypher language was used to develop the framework, while Python language was used to generate the insights from the framework. Future studies may consider the integration of the framework in an enhanced Enterprise Risk Management framework to enable real-time update of related information and response by the organization. © Little Lion Scientific.
Collect and Process Data
Gathering, curating, labelling, and preprocessing training data
Infrastructure Provider
Entity providing compute, platforms, or tooling for AI systems
Manage
Prioritising, responding to, and mitigating AI risks
