Version 2.0.0
A unified AI risk taxonomy for use across Eticas audit methodologies, assessment frameworks, and reporting outputs.
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The risk that an AI system produces outcomes that systematically advantage or disadvantage individuals or groups based on protected or sensitive attributes, leading to unequal treatment, reduced accuracy, or unjust impacts.
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The risk that an AI system collects, processes, or infers personal information in ways that infringe on individuals’ rights to control their data (privacy), or that sensitive information is exposed, accessed, or shared without authorization (confidentiality).
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Risks arising from an AI systems failure to perform dependably — whether through degraded output integrity and robustness (e.g., hallucinations, model drift), or through inability to maintain function under adverse or changing conditions (e.g., infrastructure failure, connectivity loss).
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The risk that an AI system lacks adequate structures, policies, or accountability mechanisms to oversee its design, deployment, and use.
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The risk that an AI system is exposed to AI-specific vulnerabilities, attacks, or misuse that compromise its integrity, availability, or confidentiality.
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The risk that an AI system’s development, deployment, or use causes negative environmental effects, such as excessive energy or water consumption, carbon emissions, or unsustainable use of hardware and resources.
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The risk that stakeholders cannot understand how an AI system works, what it does, or why it produces specific outputs.
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Risks arising from AI systems that autonomously plan, reason, and act across multiple steps using tools, memory, and external services, beyond single-inference interactions.