Algorithmic Accountability
The principle that organizations deploying AI systems should be responsible for their outcomes and able to explain how algorithmic decisions are made, especially in high-stakes contexts.
Algorithmic accountability legislation is advancing globally: the EU AI Act requires impact assessments for high-risk AI, New York City's Local Law 144 mandates bias audits for AI hiring tools, and the Colorado AI Act requires impact assessments for high-risk AI systems. Accountability frameworks typically require documentation of training data, model performance metrics, bias testing results, and human oversight mechanisms. The challenge is that modern AI models — particularly large neural networks — operate as black boxes, making full accountability technically difficult.
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Related Terms
Artificial General Intelligence (AGI)
A hypothetical form of AI that can understand, learn, and apply knowledge across any intellectual task at or above human level, rather than being specialized for specific tasks.
AI Alignment
The research field focused on ensuring AI systems behave in accordance with human values and intentions, particularly as systems become more capable.
AI Safety
The interdisciplinary field focused on preventing AI systems from causing harm, encompassing alignment, robustness, interpretability, and governance of AI technologies.
Deepfake
AI-generated synthetic media — images, video, or audio — that realistically depict events or statements that never occurred, created using deep learning techniques.
EU AI Act
The European Union's comprehensive AI regulation, which entered into force on August 1, 2024, classifying AI systems by risk level and imposing requirements from transparency disclosures to outright bans.
Hallucination
When an AI model generates plausible-sounding but factually incorrect or fabricated information, presenting it with the same confidence as accurate responses.
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