AI Ethics and Responsible AI Flashcards
6 cards from real Artificial Intelligence practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 6 AI Ethics and Responsible AI flashcards as text
What is 'consent' in the context of AI and data privacy?
Answer: Freely given, specific, informed, and unambiguous permission from individuals for their data to be collected and used
Meaningful consent ensures individuals know what data is collected, how it is used, and can withdraw permission, forming the ethical and legal foundation of data-driven AI.
What is 'AI hallucination' and why is it an ethical concern?
Answer: When AI language models generate plausible-sounding but factually incorrect or fabricated information
AI hallucinations occur when language models produce confident, coherent but false statements, posing risks in medical, legal, or journalistic contexts where accuracy is critical.
What is 'accountability' in responsible AI governance?
Answer: Ensuring that developers, deployers, and organizations can be held responsible for the outcomes and harms caused by AI systems
Accountability means that identifiable parties are responsible for AI system outcomes, can be questioned about their decisions, and bear consequences when systems cause harm.
What is 'federated learning' and how does it address privacy concerns?
Answer: Training models locally on distributed devices and sharing only model updates (not raw data) with a central server
Federated learning keeps personal data on local devices and aggregates only gradient updates, reducing privacy risks compared to centralized training on raw data.
What is 'value alignment' in AI safety research?
Answer: The challenge of designing AI systems whose goals and behaviors align with human values and intentions
Value alignment addresses the problem of ensuring that as AI systems become more capable, their objectives remain consistent with human values rather than pursuing misaligned proxy goals.
What is a 'fairness-accuracy tradeoff' in machine learning?
Answer: The observation that imposing fairness constraints may sometimes reduce a model's overall predictive accuracy
Enforcing fairness constraints (e.g., equal error rates across groups) can require sacrificing some predictive performance, creating a tension between statistical fairness and accuracy metrics.