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 'model fairness' in AI, and why does it matter?
Answer: Ensuring the model's predictions are equitable across demographic groups and do not perpetuate systemic discrimination
Model fairness ensures that AI decisions do not systematically disadvantage protected groups (e.g., by race, gender, age), promoting equitable outcomes in consequential applications.
What is the 'right to explanation' under GDPR in the context of automated AI decisions?
Answer: The right of individuals to receive meaningful information about the logic behind automated decisions that significantly affect them
GDPR Article 22 grants individuals the right not to be subject to solely automated decisions with significant effects, and to obtain meaningful explanations of the decision logic.
What is 'model auditing' in responsible AI practices?
Answer: Systematically evaluating an AI model for bias, accuracy, safety, and compliance with ethical standards
Model auditing involves third-party or internal evaluation of AI systems to identify unfair outcomes, errors, compliance gaps, or potential harms before and after deployment.
What does 'transparency' require of AI developers and deployers?
Answer: Being open about how AI systems work, what data they use, their limitations, and potential risks
Transparency requires that AI developers disclose how systems are built, what they can and cannot do, and where they may fail, enabling informed use and external scrutiny.
What is 'data minimization' as it relates to responsible AI?
Answer: Collecting and retaining only the personal data strictly necessary for the intended AI application
Data minimization reduces privacy risk by limiting data collection and retention to what is genuinely needed, aligning with GDPR and privacy-by-design principles.
What is 'human-in-the-loop' (HITL) in AI systems?
Answer: A design approach where human judgment is integrated into AI decision-making for oversight and correction
HITL systems incorporate human review and approval at critical decision points, ensuring that humans can catch errors, override AI judgments, and maintain accountability.