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 'algorithmic bias' in AI systems?
Answer: Systematic and unfair discrimination in AI outputs caused by biased training data or flawed model design
Algorithmic bias occurs when AI systems produce outputs that unfairly advantage or disadvantage certain groups, often reflecting historical biases present in training data.
What does 'explainability' mean in the context of responsible AI?
Answer: The capacity to provide human-understandable reasons for an AI system's decisions
Explainability (or interpretability) refers to the degree to which AI decisions can be understood by humans, enabling trust, accountability, and error diagnosis.
Which principle of responsible AI states that AI systems should cause minimal harm and consider the well-being of all stakeholders?
Answer: Non-maleficence
Non-maleficence ('do no harm') requires AI systems to avoid causing physical, psychological, financial, or social harm to individuals or society.
What is 'differential privacy' used for in AI and machine learning?
Answer: Adding mathematically calibrated noise to data or outputs to protect individual privacy while allowing aggregate analysis
Differential privacy provides a formal guarantee that the inclusion of any individual's data in training has a bounded, negligible effect on model outputs, protecting personal information.
What is an 'adversarial example' in the context of AI security?
Answer: An input deliberately modified with small perturbations to cause an AI model to make wrong predictions
Adversarial examples are inputs crafted by adding carefully chosen, often imperceptible perturbations that reliably fool AI classifiers into producing incorrect outputs.
What does the EU AI Act classify as 'high-risk AI'?
Answer: AI deployed in critical domains like healthcare, hiring, credit scoring, and law enforcement where errors have serious consequences
The EU AI Act designates AI used in safety-critical or rights-affecting contexts as high-risk, requiring strict conformity assessments, transparency, and human oversight.