Artificial Intelligence AI Ethics and Responsible AI 1 — Questions and Answers
Question 1: What is 'algorithmic bias' in AI systems?
- A software bug that causes incorrect calculations
- Systematic and unfair discrimination in AI outputs caused by biased training data or flawed model design (Correct answer)
- A technique for improving model accuracy
- The tendency of AI to prefer certain programming languages
Correct 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.
Question 2: What does 'explainability' mean in the context of responsible AI?
- The ability to translate AI outputs into multiple languages
- The capacity to provide human-understandable reasons for an AI system's decisions (Correct answer)
- Documenting the training code for a model
- The speed at which a model produces predictions
Correct 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.
Question 3: Which principle of responsible AI states that AI systems should cause minimal harm and consider the well-being of all stakeholders?
- Transparency
- Non-maleficence (Correct answer)
- Accountability
- Fairness
Correct answer: Non-maleficence
Non-maleficence ('do no harm') requires AI systems to avoid causing physical, psychological, financial, or social harm to individuals or society.
Question 4: What is 'differential privacy' used for in AI and machine learning?
- Training models faster on distributed hardware
- Adding mathematically calibrated noise to data or outputs to protect individual privacy while allowing aggregate analysis (Correct answer)
- Detecting adversarial examples in model inputs
- Balancing class distributions in training data
Correct 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.
Question 5: What is an 'adversarial example' in the context of AI security?
- A training sample with an incorrect label
- An input deliberately modified with small perturbations to cause an AI model to make wrong predictions (Correct answer)
- A competitor's AI product
- An example used to evaluate model robustness
Correct 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.
Question 6: What does the EU AI Act classify as 'high-risk AI'?
- AI systems that consume large amounts of computing power
- AI deployed in critical domains like healthcare, hiring, credit scoring, and law enforcement where errors have serious consequences (Correct answer)
- Any AI model with more than 1 billion parameters
- AI systems used for entertainment and gaming
Correct 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.
Artificial Intelligence AI Ethics and Responsible AI 1
What is 'algorithmic bias' in AI systems?