Artificial Intelligence AI Ethics and Responsible AI

Question 1

What is 'algorithmic bias' in AI systems?

Accepted Answer

Systematic and unfair discrimination in AI outputs caused by biased training data or flawed model design

Answer

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?

Accepted Answer

The capacity to provide human-understandable reasons for an AI system's decisions

Answer

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?

Accepted Answer

Non-maleficence

Answer

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?

Accepted Answer

Adding mathematically calibrated noise to data or outputs to protect individual privacy while allowing aggregate analysis

Answer

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?

Accepted Answer

An input deliberately modified with small perturbations to cause an AI model to make wrong predictions

Answer

Adversarial examples are inputs crafted by adding carefully chosen, often imperceptible perturbations that reliably fool AI classifiers into producing incorrect outputs.

Artificial Intelligence Practice Test

Artificial Intelligence Practice Test

Artificial Intelligence AI Ethics and Responsible AI