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Quizzes > Computers & IT

AI Ethics Quiz: Test Your Knowledge

Quick, free AI ethics test. Instant results.

Editorial: Review CompletedCreated By: Chamnab PenUpdated Aug 23, 2025
Difficulty: Moderate
Questions: 15
Study OutcomesAdditional Reading
3D voxel art depicting Ethics of Artificial Intelligence course content

This AI ethics quiz helps you check your grasp of fair, safe, and responsible AI. Answer 15 short questions on bias, accountability, privacy, and impacts on work and society, then get instant feedback. Explore basics with an ai knowledge test, reflect on values with an ethics test, or examine blind spots with an implicit bias quiz.

Which ethical principle is most closely associated with the obligation to avoid causing harm when deploying AI systems?
Procedural neutrality
Non-maleficence (avoiding harm) - Explanation: Core bioethical principle applied to AI ethics focusing on preventing harm
Intellectual property protection
Utility maximization
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Statement: Transparency in AI requires that the system's decision-making process be understandable to relevant stakeholders.
False
True
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Which approach aims to mitigate privacy risks by adding carefully calibrated noise to data or outputs?
Differential privacy - Explanation: Provides quantifiable privacy guarantees via noise
Early stopping
Batch normalization
Dropout regularization
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Statement: Automation bias can lead humans to over-trust AI recommendations, reducing critical oversight.
False
True
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Which document type communicates a model's intended use, performance, limitations, and ethical considerations to stakeholders?
Changelog
Service Level Agreement
White paper
Model Card - Explanation: Standardized report detailing model behavior and constraints
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What is the primary ethical concern with facial recognition used in public spaces?
Battery consumption
GPU overheating
Mass surveillance and privacy invasion - Explanation: Enables pervasive tracking and chilling effects
Screen resolution limits
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What is a key ethical concern of deploying AI in hiring?
Lower server latency
Higher employee retention by default
Longer onboarding time
Reinforcement of historical bias via training data - Explanation: Biased past decisions propagate to future
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Which practice documents dataset motivation, composition, collection process, and recommended uses to support ethical review?
Data lakes
Model pruning
Datasheets for Datasets - Explanation: A standardized documentation practice proposed by Gebru et al.
Synthetic augmentation
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Statement: Federated learning can reduce data centralization risks but does not, by itself, guarantee privacy.
True
False
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Which ethical risk arises when AI systems learn proxies for protected attributes even when those attributes are removed?
Catastrophic forgetting
Model underfitting
Proxy discrimination - Explanation: Correlated features allow indirect discrimination
Data deduplication
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Which governance practice systematically evaluates potential societal impacts and risks prior to AI deployment?
A/B testing
Memory profiling
Algorithmic Impact Assessment (AIA) - Explanation: A structured, pre-deployment risk and impact review
Code linting
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Statement: Black-box models are always unethical because they are complex.
True
False
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Statement: Value alignment in AI refers to aligning system objectives with human values and norms.
True
False
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Which concept warns that optimizing a proxy metric can distort the underlying goal and produce harmful side effects?
Occam's Razor
Pareto principle
Goodhart's Law - Explanation: When a measure becomes a target, it ceases to be a good measure
Simpson's paradox
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In AI fairness, which metric requires that the positive prediction rate be equal across protected groups regardless of outcome rates?
Predictive parity
Calibration within groups
Demographic parity (statistical parity) - Explanation: It equalizes selection rates across groups
Equalized odds
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Statement: The EU General Data Protection Regulation (GDPR) explicitly grants a universal, legally binding right to explanation of every algorithmic decision.
False
True
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Which conflict in fairness metrics shows that not all fairness criteria can be satisfied simultaneously when base rates differ across groups?
Bell's theorem
Fairness impossibility results - Explanation: Demonstrate incompatibility among metrics like calibration and equalized odds
Central limit theorem
No free lunch theorem
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Statement: Equalized odds requires equal false positive and false negative rates across protected groups.
False
True
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Statement: Article 22 of the GDPR concerns rights related to automated individual decision-making, including profiling.
True
False
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Statement: Differential privacy eliminates all risks of re-identification in any context.
False
True
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Study Outcomes

  1. Analyze the ethical challenges that arise with the deployment and use of AI technologies.
  2. Evaluate the societal and political implications of AI, particularly in relation to automation and the future of work.
  3. Apply ethical frameworks to assess the moral considerations of granting rights or duties to AI systems.
  4. Examine the impact of AI on privacy, security, and democratic processes.

Ethics Of Artificial Intelligence Additional Reading

Embarking on a journey through the ethical landscapes of Artificial Intelligence? Here are some insightful academic resources to guide your exploration:

  1. This article delves into the ethical considerations of AI, focusing on moral responsibility and autonomy, and discusses the importance of transparency and accountability in AI systems.
  2. This study explores key ethical concerns in AI development, including bias, privacy, transparency, and accountability, providing a comprehensive overview of the challenges and considerations in the field.
  3. This research examines university educators' perspectives on AI ethics, highlighting the need for targeted professional development and collaborative policy-making to promote ethical AI use in higher education.
  4. This systematic review identifies 22 ethical principles and 15 challenges in AI ethics, emphasizing the importance of transparency, privacy, accountability, and fairness in AI systems.
  5. This article provides a typology for the AI life-cycle, ethical principles, and tools to foster compliance, offering insights into the ethical development and governance of AI systems.
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