Ethics Advanced Quiz 4
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Advanced Quiz 4
1. What is existential risk from AI?
Any minor bug in a software project
A risk that AI could cause human extinction or permanent, drastic curtailment of humanity’s potential
A risk that a laptop battery may fail
2. What argument supports taking extreme AI risks seriously?
Advanced AI systems will never surpass human abilities
Software cannot influence the real world
Highly capable, misaligned systems could obtain power and pursue goals harmful to humans
3. What is one critique of focusing too much on long-term AI extinction risk?
It might distract from present-day harms and inequalities caused by current systems
It guarantees that no regulation will be passed
It makes models physically smaller
4. What is differential technological progress in the context of AI safety?
Speeding up all AI capabilities equally
Accelerating safety and governance work relative to raw capability improvements
Focusing only on hardware development
5. What is an example of safety-enhancing research that should be prioritized?
Techniques for making ads more eye-catching
Methods for generating more addictive games
Robustness, interpretability, scalable oversight, and alignment methods for powerful models
6. How can governance and technical safety research reinforce each other?
Governance can require safety evaluations; safety research provides tools policymakers can rely on
Governance avoids using any technical metrics
Safety research ignores legal and social constraints entirely
7. What does “safety-by-design” mean for foundation models?
Adding safety checks only after deployment
Building safety considerations into data curation, training, evaluation, and deployment from the start
Relying only on user complaints to adjust behavior
8. How can open science be balanced with security for dangerous AI capabilities?
By publishing all details regardless of risk
By never sharing any scientific results
By selectively sharing methods and results while restricting highly actionable misuse information
9. What is compute governance in AI safety discussions?
Policies and systems for monitoring and controlling access to large amounts of computational power
Only choosing efficient programming languages
Replacing all data centers with personal laptops
10. How might monitoring large training runs help reduce risk?
By slowing down all research forever
By allowing oversight of who is training very capable systems and under what safeguards
By automatically making models harmless without testing
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