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Kuis AI & Masyarakat
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Why consider people who do not directly use an AI system?
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- Why consider people who do not directly use an AI system?
- Only direct users can experience impact
- Institutions can change their access, information, or work even for non-users
- Non-users have no rights or interests
- System effects stop at the login screen
- A public service is faster on average but inaccessible to a language group. What does this suggest?
- Average speed proves fair access
- Language coverage is separate from system impact
- The service has no social consequence
- Efficiency does not capture who can access or contest the service
- When should affected communities be consulted?
- Only after deployment ends
- Only if they operate the system
- When defining the problem and evaluating the proposed solution
- Only when the model is open source
- What can generated content do in an information environment?
- Increase access to explanations while also amplifying misleading material
- Guarantee accurate public information
- Remove the need for provenance
- Ensure all readers see corrections
- What helps readers evaluate a generated claim without blind trust?
- A model’s confidence phrase
- The number of generated pages
- A polished voice
- Provenance, corrections, and transparent evidence
- Why should governance continue after deployment?
- All social impacts are known before launch
- Actual outcomes, complaints, and changed uses can reveal new impacts
- Model accuracy prevents new effects
- Governance is only procurement paperwork
- Does technical accuracy on a test set establish that a system is socially suitable?
- No; access, contestability, and institutional fit also matter
- Yes, if accuracy exceeds a benchmark
- Only if users agree with the answer
- No, because accuracy cannot be measured
- Who shapes the distribution of AI benefits and harms?
- Model weights alone
- Only individual users
- Institutions and policy choices as well as technical capabilities
- The benchmark designer alone