Contribution to the handbook
Supporting · Assessed 2026-10-28
The authors’ concerns about proprietary training data and possible manipulation, along with their proposed attention to open code and data, provide useful safeguards context. The packet does not specify workable rules or show that openness would prevent manipulation.
Does this challenge the goal?
In-scope gap
The question concerns transparency and manipulation risks in system development, which fit the existing safeguards goal; it does not challenge the goal or beneficiary boundary.
The supplied material reports concerns and proposed directions, not tested measures or their effectiveness.
Why this category?
- consequential risks/limits
- safeguards
The answer is an AI interpretation of the authors’ position, not independent evidence; the effectiveness and possible limits of the proposed measures remain open.
This is an AI assessment of contribution to the goal, separate from whether the question has an answer.
Authority, accountability and resilience →
Read the exploration goalNew evidence is being assessed. Any answer below is provisional; parts whose evidence changed or was withdrawn are withheld.
Identify the authors’ concerns about opaque or proprietary training data, possible manipulation, and transparency of system development, including the open-code or open-data measures they discuss.
What we can say so far
The authors warn that some models use proprietary training data that could be open to manipulation. They call for serious consideration of open-code and open-data rules to make system development more transparent.
What remains open
- The passage does not specify which rules would work or whether they would address all sources of manipulation.
Conditions and limits
- The passage presents open code and open data as a proposed direction, not as measures shown to prevent manipulation.
Evidence and related ideas
Supporting evidence, qualifications and context play different roles in this answer.
supports
Training data and transparency matter The authors warn that some models’ training data is proprietary and could be open to manipulation. They call for serious thought about open code and open data rules to make development more transparent.
Directly supports the attributed account of the authors’ transparency concerns and proposed measures.
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