Cross-Subject Predictive Validity for Learning Outcomes of Delayed Start Behavior
2026-06-25T07:23:50Z•d37e6cbb6d61cb5eec3260113469d281f9abc0bab7d7d9fd26cbc494394935e6
AI-safetyLLM-evaluationadversarial-manipulationagentic-aiautomated-decision-systemsdata-poisoningdata-provenanceeducation-technologygovernanceinfrastructure-stabilitymodel-attributionmodel-influenceopen-source-licensessupply-chain-risk
What happened
This aggregated set of arXiv papers highlights multiple operational and governance risks relevant to AI and data infrastructure. Key findings: small, targeted edits to widely used corpora (Wikipedia) can measurably shift LLM behavior (data-poisoning/model-influence); open-source memory and data-infrastructure projects face non-trivial license and sustainability events that create supply-chain risk; AI governance artifacts often lack aviation-style structural requirements (traceability, epoch limits, objective evidence), increasing deployment and accountability gaps; automated decision systems'
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_cs_cy
- Record identifier
- d37e6cbb6d61cb5eec3260113469d281f9abc0bab7d7d9fd26cbc494394935e6
- Enrichment time
- 2026-06-25T07:23:50Z
- AI-assisted enrichment
- Yes
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