Evaluating AI-Enabled deception vulnerability amongst Sub-Saharan-Africa migrants
2026-03-10T07:23:54Z•1b0169ad739fe7f9d0c16954131f7b7def1e4b44743ffbe5442ccf621e879a16
academic-integrityai-deceptionai-governancecausal-inferenceeducationethics-by-designeu-ai-actfairnessgenerative-aigithubhuman-ai-collaborationlearning-visibilitymental-health-chatbotsmigrant-vulnerabilitymlopsmulti-agent-systemsnist-rmfopen-source-securitypeer-review-biaspolitical-polarizationscamssocial-proofsustainability
What happened
Collection of arXiv CS/ CY papers (10 Mar 2026) covering AI risks, governance, and socio-technical impacts. Key contributions include: empirical study of Sub-Saharan African migrants' vulnerability to AI-enabled deception and protective factors (verification effort, prior exposure); an ethics-by-design, gate-based control architecture mapping to EU AI Act and NIST for operational AI governance in MLOps; causal evidence of demographic biases in academic peer review and calls for fairness interventions; frameworks for using generative AI to support science literacy and design education (human-AI
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_cs_cy
- Record identifier
- 1b0169ad739fe7f9d0c16954131f7b7def1e4b44743ffbe5442ccf621e879a16
- Enrichment time
- 2026-03-10T07:23:54Z
- AI-assisted enrichment
- Yes
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