Learning Fair Demand Models
2026-06-08T07:23:51Z•38bc27247683f7a1e4ddf24f299f23cb6ec564d61db6fb089515b40c5f4c8784
AI governanceAI sovereigntyHKJudge datasetLLM biasRLHFRawlsian fairnessSopriBenchalgorithmic fairnessdataset releasedemand estimationfair housinggenerative modelshuman temporal learninglegal NLPmultimodal privacypreference pluralitypricing modelsprivacy exposure scoreracial steeringstrategic competitionvalue collapse
What happened
This feed contains multiple 2026 arXiv submissions covering algorithmic fairness, AI governance, societal risks from generative models, legal-NLP resources, and privacy benchmarks. Key contributions include: a theoretical/empirical study of fairness interventions in data-driven pricing and demand modeling (with vaccine-pricing case study); an ontological analysis of learning analytics; a qualitative model of "AI sovereignty" highlighting micro/meso/macro levers and kinetic/cyber operational pathways; a two-phase simulated-annealing method for equitable team formation with strong empirical wins
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_cs_cy
- Record identifier
- 38bc27247683f7a1e4ddf24f299f23cb6ec564d61db6fb089515b40c5f4c8784
- Enrichment time
- 2026-06-08T07:23:51Z
- AI-assisted enrichment
- Yes
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