6 papers
Socio-Conformal Calibration in Complex Survey Data: Marginal Validity Is Not Enough for Subgroup Reliability
Amir Rafe, Subasish Das
Machine-learning systems used in survey-based social measurement require uncertainty estimates that are reliable across population subgroups, not merely valid in aggregate. We stud…
Coupled-NeuralHP: Directional Temporal Coupling Between AI Innovation Exposure and Public Response
Amir Rafe, Subasish Das
Artificial intelligence innovation exposure and public response co-evolve, but innovation arrives as irregular event streams while response is observed monthly. We introduce Couple…
Heterogeneous Ordinal Structure Learning with Bayesian Nonparametric Complexity Discovery
Amir Rafe, Subasish Das
Public attitudes toward artificial intelligence are heterogeneous, ordinally measured, and poorly captured by any single dependency graph. Existing ordinal structure learners assum…
Advancing Intelligent Sequence Modeling: Evolution, Trade-offs, and Applications of State-Space Architectures from S4 to Mamba
Shriyank Somvanshi, Md Monzurul Islam, Mahmuda Sultana Mimi +5
Structured State Space Models (SSMs) have become a prominent class of sequence models, developed against two long-standing difficulties: the sequential computation and gradient pro…
Latent Profiles of AI Risk Perception and Their Differential Association with Community Driving Safety Concerns: A Person-Centered Analysis
Amir Rafe, Anika Baitullah, Subasish Das
Public attitudes toward artificial intelligence (AI) and driving safety are typically studied in isolation using variable-centered methods that assume population homogeneity, yet r…
Community Driving-Safety Deterioration as a Push Factor for Public Endorsement of AI Driving Capability
Amir Rafe, Subasish Das
Road traffic crashes claim approximately 1.19 million lives annually worldwide, and human error accounts for the vast majority, yet the autonomous vehicle acceptance literature mod…