11 citations · 24 across the 12 of their papers we have counts for
11 papers · 1 filter
Estimating Uncoded Crash Factors with Tabular Foundation and System One Models: Kumo Tabular and Jev
Amir Rafe, Subasish Das
Road safety programs count the coded fields of police crash records, while the officer's narrative, which often records factors the fields omit, is rarely read. A safety office thu…
Benchmarking System One decision models against trained classifiers and language models for automated decision gates
Amir Rafe, Subasish Das
Software that hands branching decisions to a model needs a declared option and a probability it can threshold. Typed decision models, also called System One models, return such pro…
Counting the Uncounted: Population-Level Surveillance of Documented Pregnancy and Fetal Harm in Police Crash Narratives with a System One Model (Jev)
Amir Rafe, Subasish Das
Motor-vehicle crashes are a leading cause of traumatic fetal death in the United States, yet no crash database records pregnancy. Existing estimates come from national investigatio…
Calibrated Decisions at Scale: Converting Police Crash Narratives into Probabilistic Crash Variables with a System One Model (Jev)
Amir Rafe, Subasish Das
Crash datasets that carry an investigator narrative hold information the coded fields omit. Coding those narratives at scale has been blocked by three obstacles. Frontier large lan…
Who Bears the Risk When Generative AI Enters Transport? A Distributional Sociotechnical Audit of Algorithmic Equity, Synthetic-Data Validity, and Public Trust
Amir Rafe, Subasish Das
Generative artificial intelligence is entering transportation through traveler-facing advisories, synthetic crash-record generation, and policy decision support. Existing governanc…
CHOIR: heterogeneity-aware conformal prediction for crash injury severity across driver safety strata
Amir Rafe, Subasish Das
Transportation agencies increasingly predict crash-injury severity with statistical and machine-learning models, but these models do not state how often their output contains the r…
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…
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…