5 papers
A Neuro-Symbolic Framework for Accountability in Public-Sector AI
Allen Daniel Sunny, Ido Sivan-Sevilla
Automated eligibility systems increasingly determine access to essential public benefits, but the explanations they generate often fail to reflect the legal rules that authorize th…
Preliminary Quantitative Study on Explainability and Trust in AI Systems
Allen Daniel Sunny
Large-scale AI models such as GPT-4 have accelerated the deployment of artificial intelligence across critical domains including law, healthcare, and finance, raising urgent questi…
TangledFeatures: Robust Feature Selection in Highly Correlated Spaces
Allen Daniel Sunny
Feature selection is a fundamental step in model development, shaping both predictive performance and interpretability. Yet, most widely used methods focus on predictive accuracy,…
StructuralDecompose: A Modular Framework for Robust Time Series Decomposition in R
Allen Daniel Sunny
We present StructuralDecompose, an R package for modular and interpretable time series decomposition. Unlike existing approaches that treat decomposition as a monolithic process, S…
Trust in Transparency: How Explainable AI Shapes User Perceptions
Allen Daniel Sunny
This study explores the integration of contextual explanations into AI-powered loan decision systems to enhance trust and usability. While traditional AI systems rely heavily on al…