The paper introduces the Agentic Risk Standard (ARS) as a payment settlement framework that delivers predefined compensation for AI agent execution failures, misalignment, or unintended outcomes.
Explainable Artificial Intelli- gence (xai): From Inherent Explainability To Large Language Models
4 Pith papers cite this work. Polarity classification is still indexing.
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GRAPHIC interprets confusion matrices from linear classifiers on intermediate layers as graphs to visualize and quantify class confusion dynamics in deep learning.
ERPPO adds a DSA-based ambiguity estimator to MAPPO and switches between L1 and L2 entropy regularization to improve exploration and stability in non-stationary multi-dimensional observations.
Omics datasets show low ancestry reporting and strong European bias, which biomedical foundation models risk perpetuating into downstream healthcare disparities unless addressed through provenance, openness, and evaluation transparency.
citing papers explorer
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Quantifying Trust: Financial Risk Management for Trustworthy AI Agents
The paper introduces the Agentic Risk Standard (ARS) as a payment settlement framework that delivers predefined compensation for AI agent execution failures, misalignment, or unintended outcomes.
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The Confusion is Real: GRAPHIC -- A Network Science Approach to Confusion Matrices in Deep Learning
GRAPHIC interprets confusion matrices from linear classifiers on intermediate layers as graphs to visualize and quantify class confusion dynamics in deep learning.
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ERPPO: Entropy Regularization-based Proximal Policy Optimization
ERPPO adds a DSA-based ambiguity estimator to MAPPO and switches between L1 and L2 entropy regularization to improve exploration and stability in non-stationary multi-dimensional observations.
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Perspective on Bias in Biomedical AI: Preventing Downstream Healthcare Disparities
Omics datasets show low ancestry reporting and strong European bias, which biomedical foundation models risk perpetuating into downstream healthcare disparities unless addressed through provenance, openness, and evaluation transparency.