A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
J. Jakeman, Lorena A. Barba, Joaquim R. R. A. Martins, et al.
This paper proposes a framework for verification and validation (V&V) of scientific machine learning models to enhance trustworthiness and reliability in scientific applications.
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This paper proposes a framework to harden LLM fine-tuning by combining differentially private data selection with trustworthiness-aware model quantization to enhance privacy and robustness.
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This paper introduces Multitrust, a comprehensive benchmark for evaluating the trustworthiness of multimodal large language models across multiple dimensions.
Xian Sun, Wei Chow, Yingshuo Wang, et al.
Introduces MIST benchmark and SC2W metric for selective trust in LLMs, plus SCOPE method using balanced DPO to reduce context-induced errors without sacrificing accuracy.
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This paper investigates how trust in AI creators influences trust in AI systems, emphasizing the critical roles of AI alignment and steerability within the agentic IS framework.
Aniri, Jinhe Bi, Peng Liao, et al.
OPD-V introduces a visual on-policy self-distillation paradigm that uses modality-balance trust regions to improve MLLM reasoning while reducing training cost.
Zohaib Salahuddin, Henry C. Woodruff, Avishek Chatterjee, et al.
This review categorizes nine interpretability methods for deep neural networks in medical image analysis, evaluates progress in explanation assessment, and provides guidelines and future directions for trustworthy AI.
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A survey of small language models covering architecture, capabilities, and trustworthiness, with emphasis on robustness evaluation.
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This paper systematically reviews evaluation methodologies for agentic AI systems, arguing that progress toward trustworthy agentic AI depends on evolving evaluation approaches.
Yujia Zhou, Yan Liu, Xiaoxi Li, et al.
This survey systematically reviews trustworthiness in Retrieval-Augmented Generation (RAG) systems, proposing a taxonomy of six key dimensions and analyzing current challenges and future directions.
Scott Thiebes, Sebastian Lins, Ali Sunyaev
Introduces Trustworthy AI (TAI) with five foundational principles and a data-driven research framework for future AI governance.
Michael Rogerson, Glenn Parry
This paper uses qualitative case studies to show blockchain enhances supply chain visibility and trust, but faces challenges in technology trust, human error, governance, and consumer willingness to pay.