A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
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.
Kefallinos, Dionysios, Alexandris, Georgios, Maras, Alexis, et al.
Proposes a framework for answering qualitative spatial questions using a geoparser, crisp reasoner, and answer extraction, with promising synthetic evaluation.
J. Kirchner, Yining Chen, Harri Edwards, et al.
Proposes legibility training via a Prover-Verifier Game to make LLM chain-of-thought reasoning easier for humans to verify, improving trust in model outputs.
Unknown
LongCite enables LLMs to generate fine-grained sentence-level citations in long-context QA, improving trustworthiness via a new benchmark, pipeline, dataset, and trained models.
Saadeldine Eletter, Owais Aijaz, Preslav Nakov
This paper proposes QIMG-7 and source-aware resolution to improve multimodal RAG reliability by resolving conflicts before fusion.
Unknown
This survey systematically reviews threats and countermeasures for trustworthy LLM agents, organizing defense approaches into three paradigms: alignment, monitoring, and control.
David Gunning, David W. Aha
DARPA's XAI program aims to create AI systems whose learned models and decisions can be understood and appropriately trusted by end users.
Andy Nguyen, Ha Ngan Ngo, Yvonne Hong, et al.
This paper maps and analyzes international policies to establish a set of ethical principles for trustworthy AI in education, serving as a framework for stakeholders.
Erico Tjoa, Cuntai Guan
A survey categorizing interpretability methods in explainable AI, with a focus on medical applications to improve transparency and trust.
Sajid Ali, Tamer Abuhmed, Shaker El–Sappagh, et al.
A comprehensive survey of XAI techniques, evaluation methods, tools, datasets, and legal/ethical concerns for trustworthy AI.