A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Daniel J. Mathew, Deborah Ebem, Anayo Chukwu Ikegwu, et al.
This paper reviews recent XAI techniques to enhance interpretability and human understanding of complex AI models, addressing transparency challenges.
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Dolma is an open corpus of three trillion tokens for language model pretraining, designed to support transparency and reproducibility in AI research.
Ribana Roscher, Bastian Bohn, Marco F. Duarte, et al.
Reviews explainable machine learning for scientific discovery, emphasizing transparency, interpretability, and explainability with domain knowledge.
W. Holmes, Kaśka Porayska‐Pomsta, Ken Holstein, et al.
This paper surveys 60 AIED researchers to identify ethical gaps and proposes a multidisciplinary framework for fairness, accountability, transparency, bias, autonomy, agency, and inclusion in AI education.
Erico Tjoa, Cuntai Guan
A survey categorizing interpretability methods in explainable AI, with a focus on medical applications to improve transparency and trust.