A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
George Grispos, Sajda Qureshi
This study examines AI transparency in Nigerian mobile shopping apps, finding widespread AI use but limited disclosure, which challenges individual digital sovereignty.
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.
Unknown
This study evaluates small language models using standardized benchmarks and publicly available datasets, ensuring transparency and reproducibility.
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.
Unknown
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.