Biography
Ahmed Izzidien is a postdoctoral Research Associate at the University of Cambridge.
His interests natural language processing and artificial intelligence machine learning for: legal analysis, Hohfeldian mapping of legal documents, and predicting the outcome of courts.
Reviewer: Humanities and Social Sciences Communications, Springer Nature: nature.com/palcomms/
Publications
Izzidien, A. (In Press). Using the Interest Theory of Rights and Hohfeldian Taxonomy to Address a Gap in Machine Learning Methodologies. Humanities and Social Sciences Communications.
Izzidien, A., Sargeant, H., & Steffek, F. (2022). What goes on in court? Identifying contract-related topics decided by United Kingdom courts from 1709 to 2021 using machine learning. Cambridge Open Engage. https://doi:10.33774/coe-2022-p7rjg-v2
Izzidien, A., Fitz, S., Romero, P. et al. (2022) Developing a sentence level fairness metric using word embeddings. Int J Digit Humanities. https://doi.org/10.1007/s42803-022-00049-4
Izzidien, A. (2022). Word vector embeddings hold social ontological relations capable of reflecting meaningful fairness assessments. AI & SOCIETY, 37(1), 299–318. https://doi.org/10.1007/s00146-021-01167-3
Izzidien, A. (2021, November 15). The Limits of Annotation in Machine Learning a Documents Hohfeldian Legal Entities [Poster session]. Cambridge Language Sciences Symposium, Cambridge, UK. https://doi.org/10.33774/coe-2021-dqwvg
Izzidien, A., & Stillwell, D. (2021). The Golden Rule as a Heuristic to Measure the Fairness of Texts Using Machine Learning. ArXiv:2111.00107 [Cs]. http://arxiv.org/abs/2111.00107
Izzidien, A., & Chennu, S. (2018). A Neuroscience Study on the Implicit Subconscious Perceptions of Fairness and Islamic Law in Muslims Using the EEG N400 Event Related Potential. Journal of Cognition and Neuroethics, 2(5)
