Σύγκριση μεθόδων

	Ανίχνευση Φυλετικής Μεροληψίας στην Επεξεργασία Φυσικής Γλώσσας	Ταξινόμηση Κειμένου
Πεδίο	Εξόρυξη Κειμένου	Εξόρυξη Κειμένου
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	2017–2018 (seminal benchmarks)	—
Δημιουργός	Caliskan et al. (2017); Zhao et al. (2018)	—
Τύπος	NLP bias auditing pipeline	Supervised NLP classification task
Θεμελιώδης πηγή	Caliskan, A., Bryson, J. J., & Narayanan, A. (2017). Semantics derived automatically from language corpora contain human-like biases. Science, 356(6334), 183–186.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.
Εναλλακτικές ονομασίες	Toplumsal Cinsiyet Yanlılığı Tespiti — NLP, bias auditing NLP, WEAT, WinoBias	text categorization, document classification, topic classification, metin sınıflandırma
Συναφείς	5	4
Σύνοψη	Gender bias detection in NLP is a family of statistical and embedding-based methods used to measure stereotyping, representational imbalance, and occupational bias in text corpora and language models. Grounded in benchmarks established by Caliskan et al. (2017) with the Word Embedding Association Test (WEAT) and Zhao et al. (2018) with the WinoBias dataset, these methods produce quantitative evidence of gender bias rather than qualitative impressions. They are widely applied in ethical AI research, media analysis, and fairness auditing of machine-learning systems.	Text classification, also called text categorization, is a supervised natural-language-processing task that automatically assigns documents to predefined categories. Building on the support-vector-machine approach to text categorization established by Joachims (1998) and consolidated in the text-mining literature by Aggarwal and Zhai (2012), it powers tasks such as spam detection and topic classification by learning from labelled examples.