Σύγκριση μεθόδων

	Ανίχνευση Φυλετικής Μεροληψίας στην Επεξεργασία Φυσικής Γλώσσας	Ενσωματώσεις BERT
Πεδίο	Εξόρυξη Κειμένου	Εξόρυξη Κειμένου
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	2017–2018 (seminal benchmarks)	2019
Δημιουργός	Caliskan et al. (2017); Zhao et al. (2018)	Devlin, Chang, Lee & Toutanova (Google AI)
Τύπος	NLP bias auditing pipeline	Contextual transformer text-representation method
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.	Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL-HLT, 4171-4186.
Εναλλακτικές ονομασίες	Toplumsal Cinsiyet Yanlılığı Tespiti — NLP, bias auditing NLP, WEAT, WinoBias	contextual embeddings, transformer embeddings, BERT Tabanlı Metin Gömülmeleri
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.	BERT-based text embeddings, introduced by Devlin and colleagues at Google AI in 2019, turn text into context-sensitive dense vectors using a bidirectional Transformer encoder. Because the meaning of a word shifts with its context, BERT produces richer representations than static methods such as Word2Vec or topic models like LDA.