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1	El sesgo ideológico en el discurso político de la prensa española: la reforma constitucional de 2011
	Ramos Ruiz, Álvaro; Ramos Ruiz, Ismael
	In: ISSN: 1576-4737 ; CIRCULO de Linguistica Aplicada a la Comunicacion ; https://hal.archives-ouvertes.fr/hal-03616798 ; CIRCULO de Linguistica Aplicada a la Comunicacion, Universidad Complutense de Madrid, 2022, 89, pp.171-182 (2022)
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2	Computational Measures of Deceptive Language: Prospects and Issues
	Tomas, Frédéric; Dodier, Olivier; Demarchi, Samuel
	In: ISSN: 2297-900X ; EISSN: 2297-900X ; Frontiers in Communication ; https://hal.archives-ouvertes.fr/hal-03629780 ; Frontiers in Communication, Frontiers, 2022, 7, pp.792378. ⟨10.3389/fcomm.2022.792378⟩ (2022)
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3	Labour market discrimination and biases in human judgement and Artificial Intelligence ...
	Patel, Krishane. - : Open Science Framework, 2022
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4	Linguistic intergroup bias and persistence of stereotype-affirming memory ...
	Lee, Junho. - : Open Science Framework, 2022
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5	Linguistic intergroup bias and persistence of stereotype-affirming memory - Addendum 04.26.2022 ...
	Lee, Junho. - : Open Science Framework, 2022
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6	Quality and Efficiency of Manual Annotation: Data from the Pre-annotation Bias Experiment (part of the PDT-C 2.0 project)
	Mikulová, Marie; Straka, Milan; Štěpánek, Jan. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2022
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7	Threat assessment, sense making, and critical decision-making in police, military, ambulance, and fire services
	Penney, Greg; Launder, David; Cuthbertson, Joe...
	In: Research outputs 2022 to 2026 (2022)
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8	Exploring the links between student ethnicity, perceived SES and teachers' academic judgements ...
	Doyle, Lewis. - : Open Science Framework, 2022
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9	The influence of animacy on perspective-taking and word order during language production ...
	Brough, Jessica. - : Open Science Framework, 2022
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10	Measuring and Comparing Social Bias in Static and Contextual Word Embeddings
	Mora, Alan Cueva. - : Technological University Dublin, 2022
	In: Dissertations (2022)
	Abstract: Word embeddings have been considered one of the biggest breakthroughs of deep learning for natural language processing. They are learned numerical vector representations of words where similar words have similar representations. Contextual word embeddings are the promising second-generation of word embeddings assigning a representation to a word based on its context. This can result in different representations for the same word depending on the context (e.g. river bank and commercial bank). There is evidence of social bias (human-like implicit biases based on gender, race, and other social constructs) in word embeddings. While detecting bias in static (classical or non-contextual) word embeddings is a well-researched topic, there has been limited work in detecting bias in contextual word embeddings, mostly focussed on using the Word Embedding Association Test (WEAT). This paper explores measuring social bias (gender, ethnicity, and religion) in contextual word embeddings using a number of fairness metrics, including the Relative Norm Distance (RND), the Relative Negative Sentiment Bias (RNSB) and the already mentioned WEAT. It extends the Word Embeddings Fairness Evaluation (WEFE) framework to facilitate measuring social biases in contextual embeddings and compares these with biases in static word embeddings. The results show when ranking performance over a number of fairness metrics that contextual word embedding pre-trained models BERT and RoBERTa have more social bias than static word embedding pre-trained models GloVe and Word2Vec.
	Keyword: Computer Engineering; Computer Sciences; Contextual Word Embeddings; Fairness Evaluation; Natural Language Processing; Sentence Embeddings; Social Bias; Word Embeddings
	URL: https://arrow.tudublin.ie/scschcomdis/250 https://arrow.tudublin.ie/cgi/viewcontent.cgi?article=1267&context=scschcomdis
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11	Assessing Gender Bias in Particle Physics and Social Science Recommendations for Academic Jobs
	Robert H. Bernstein; Michael W. Macy; Wendy M. Williams; Christopher J. Cameron; Sterling Chance Williams-Ceci; Stephen J. Ceci
	In: Social Sciences; Volume 11; Issue 2; Pages: 74 (2022)
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12	Language-Internal Reanalysis of Clitic Placement in Heritage Grammars Reduces the Cost of Computation: Evidence from Bulgarian
	Tanya Ivanova-Sullivan; Irina A. Sekerina; Davood Tofighi; Maria Polinsky
	In: Languages; Volume 7; Issue 1; Pages: 24 (2022)
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13	Measuring Gender Bias in Contextualized Embeddings
	Styliani Katsarou; Borja Rodríguez-Gálvez; Jesse Shanahan
	In: Computer Sciences & Mathematics Forum; Volume 3; Issue 1; Pages: 3 (2022)
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14	Gender Bias in Text: Labeled Datasets and Lexicons ...
	Doughman, Jad; Khreich, Wael. - : Zenodo, 2022
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15	Gender Bias in Text: Labeled Datasets and Lexicons ...
	Doughman, Jad; Khreich, Wael. - : Zenodo, 2022
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16	Gender Bias in Text: Labeled Datasets and Lexicons ...
	Doughman, Jad; Khreich, Wael. - : Zenodo, 2022
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17	Promoting Equity and Engagement with Randomness
	Saiewitz, Aaron
	In: UNLV Best Teaching Practices Expo (2022)
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18	MaxEnt Learners are Biased Against Giving Probability to Harmonically Bounded Candidates
	O'Hara, Charlie
	In: Proceedings of the Society for Computation in Linguistics (2022)
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19	Prices are rising, wages are falling: Argument structure of verbs denoting ‘increase’ and ‘decrease’ in the Russian language
	Valentina Y. Apresjan
	In: Russian Journal of Linguistics, Vol 26, Iss 1, Pp 194-223 (2022) (2022)
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20	A unified analysis of polar particles in Farsi
	Mohammadi, Maryam
	In: Proceedings of the Linguistic Society of America; Vol 7, No 1 (2022): Proceedings of the Linguistic Society of America; 5268 ; 2473-8689 (2022)
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