{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T04:44:22Z","timestamp":1761972262724,"version":"build-2065373602"},"reference-count":44,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"11","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. &amp; Syst."],"published-print":{"date-parts":[[2025,11,1]]},"DOI":"10.1587\/transinf.2024edp7326","type":"journal-article","created":{"date-parts":[[2025,5,6]],"date-time":"2025-05-06T18:06:52Z","timestamp":1746554812000},"page":"1392-1401","source":"Crossref","is-referenced-by-count":0,"title":["Debiasing Large Language Models with Structured Knowledge"],"prefix":"10.1587","volume":"E108.D","author":[{"given":"Congda","family":"MA","sequence":"first","affiliation":[{"name":"Institute of Science Tokyo"}]},{"given":"Tianyu","family":"ZHAO","sequence":"additional","affiliation":[{"name":"Sakana AI"}]},{"given":"Manabu","family":"OKUMURA","sequence":"additional","affiliation":[{"name":"Institute of Science Tokyo"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"crossref","unstructured":"[1] E. Sheng, K.-W. Chang, P. Natarajan, and N. Peng, \u201cThe woman worked as a babysitter: On biases in language generation,\u201d Proc. 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), ed. K. Inui, J. Jiang, V. Ng, and X. Wan, Hong Kong, China, pp.3407-3412, Association for Computational Linguistics, Nov. 2019. 10.18653\/v1\/d19-1339","DOI":"10.18653\/v1\/D19-1339"},{"key":"2","doi-asserted-by":"crossref","unstructured":"[2] D. Nozza, F. Bianchi, and D. Hovy, \u201cHONEST: Measuring hurtful sentence completion in language models,\u201d Proc. 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, ed. K. Toutanova, A. Rumshisky, L. Zettlemoyer, D. Hakkani-Tur, I. Beltagy, S. Bethard, R. Cotterell, T. Chakraborty, and Y. Zhou, Online, pp.2398-2406, Association for Computational Linguistics, June 2021. 10.18653\/v1\/2021.naacl-main.191","DOI":"10.18653\/v1\/2021.naacl-main.191"},{"key":"3","doi-asserted-by":"crossref","unstructured":"[3] J. Hewitt, J. Thickstun, C. Manning, and P. Liang, \u201cBackpack language models,\u201d Proc. 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ed. A. Rogers, J. Boyd-Graber, and N. Okazaki, Toronto, Canada, pp.9103-9125, Association for Computational Linguistics, July 2023. 10.18653\/v1\/2023.acl-long.506","DOI":"10.18653\/v1\/2023.acl-long.506"},{"key":"4","unstructured":"[4] M. Brunet, C. Alkalay-Houlihan, A. Anderson, and R.S. Zemel, \u201cUnderstanding the origins of bias in word embeddings,\u201d Proc. 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, ed. K. Chaudhuri and R. Salakhutdinov, Proceeding Machine Learning Research, vol.97, pp.803-811, PMLR, 2019."},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] O. Papakyriakopoulos, S. Hegelich, J.C.M. Serrano, and F. Marco, \u201cBias in word embeddings,\u201d FAT*\u2006\u201920: Conference on Fairness, Accountability, and Transparency, Barcelona, Spain, January 27-30, 2020, ed. M. Hildebrandt, C. Castillo, L.E. Celis, S. Ruggieri, L. Taylor, and G. Zanfir-Fortuna, pp.446-457, ACM, 2020. 10.1145\/3351095.3372843","DOI":"10.1145\/3351095.3372843"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] S. Dev, T. Li, J.M. Phillips, and V. Srikumar, \u201cOn measuring and mitigating biased inferences of word embeddings,\u201d The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020, pp.7659-7666, AAAI Press, 2020. 10.1609\/aaai.v34i05.6267","DOI":"10.1609\/aaai.v34i05.6267"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] K. Kurita, N. Vyas, A. Pareek, A.W. Black, and Y. Tsvetkov, \u201cMeasuring bias in contextualized word representations,\u201d Proc. First Workshop on Gender Bias in Natural Language Processing, ed. M.R. Costa-juss\u00e0, C. Hardmeier, W. Radford, and K. Webster, Florence, Italy, pp.166-172, Association for Computational Linguistics, Aug. 2019. 10.18653\/v1\/w19-3823","DOI":"10.18653\/v1\/W19-3823"},{"key":"8","unstructured":"[8] H. Touvron, T. Lavril, G. Izacard, X. Martinet, M. Lachaux, T. Lacroix, B. Rozi\u00e8re, N. Goyal, E. Hambro, F. Azhar, A. Rodriguez, A. Joulin, E. Grave, and G. Lample, \u201cLLaMA: Open and efficient foundation language models,\u201d CoRR, vol.abs\/2302.13971, 2023."},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] K. Yang, C. Yu, Y.R. Fung, M. Li, and H. Ji, \u201cADEPT: A debiasing prompt framework,\u201d Thirty-Seventh AAAI Conference on Artificial Intelligence, AAAI 2023, Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence, IAAI 2023, Thirteenth Symposium on Educational Advances in Artificial Intelligence, EAAI 2023, Washington, DC, USA, February 7-14, 2023, ed. B. Williams, Y. Chen, and J. Neville, pp.10780-10788, AAAI Press, 2023. 10.1609\/aaai.v37i9.26279","DOI":"10.1609\/aaai.v37i9.26279"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] P. Badjatiya, M. Gupta, and V. Varma, \u201cStereotypical bias removal for hate speech detection task using knowledge-based generalizations,\u201d The World Wide Web Conference, WWW 2019, San Francisco, CA, USA, May 13-17, 2019, ed. L. Liu, R.W. White, A. Mantrach, F. Silvestri, J.J. McAuley, R. Baeza-Yates, and L. Zia, pp.49-59, ACM, 2019. 10.1145\/3308558.3313504","DOI":"10.1145\/3308558.3313504"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] S. Gururangan, A. Marasovi\u0107, S. Swayamdipta, K. Lo, I. Beltagy, D. Downey, and N.A. Smith, \u201cDon\u2019t stop pretraining: Adapt language models to domains and tasks,\u201d Proc. 58th Annual Meeting of the Association for Computational Linguistics, ed. D. Jurafsky, J. Chai, N. Schluter, and J. Tetreault, Online, pp.8342-8360, Association for Computational Linguistics, July 2020. 10.18653\/v1\/2020.acl-main.740","DOI":"10.18653\/v1\/2020.acl-main.740"},{"key":"12","unstructured":"[12] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, \u201cBERT: Pre-training of deep bidirectional transformers for language understanding,\u201d Proc. 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), ed. J. Burstein, C. Doran, and T. Solorio, Minneapolis, Minnesota, pp.4171-4186, Association for Computational Linguistics, June 2019. 10.18653\/v1\/N19-1423"},{"key":"13","unstructured":"[13] A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, and I. Sutskever, \u201cLanguage models are unsupervised multitask learners,\u201d OpenAI blog, vol.1, no.8, 9, 2019."},{"key":"14","unstructured":"[14] T. Bolukbasi, K. Chang, J.Y. Zou, V. Saligrama, and A.T. Kalai, \u201cMan is to computer programmer as woman is to homemaker? Debiasing word embeddings,\u201d Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain, ed. D.D. Lee, M. Sugiyama, U. von Luxburg, I. Guyon, and R. Garnett, pp.4349-4357, 2016."},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] S. Jentzsch and C. Turan, \u201cGender bias in BERT - Measuring and analysing biases through sentiment rating in a realistic downstream classification task,\u201d Proc. 4th Workshop on Gender Bias in Natural Language Processing (GeBNLP), ed. C. Hardmeier, C. Basta, M.R. Costa-juss\u00e0, G. Stanovsky, and H. Gonen, Seattle, Washington, pp.184-199, Association for Computational Linguistics, July 2022. 10.18653\/v1\/2022.gebnlp-1.20","DOI":"10.18653\/v1\/2022.gebnlp-1.20"},{"key":"16","unstructured":"[16] H.R. Kirk, Y. Jun, F. Volpin, H. Iqbal, E. Benussi, F.A. Dreyer, A. Shtedritski, and Y.M. Asano, \u201cBias out-of-the-box: An empirical analysis of intersectional occupational biases in popular generative language models,\u201d Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual, ed. M. Ranzato, A. Beygelzimer, Y.N. Dauphin, P. Liang, and J.W. Vaughan, pp.2611-2624, 2021."},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] N. Nangia, C. Vania, R. Bhalerao, and S.R. Bowman, \u201cCrowS-pairs: A challenge dataset for measuring social biases in masked language models,\u201d Proc. 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), ed. B. Webber, T. Cohn, Y. He, and Y. Liu, Online, pp.1953-1967, Association for Computational Linguistics, Nov. 2020. 10.18653\/v1\/2020.emnlp-main.154","DOI":"10.18653\/v1\/2020.emnlp-main.154"},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] J.H. Park, J. Shin, and P. Fung, \u201cReducing gender bias in abusive language detection,\u201d Proc. 2018 Conference on Empirical Methods in Natural Language Processing, ed. E. Riloff, D. Chiang, J. Hockenmaier, and J. Tsujii, Brussels, Belgium, pp.2799-2804, Association for Computational Linguistics, Oct.-Nov. 2018. 10.18653\/v1\/d18-1302","DOI":"10.18653\/v1\/D18-1302"},{"key":"19","unstructured":"[19] J. Zhao, T. Wang, M. Yatskar, R. Cotterell, V. Ordonez, and K.-W. Chang, \u201cGender bias in contextualized word embeddings,\u201d Proc. 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), ed. J. Burstein, C. Doran, and T. Solorio, Minneapolis, Minnesota, pp.629-634, Association for Computational Linguistics, June 2019. 10.18653\/v1\/N19-1064"},{"key":"20","doi-asserted-by":"crossref","unstructured":"[20] S. Garg, V. Perot, N. Limtiaco, A. Taly, E.H. Chi, and A. Beutel, \u201cCounterfactual fairness in text classification through robustness,\u201d Proc. 2019 AAAI\/ACM Conference on AI, Ethics, and Society, AIES 2019, Honolulu, HI, USA, January 27-28, 2019, ed. V. Conitzer, G.K. Hadfield, and S. Vallor, pp.219-226, ACM, 2019. 10.1145\/3306618.3317950","DOI":"10.1145\/3306618.3317950"},{"key":"21","doi-asserted-by":"crossref","unstructured":"[21] M. Kaneko and D. Bollegala, \u201cDictionary-based debiasing of pre-trained word embeddings,\u201d Proc. 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, ed. P. Merlo, J. Tiedemann, and R. Tsarfaty, Online, pp.212-223, Association for Computational Linguistics, April 2021. 10.18653\/v1\/2021.eacl-main.16","DOI":"10.18653\/v1\/2021.eacl-main.16"},{"key":"22","doi-asserted-by":"crossref","unstructured":"[22] S. Bordia and S.R. Bowman, \u201cIdentifying and reducing gender bias in word-level language models,\u201d Proc. 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop, ed. S. Kar, F. Nadeem, L. Burdick, G. Durrett, and N.-R. Han, Minneapolis, Minnesota, pp.7-15, Association for Computational Linguistics, June 2019. 10.18653\/v1\/n19-3002","DOI":"10.18653\/v1\/N19-3002"},{"key":"23","unstructured":"[23] V. Zimmermann and M. Hoffmann, \u201cAbsinth: A small world approach to word sense induction,\u201d Proc. 18th Conference on Natural Language Processing (KONVENS 2022), ed. R. Schaefer, X. Bai, M. Stede, and T. Zesch, Potsdam, Germany, pp.121-128, KONVENS 2022 Organizers, 12-15 Sept. 2022."},{"key":"24","doi-asserted-by":"crossref","unstructured":"[24] H. Thakur, A. Jain, P. Vaddamanu, P.P. Liang, and L.-P. Morency, \u201cLanguage models get a gender makeover: Mitigating gender bias with few-shot data interventions,\u201d Proc. 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), ed. A. Rogers, J. Boyd-Graber, and N. Okazaki, Toronto, Canada, pp.340-351, Association for Computational Linguistics, July 2023. 10.18653\/v1\/2023.acl-short.30","DOI":"10.18653\/v1\/2023.acl-short.30"},{"key":"25","doi-asserted-by":"crossref","unstructured":"[25] P.P. Liang, I.M. Li, E. Zheng, Y.C. Lim, R. Salakhutdinov, and L.-P. Morency, \u201cTowards debiasing sentence representations,\u201d Proc. 58th Annual Meeting of the Association for Computational Linguistics, ed. D. Jurafsky, J. Chai, N. Schluter, and J. Tetreault, Online, pp.5502-5515, Association for Computational Linguistics, July 2020. 10.18653\/v1\/2020.acl-main.488","DOI":"10.18653\/v1\/2020.acl-main.488"},{"key":"26","doi-asserted-by":"crossref","unstructured":"[26] O. Shaikh, H. Zhang, W. Held, M. Bernstein, and D. Yang, \u201cOn second thought, let\u2019s not think step by step! Bias and toxicity in zero-shot reasoning,\u201d Proc. 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ed. A. Rogers, J. Boyd-Graber, and N. Okazaki, Toronto, Canada, pp.4454-4470, Association for Computational Linguistics, July 2023. 10.18653\/v1\/2023.acl-long.244","DOI":"10.18653\/v1\/2023.acl-long.244"},{"key":"27","unstructured":"[27] H. Touvron, L. Martin, K. Stone, P. Albert, A. Almahairi, Y. Babaei, N. Bashlykov, S. Batra, P. Bhargava, S. Bhosale, D. Bikel, L. Blecher, C. Canton-Ferrer, M. Chen, G. Cucurull, D. Esiobu, J. Fernandes, J. Fu, W. Fu, B. Fuller, C. Gao, V. Goswami, N. Goyal, A. Hartshorn, S. Hosseini, R. Hou, H. Inan, M. Kardas, V. Kerkez, M. Khabsa, I. Kloumann, A. Korenev, P.S. Koura, M. Lachaux, T. Lavril, J. Lee, D. Liskovich, Y. Lu, Y. Mao, X. Martinet, T. Mihaylov, P. Mishra, I. Molybog, Y. Nie, A. Poulton, J. Reizenstein, R. Rungta, K. Saladi, A. Schelten, R. Silva, E.M. Smith, R. Subramanian, X.E. Tan, B. Tang, R. Taylor, A. Williams, J.X. Kuan, P. Xu, Z. Yan, I. Zarov, Y. Zhang, A. Fan, M. Kambadur, S. Narang, A. Rodriguez, R. Stojnic, S. Edunov, and T. Scialom, \u201cLlama 2: Open foundation and fine-tuned chat models,\u201d CoRR, vol.abs\/2307.09288, 2023."},{"key":"28","doi-asserted-by":"crossref","unstructured":"[28] A. Bosselut, H. Rashkin, M. Sap, C. Malaviya, A. Celikyilmaz, and Y. Choi, \u201cCOMET: Commonsense transformers for automatic knowledge graph construction,\u201d Proc. 57th Annual Meeting of the Association for Computational Linguistics, ed. A. Korhonen, D. Traum, and L. M\u00e0rquez, Florence, Italy, pp.4762-4779, Association for Computational Linguistics, July 2019. 10.18653\/v1\/p19-1470","DOI":"10.18653\/v1\/P19-1470"},{"key":"29","doi-asserted-by":"publisher","unstructured":"[29] J. Guan, F. Huang, Z. Zhao, X. Zhu, and M. Huang, \u201cA knowledge-enhanced pretraining model for commonsense story generation,\u201d Trans. Association for Computational Linguistics, vol.8, pp.93-108, 2020. 10.1162\/tacl_a_00302","DOI":"10.1162\/tacl_a_00302"},{"key":"30","doi-asserted-by":"publisher","unstructured":"[30] G.A. Miller, \u201cWordNet: A lexical database for english,\u201d Commun. ACM, vol.38, no.11, pp.39-41, 1995. 10.1145\/219717.219748","DOI":"10.1145\/219717.219748"},{"key":"31","doi-asserted-by":"publisher","unstructured":"[31] R. Speer, J. Chin, and C. Havasi, \u201cConceptNet 5.5: An open multilingual graph of general knowledge,\u201d Proc. Thirty-First AAAI Conference on Artificial Intelligence, February 4-9, 2017, San Francisco, California, USA, ed. S. Singh and S. Markovitch, vol.31, no.1, pp.4444-4451, AAAI Press, 2017. 10.1609\/aaai.v31i1.11164","DOI":"10.1609\/aaai.v31i1.11164"},{"key":"32","doi-asserted-by":"crossref","unstructured":"[32] S. Black, G. Leo, P. Wang, C. Leahy, and S. Biderman, \u201cGPT-Neo: Large scale autoregressive language modeling with mesh-tensorflow,\u201d Zenodo, 2021. 10.5281\/zenodo.5297715","DOI":"10.18653\/v1\/2022.bigscience-1.9"},{"key":"33","unstructured":"[33] D.P. Kingma and J. Ba, \u201cAdam: A method for stochastic optimization,\u201d 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, ed. Y. Bengio and Y. LeCun, 2015."},{"key":"34","unstructured":"[34] S. Zhang, S. Roller, N. Goyal, M. Artetxe, M. Chen, S. Chen, C. Dewan, M.T. Diab, X. Li, X.V. Lin, T. Mihaylov, M. Ott, S. Shleifer, K. Shuster, D. Simig, P.S. Koura, A. Sridhar, T. Wang, and L. Zettlemoyer, \u201cOPT: Open pre-trained transformer language models,\u201d CoRR, vol.abs\/2205.01068, 2022."},{"key":"35","unstructured":"[35] T.B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D.M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, \u201cLanguage models are few-shot learners,\u201d Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual, ed. H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, and H. Lin, 2020."},{"key":"36","unstructured":"[36] Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov, \u201cRoBERTa: A robustly optimized BERT pretraining approach,\u201d CoRR, vol.abs\/1907.11692, 2019."},{"key":"37","doi-asserted-by":"crossref","unstructured":"[37] Y. Guo, Y. Yang, and A. Abbasi, \u201cAuto-Debias: Debiasing masked language models with automated biased prompts,\u201d Proc. 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ed. S. Muresan, P. Nakov, and A. Villavicencio, Dublin, Ireland, pp.1012-1023, Association for Computational Linguistics, May 2022. 10.18653\/v1\/2022.acl-long.72","DOI":"10.18653\/v1\/2022.acl-long.72"},{"key":"38","doi-asserted-by":"crossref","unstructured":"[38] P.N. Venkit, S. Gautam, R. Panchanadikar, T.-H. Huang, and S. Wilson, \u201cNationality bias in text generation,\u201d Proc. 17th Conference of the European Chapter of the Association for Computational Linguistics, ed. A. Vlachos and I. Augenstein, Dubrovnik, Croatia, pp.116-122, Association for Computational Linguistics, May 2023. 10.18653\/v1\/2023.eacl-main.9","DOI":"10.18653\/v1\/2023.eacl-main.9"},{"key":"39","unstructured":"[39] M. Kamruzzaman and G.L. Kim, \u201cPrompting techniques for reducing social bias in LLMs through system 1 and system 2 cognitive processes,\u201d CoRR, vol.abs\/2404.17218, 2024."},{"key":"40","unstructured":"[40] S. Merity, C. Xiong, J. Bradbury, and R. Socher, \u201cPointer sentinel mixture models,\u201d 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings, OpenReview.net, 2017."},{"key":"41","doi-asserted-by":"crossref","unstructured":"[41] J. Dhamala, T. Sun, V. Kumar, S. Krishna, Y. Pruksachatkun, K. Chang, and R. Gupta, \u201cBOLD: dataset and metrics for measuring biases in open-ended language generation,\u201d FAccT\u2006\u201921: 2021 ACM Conference on Fairness, Accountability, and Transparency, Virtual Event\/Toronto, Canada, March 3-10, 2021, ed. M.C. Elish, W. Isaac, and R.S. Zemel, pp.862-872, ACM, 2021. 10.1145\/3442188.3445924","DOI":"10.1145\/3442188.3445924"},{"key":"42","doi-asserted-by":"crossref","unstructured":"[42] M.P. Marcus, B. Santorini, and M.A. Marcinkiewicz, \u201cBuilding a large annotated corpus of English: The Penn Treebank,\u201d Computational Linguistics, vol.19, no.2, pp.313-330, 1993.","DOI":"10.21236\/ADA273556"},{"key":"43","doi-asserted-by":"publisher","unstructured":"[43] T. Schick, S. Udupa, and H. Sch\u00fctze, \u201cSelf-diagnosis and self-debiasing: A proposal for reducing corpus-based bias in NLP,\u201d Trans. Association for Computational Linguistics, vol.9, pp.1408-1424, 2021. 10.1162\/tacl_a_00434","DOI":"10.1162\/tacl_a_00434"},{"key":"44","doi-asserted-by":"publisher","unstructured":"[44] A. Caliskan, J.J. Bryson, and A. Narayanan, \u201cSemantics derived automatically from language corpora contain human-like biases,\u201d Science, vol.356, no.6334, pp.183-186, 2017. 10.1126\/science.aal4230","DOI":"10.1126\/science.aal4230"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E108.D\/11\/E108.D_2024EDP7326\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T03:36:10Z","timestamp":1761968170000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E108.D\/11\/E108.D_2024EDP7326\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,1]]},"references-count":44,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2024edp7326","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"type":"print","value":"0916-8532"},{"type":"electronic","value":"1745-1361"}],"subject":[],"published":{"date-parts":[[2025,11,1]]},"article-number":"2024EDP7326"}}