{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T19:24:42Z","timestamp":1778613882463,"version":"3.51.4"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T00:00:00Z","timestamp":1661990400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T00:00:00Z","timestamp":1661990400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Data Sci Anal"],"published-print":{"date-parts":[[2024,1]]},"DOI":"10.1007\/s41060-022-00359-4","type":"journal-article","created":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T09:04:12Z","timestamp":1662023052000},"page":"39-59","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Dbias: detecting biases and ensuring fairness in news articles"],"prefix":"10.1007","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1061-5845","authenticated-orcid":false,"given":"Shaina","family":"Raza","sequence":"first","affiliation":[]},{"given":"Deepak John","family":"Reji","sequence":"additional","affiliation":[]},{"given":"Chen","family":"Ding","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,1]]},"reference":[{"issue":"3","key":"359_CR1","first-page":"2493","volume":"12","author":"R Collobert","year":"2011","unstructured":"Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12(3), 2493\u20132537 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"359_CR2","volume-title":"Practical fairness","author":"A Nielsen","year":"2020","unstructured":"Nielsen, A.: Practical fairness. O\u2019Reilly Media, Sebastopol (2020)"},{"issue":"6464","key":"359_CR3","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1126\/science.aax2342","volume":"366","author":"Z Obermeyer","year":"2019","unstructured":"Obermeyer, Z., Powers, B., Vogeli, C., Mullainathan, S.: Dissecting racial bias in an algorithm used to manage the health of populations. Science (80) 366(6464), 447\u2013453 (2019)","journal-title":"Science (80)"},{"issue":"1","key":"359_CR4","first-page":"1","volume":"55","author":"S Raza","year":"2021","unstructured":"Raza, S., Ding, C.: News recommender system: a review of recent progress, challenges, and opportunities. Artif. Intell. Rev. 55(1), 1\u201352 (2021)","journal-title":"Artif. Intell. Rev."},{"key":"359_CR5","doi-asserted-by":"crossref","unstructured":"F. N. Ribeiro et al., \u201cMedia bias monitor: Quantifying biases of social media news outlets at large-scale\u201d. In: Twelfth international AAAI conference on web and social media, 2018.","DOI":"10.1609\/icwsm.v12i1.15025"},{"issue":"6","key":"359_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3457607","volume":"54","author":"N Mehrabi","year":"2021","unstructured":"Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., Galstyan, A.: A survey on bias and fairness in machine learning. ACM Comput. Surv. 54(6), 1\u201335 (2021)","journal-title":"ACM Comput. Surv."},{"key":"359_CR7","unstructured":"S. Caton and C. Haas, \u201cFairness in Machine Learning: A Survey,\u201d pp. 1\u201333, 2020."},{"issue":"4\u20135","key":"359_CR8","first-page":"401","volume":"63","author":"RKE Bellamy","year":"2019","unstructured":"Bellamy, R.K.E., et al.: AI Fairness 360: an extensible toolkit for detecting and mitigating algorithmic bias. IBM J. Res. Dev. 63(4\u20135), 401\u2013415 (2019)","journal-title":"IBM J. Res. Dev."},{"key":"359_CR9","first-page":"385","volume-title":"Does Gender matter in the news? Detecting and examining gender bias in news articles","author":"J Dacon","year":"2021","unstructured":"Dacon, J., Liu, H.: Does Gender matter in the news? Detecting and examining gender bias in news articles, pp. 385\u2013392. Springer, New York (2021)"},{"key":"359_CR10","doi-asserted-by":"crossref","unstructured":"K. Orphanou et al., \u201cMitigating Bias in Algorithmic Systems -- A Fish-Eye View\u201d. ACM Comput. Surv., 2021.","DOI":"10.1145\/3527152"},{"key":"359_CR11","doi-asserted-by":"crossref","unstructured":"D. Borkan, L. Dixon, J. Sorensen, N. Thain, and L. Vasserman, \u201cNuanced metrics for measuring unintended bias with real data for text classification\u201d. Web Conf. 2019 - Companion World Wide Web Conf. WWW 2019, vol. 2, pp. 491\u2013500, 2019.","DOI":"10.1145\/3308560.3317593"},{"key":"359_CR12","doi-asserted-by":"crossref","unstructured":"M. Feldman, S. A. Friedler, J. Moeller, C. Scheidegger, and S. Venkatasubramanian, \u201cCertifying and removing disparate impact\u201d. In: proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, 2015, pp. 259\u2013268.","DOI":"10.1145\/2783258.2783311"},{"key":"359_CR13","doi-asserted-by":"crossref","unstructured":"N. Mehrabi, T. Gowda, F. Morstatter, N. Peng, and A. Galstyan, \u201cMan is to person as woman is to location: Measuring gender bias in named entity recognition\u201d. Proc. 31st ACM Conf. Hypertext Soc. Media, HT 2020, pp. 231\u2013232, 2020.","DOI":"10.1145\/3372923.3404804"},{"key":"359_CR14","unstructured":"J. A. Adebayo et al., \u201cFairML: ToolBox for diagnosing bias in predictive modeling\u201d. Massachusetts Institute of Technology, 2016."},{"key":"359_CR15","doi-asserted-by":"crossref","unstructured":"F. Tram\u00e8r et al., \u201cFairTest: Discovering Unwarranted Associations in Data-Driven Applications\u201d. Proc. - 2nd IEEE Eur. Symp. Secur. Privacy, EuroS P 2017, pp. 401\u2013416, 2017.","DOI":"10.1109\/EuroSP.2017.29"},{"issue":"1","key":"359_CR16","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1080\/15228835.2017.1416512","volume":"36","author":"N Bantilan","year":"2018","unstructured":"Bantilan, N.: Themis-ml: a fairness-aware machine learning interface for end-to-end discrimination discovery and mitigation. J. Technol. Hum. Serv. 36(1), 15\u201330 (2018)","journal-title":"J. Technol. Hum. Serv."},{"key":"359_CR17","unstructured":"H. Hapke, C. Nelson, \u201cBuilding Machine Learning Pipelines\u201d. Building machine learning pipelines\u202f: automating model life cycles with TensorFlow. p. 367, 2020."},{"key":"359_CR18","unstructured":"A. Narayanan, \u201cFairness Definitions and Their Politics\u201d. In Tutorial presented at the Conf. on Fairness, Accountability, and Transparency, 2018."},{"issue":"1","key":"359_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10115-011-0463-8","volume":"33","author":"F Kamiran","year":"2012","unstructured":"Kamiran, F., Calders, T.: Data preprocessing techniques for classification without discrimination. Knowl. Inf. Syst. 33(1), 1\u201333 (2012)","journal-title":"Knowl. Inf. Syst."},{"key":"359_CR20","unstructured":"R. Zemel, Y. Wu, K. Swersky, T. Pitassi, and C. Dwork, \u201cLearning fair representations\u201d. In: International conference on machine learning, 2013, pp. 325\u2013333."},{"key":"359_CR21","unstructured":"F. P. Calmon, D. Wei, B. Vinzamuri, K. N. Ramamurthy, and K. R. Varshney, \u201cOptimized pre-processing for discrimination prevention\u201d. Adv. Neural Inf. Process. Syst., vol. 2017-Decem, no. Nips, pp. 3993\u20134002, 2017."},{"key":"359_CR22","first-page":"35","volume-title":"Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics)","author":"T Kamishima","year":"2012","unstructured":"Kamishima, T., Akaho, S., Asoh, H., Sakuma, J.: Fairness-aware classifier with prejudice remover regularizer. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), pp. 35\u201350. Springer, New Jersey (2012)"},{"key":"359_CR23","doi-asserted-by":"crossref","unstructured":"L. E. Celis, L. Huang, V. Keswani, and N. K. Vishnoi, \u201cClassification with fairness constraints: A meta-algorithm with provable guarantees\u201d. In: Proceedings of the conference on fairness, accountability, and transparency, 2019, pp. 319\u2013328.","DOI":"10.1145\/3287560.3287586"},{"key":"359_CR24","doi-asserted-by":"crossref","unstructured":"B. H. Zhang, B. Lemoine, M. Mitchell, \u201cMitigating unwanted biases with adversarial learning,\u201d in Proceedings of the 2018 AAAI\/ACM Conference on AI, Ethics, and Society, 2018, pp. 335\u2013340.","DOI":"10.1145\/3278721.3278779"},{"key":"359_CR25","unstructured":"A. Agarwal, A. Beygelzimer, M. Dud\\\u2019\\ik, J. Langford, and H. Wallach, \u201cA reductions approach to fair classification,\u201d in International Conference on Machine Learning, 2018, pp. 60\u201369."},{"key":"359_CR26","doi-asserted-by":"crossref","unstructured":"F. Kamiran, A. Karim, and X. Zhang, \u201cDecision theory for discrimination-aware classification\u201d. In: 2012 IEEE 12th International Conference on Data Mining, 2012, pp. 924\u2013929.","DOI":"10.1109\/ICDM.2012.45"},{"key":"359_CR27","first-page":"3315","volume":"29","author":"M Hardt","year":"2016","unstructured":"Hardt, M., Price, E., Srebro, N.: Equality of opportunity in supervised learning. Adv. Neural Inf. Process. Syst. 29, 3315\u20133323 (2016)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"359_CR28","unstructured":"G. Pleiss, M. Raghavan, F. Wu, J. Kleinberg, and K. Q. Weinberger, \u201cOn fairness and calibration,\u201d arXiv Prepr. arXiv1709.02012, 2017."},{"key":"359_CR29","doi-asserted-by":"crossref","unstructured":"S. Udeshi, P. Arora, and S. Chattopadhyay, \u201cAutomated directed fairness testing,\u201d in Proceedings of the 33rd ACM\/IEEE International Conference on Automated Software Engineering, 2018, pp. 98\u2013108.","DOI":"10.1145\/3238147.3238165"},{"key":"359_CR30","unstructured":"P. Saleiro et al., \u201cAequitas: A bias and fairness audit toolkit,\u201d arXiv Prepr. arXiv1811.05577, 2018."},{"key":"359_CR31","doi-asserted-by":"crossref","unstructured":"C. Tan, F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu, \u201cA survey on deep transfer learning,\u201d in International conference on artificial neural networks, 2018, pp. 270\u2013279.","DOI":"10.1007\/978-3-030-01424-7_27"},{"key":"359_CR32","unstructured":"J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, \u201cBERT: Pre-training of deep bidirectional transformers for language understanding,\u201d arXiv Prepr. arXiv1810.04805, 2018."},{"key":"359_CR33","unstructured":"B. Li et al., \u201cDetecting Gender Bias in Transformer-based Models: A Case Study on BERT\u201d. 1, 2021."},{"key":"359_CR34","doi-asserted-by":"crossref","unstructured":"M. Sinha and T. Dasgupta, \u201cDetermining Subjective Bias in Text through Linguistically Informed Transformer based Multi-Task Network\u201d. In: Proceedings of the 30th ACM International Conference on Information\\& Knowledge Management, 2021, pp. 3418\u20133422.","DOI":"10.1145\/3459637.3482084"},{"issue":"1","key":"359_CR35","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1075\/li.30.1.03nad","volume":"30","author":"D Nadeau","year":"2007","unstructured":"Nadeau, D., Sekine, S.: A survey of named entity recognition and classification. Lingvisticae Investig. 30(1), 3\u201326 (2007)","journal-title":"Lingvisticae Investig."},{"key":"359_CR36","doi-asserted-by":"crossref","unstructured":"E. Excell and N. Al Moubayed, \u201cTowards Equal Gender Representation in the Annotations of Toxic Language Detection,\u201d 2021.","DOI":"10.18653\/v1\/2021.gebnlp-1.7"},{"key":"359_CR37","unstructured":"S. Mishra, S. He, L. Belli, \u201cAssessing Demographic Bias in Named Entity Recognition,\u201d 2020."},{"key":"359_CR38","unstructured":"M. Kaneko and D. Bollegala, \u201cUnmasking the Mask--Evaluating Social Biases in Masked Language Models,\u201d arXiv Prepr. arXiv2104.07496, 2021."},{"key":"359_CR39","unstructured":"P. G\u00f6lz, A. Kahng, and A. D. Procaccia, \u201cParadoxes in fair machine learning,\u201d Adv. Neural Inf. Process. Syst., 32, 2019."},{"key":"359_CR40","unstructured":"J. Mastrine, \u201cTypes of Media Bias and How to Spot It | AllSides,\u201d AllSides. 2018."},{"key":"359_CR41","first-page":"1","volume":"2021","author":"T Spinde","year":"2021","unstructured":"Spinde, T., Rudnitckaia, L., Sinha, K., Hamborg, F., Gipp, B., Donnay, K.: MBIC \u2013 A media bias annotation dataset including annotator characteristics. Proc. iConf 2021, 1\u20138 (2021)","journal-title":"Proc. iConf"},{"key":"359_CR42","unstructured":"V. Sanh, L. Debut, J. Chaumond, T. Wolf, \u201cDistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter,\u201d arXiv Prepr. arXiv1910.01108, 2019."},{"key":"359_CR43","unstructured":"Y. Liu et al., \u201cRoBERTa: A Robustly Optimized BERT Pretraining Approach,\u201d pp. 2383\u20132392, 2019."},{"issue":"1","key":"359_CR44","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1037\/a0022530","volume":"101","author":"D Gaucher","year":"2011","unstructured":"Gaucher, D., Friesen, J., Kay, A.C.: Evidence that gendered wording in job advertisements exists and sustains gender inequality. J. Pers. Soc. Psychol. 101(1), 109\u2013128 (2011)","journal-title":"J. Pers. Soc. Psychol."},{"key":"359_CR45","unstructured":"S. Menendian, E. Elsheikh, S. Gambhir, \u201cInclusiveness Index: Measuring Global Inclusion and Marginality (Berkeley, CA: Haas Institute for a Fair and Inclusive Society, 2018), haasinstitute. berkeley. edu\/inclusivenessindex,\u201d 2018."},{"key":"359_CR46","unstructured":"IBM Cloud Paks, \u201cFairness metrics overview - IBM Documentation,\u201d 2022. [Online]. Available: https:\/\/www.ibm.com\/docs\/en\/cloud-paks\/cp-data\/4.0?topic=openscale-fairness-metrics-overview. [Accessed: 12 Jan 2022]."},{"key":"359_CR47","doi-asserted-by":"crossref","unstructured":"K. Sinha, R. Jia, D. Hupkes, J. Pineau, A. Williams, D. Kiela, \u201cMasked language modeling and the distributional hypothesis: Order word matters pre-training for little,\u201d arXiv Prepr. arXiv2104.06644, 2021.","DOI":"10.18653\/v1\/2021.emnlp-main.230"},{"key":"359_CR48","unstructured":"G. Alves, V. Bhargava, F. Bernier, M. Couceiro, and A. Napoli, \u201cFixOut: an ensemble approach to fairer models,\u201d 2020."},{"key":"359_CR49","doi-asserted-by":"crossref","unstructured":"G. Alves, M. Amblard, F. Bernier, M. Couceiro, and A. Napoli, \u201cReducing unintended bias of ml models on tabular and textual data,\u201d in 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA), 2021, pp. 1\u201310.","DOI":"10.1109\/DSAA53316.2021.9564112"},{"issue":"1","key":"359_CR50","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1109\/MIS.2021.3088543","volume":"37","author":"Y Luo","year":"2021","unstructured":"Luo, Y., et al.: Robust precipitation bias correction through an ordinal distribution autoencoder. IEEE Intell. Syst. 37(1), 60\u201370 (2021)","journal-title":"IEEE Intell. Syst."},{"issue":"2","key":"359_CR51","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1007\/s41060-021-00259-z","volume":"12","author":"Y Wang","year":"2021","unstructured":"Wang, Y., Singh, L.: Analyzing the impact of missing values and selection bias on fairness. Int. J. Data Sci. Anal. 12(2), 101\u2013119 (2021)","journal-title":"Int. J. Data Sci. Anal."},{"issue":"1","key":"359_CR52","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1007\/s41060-020-00231-3","volume":"11","author":"T Yang","year":"2021","unstructured":"Yang, T., Yao, R., Yin, Q., Tian, Q., Wu, O.: Mitigating sentimental bias via a polar attention mechanism. Int. J. Data Sci. Anal. 11(1), 27\u201336 (2021)","journal-title":"Int. J. Data Sci. Anal."},{"key":"359_CR53","doi-asserted-by":"crossref","unstructured":"S. Raza and C. Ding, \u201cFake news detection based on news content and social contexts: a transformer-based approach,\u201d Int. J. Data Sci. Anal., 2022.","DOI":"10.1007\/s41060-021-00302-z"},{"issue":"May","key":"359_CR54","first-page":"1","volume":"1","author":"X Wu","year":"2020","unstructured":"Wu, X., Lode, M.: Language models are unsupervised multitask learners (summarization). OpenAI Blog 1(May), 1\u20137 (2020)","journal-title":"OpenAI Blog"},{"key":"359_CR55","unstructured":"M. J. Kusner, J. Loftus, C. Russell, and R. Silva, \u201cCounterfactual fairness,\u201d Adv. Neural Inf. Process. Syst., 30, 2017."}],"container-title":["International Journal of Data Science and Analytics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41060-022-00359-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s41060-022-00359-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41060-022-00359-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T13:28:04Z","timestamp":1744205284000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s41060-022-00359-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,1]]},"references-count":55,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,1]]}},"alternative-id":["359"],"URL":"https:\/\/doi.org\/10.1007\/s41060-022-00359-4","relation":{},"ISSN":["2364-415X","2364-4168"],"issn-type":[{"value":"2364-415X","type":"print"},{"value":"2364-4168","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,1]]},"assertion":[{"value":"13 February 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 August 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 September 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"On behalf of all authors, the corresponding author states that there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}