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Journal articles on the topic 'Bias detection'

Author: Grafiati

Published: 4 June 2025

Last updated: 15 July 2025

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1

Khan, Afreen, Anu Chandra, Abbas Ali Mahdi, Syed Tasleem Raza, and Esha Sarkar. "PROSTATE CANCER AND DIABETES: BIOLOGY OR DETECTION BIAS?" Era's Journal of Medical Research 9, no. 2 (2022): 227–38. http://dx.doi.org/10.24041/ejmr2022.36.

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Prostate cancer and diabetes are the two highly prevalent health problems in men worldwide and have a high mortality rates but their association is quite complex and contradictory. This review reported several population based studies which tried to establish a possible association and explains the mechanism by which diabetes exhibits its effect on prostate cancer progression. It also explores the literature around the expression of various receptors and genes which enlightens the possible molecular basis of association and the effect of current antidiabetic drugs like metformin and insulin on

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Galea-Rojas, Manuel, M�rcio V. de Castilho, Heleno Bolfarine, and M�rio de Castro. "Detection of analytical bias." Analyst 128, no. 8 (2003): 1073. http://dx.doi.org/10.1039/b212547a.

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Alshareef, Norah, Xiaohong Yuan, Kaushik Roy, and Mustafa Atay. "A Study of Gender Bias in Face Presentation Attack and Its Mitigation." Future Internet 13, no. 9 (2021): 234. http://dx.doi.org/10.3390/fi13090234.

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In biometric systems, the process of identifying or verifying people using facial data must be highly accurate to ensure a high level of security and credibility. Many researchers investigated the fairness of face recognition systems and reported demographic bias. However, there was not much study on face presentation attack detection technology (PAD) in terms of bias. This research sheds light on bias in face spoofing detection by implementing two phases. First, two CNN (convolutional neural network)-based presentation attack detection models, ResNet50 and VGG16 were used to evaluate the fair

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Clementson, David E. "Truth Bias and Partisan Bias in Political Deception Detection." Journal of Language and Social Psychology 37, no. 4 (2017): 407–30. http://dx.doi.org/10.1177/0261927x17744004.

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This study tests the effects of political partisanship on voters’ perception and detection of deception. Based on social identity theory, in-group members should consider their politician’s message truthful while the opposing out-group would consider the message deceptive. Truth-default theory predicts that a salient in-group would be susceptible to deception from their in-group politician. In an experiment, partisan voters in the United States ( N = 618) watched a news interview in which a politician was labeled Democratic or Republican. The politician either answered all the questions or dec

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Aggarwal, Swati, Tushar Sinha, Yash Kukreti, and Siddarth Shikhar. "Media bias detection and bias short term impact assessment." Array 6 (July 2020): 100025. http://dx.doi.org/10.1016/j.array.2020.100025.

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6

Yaxley, Richard H., and Rolf A. Zwaan. "Attentional bias affects change detection." Psychonomic Bulletin & Review 12, no. 6 (2005): 1106–11. http://dx.doi.org/10.3758/bf03206451.

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Wang, Lan, Simone M. Weinmann, Gabriella De Lucia, and Xiaohu Yang. "Detection of galaxy assembly bias." Monthly Notices of the Royal Astronomical Society 433, no. 1 (2013): 515–20. http://dx.doi.org/10.1093/mnras/stt743.

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De, Arruda Gabriel, Norton Roman, and Ana Monteiro. "Analysing Bias in Political News." JUCS - Journal of Universal Computer Science 26, no. (2) (2020): 173–99. https://doi.org/10.3897/jucs.2020.011.

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Although of paramount importance to all societies, the fact that media can be biased is a troubling thought to many people. The problem, however, is by no means easy to solve, given its high subjectivity, thereby leading to a number of different approaches by researchers. In this work, we addressed media bias according to a tripartite model whereby news can suffer from a combination of selective coverage of issues (Selection Bias), disproportionate attention given to specific subjects (Coverage Bias), and the favouring of one side in a dispute (Statement Bias). To do so, we approached the prob

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Reiner, Adam J., Justin G. Hollands, and Greg A. Jamieson. "Target Detection and Identification Performance Using an Automatic Target Detection System." Human Factors: The Journal of the Human Factors and Ergonomics Society 59, no. 2 (2016): 242–58. http://dx.doi.org/10.1177/0018720816670768.

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Objective: We investigated the effects of automatic target detection (ATD) on the detection and identification performance of soldiers. Background: Prior studies have shown that highlighting targets can aid their detection. We provided soldiers with ATD that was more likely to detect one target identity than another, potentially acting as an implicit identification aid. Method: Twenty-eight soldiers detected and identified simulated human targets in an immersive virtual environment with and without ATD. Task difficulty was manipulated by varying scene illumination (day, night). The ATD identif

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Wen, Zehao, and Rabih Younes. "ChatGPT v.s. media bias: A comparative study of GPT-3.5 and fine-tuned language models." Applied and Computational Engineering 21, no. 1 (2023): 249–57. http://dx.doi.org/10.54254/2755-2721/21/20231153.

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In our rapidly evolving digital sphere, the ability to discern media bias becomes crucial as it can shape public sentiment and influence pivotal decisions. The advent of large language models (LLMs), such as ChatGPT, noted for their broad utility in various natural language processing (NLP) tasks, invites exploration of their efficacy in media bias detection. Can ChatGPT detect media bias? This study seeks to answer this question by leveraging the Media Bias Identification Benchmark (MBIB) to assess ChatGPT's competency in distinguishing six categories of media bias, juxtaposed against fine-tu

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AKAHANE, Hirokazu, and Masaki KOSHI. "Online automatic correction vehicle detection bias." Doboku Gakkai Ronbunshu, no. 407 (1989): 27–36. http://dx.doi.org/10.2208/jscej.1989.407_27.

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Kelderman, Henk. "Item bias detection using loglinear irt." Psychometrika 54, no. 4 (1989): 681–97. http://dx.doi.org/10.1007/bf02296403.

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Barendse, M. T., F. J. Oort, C. S. Werner, R. Ligtvoet, and K. Schermelleh-Engel. "Measurement Bias Detection Through Factor Analysis." Structural Equation Modeling: A Multidisciplinary Journal 19, no. 4 (2012): 561–79. http://dx.doi.org/10.1080/10705511.2012.713261.

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14

Shetty, Harshaprabha N., Sony Asampalli, and Prabhu Vara Prasad Bonam. "Data Bias Detection in Machine Learning." International Journal of Scientific and Research Publications 12, no. 10 (2022): 557–62. http://dx.doi.org/10.29322/ijsrp.12.10.2022.p13071.

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Yee Lim, Chun, Tony Badrick, and Tze Ping Loh. "Patient-based quality control for glucometers using the moving sum of positive patient results and moving average." Biochemia medica 30, no. 2 (2020): 296–306. http://dx.doi.org/10.11613/bm.2020.020709.

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Introduction: The capability of glucometer internal quality control (QC) in detecting varying magnitude of systematic error (bias), and the potential use of moving sum of positive results (MovSum) and moving average (MA) techniques as potential alternatives were evaluated. Materials and methods: The probability of error detection using routine QC and manufacturer’s control limits were investigated using historical data. Moving sum of positive results and MA algorithms were developed and optimized before being evaluated through numerical simulation for false positive rate and probability of err

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Alelyani, Salem. "Detection and Evaluation of Machine Learning Bias." Applied Sciences 11, no. 14 (2021): 6271. http://dx.doi.org/10.3390/app11146271.

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Machine learning models are built using training data, which is collected from human experience and is prone to bias. Humans demonstrate a cognitive bias in their thinking and behavior, which is ultimately reflected in the collected data. From Amazon’s hiring system, which was built using ten years of human hiring experience, to a judicial system that was trained using human judging practices, these systems all include some element of bias. The best machine learning models are said to mimic humans’ cognitive ability, and thus such models are also inclined towards bias. However, detecting and e

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Gawali, Apurva. "Bias Checker AI Web Application: A Framework for Identifying Bias in AI Models." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48266.

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Abstract— Artificial Intelligence (AI) models are widely deployed in decision-making systems, but they often exhibit bias due to skewed training data or inherent algorithmic issues. This paper presents a Bias Checker AI Web Application designed to analyze and detect biases in AI-generated outputs. The system uses natural language processing (NLP) and statistical analysis techniques to assess potential biases in text-based predictions. The web-based interface enables [1] real-time bias evaluation, ensuring transparency and fairness in AI systems. The proposed system provides a user-friendly pla

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Drage, Samantha, and Simone Varotto. "Country bias detection in postgraduate student admissions." International Journal of Management Education 8, no. 3 (2010): 95–106. http://dx.doi.org/10.3794/ijme.83.260.

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Wood, Benjamin P., Man Cao, Michael D. Bond, and Dan Grossman. "Instrumentation bias for dynamic data race detection." Proceedings of the ACM on Programming Languages 1, OOPSLA (2017): 1–31. http://dx.doi.org/10.1145/3133893.

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Hurst, Melanie, and Margit Oswald. "Mechanisms underlying response bias in deception detection." Psychology, Crime & Law 18, no. 8 (2012): 759–78. http://dx.doi.org/10.1080/1068316x.2010.550615.

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21

Rollins, Derrick K., Sriram Devanathan, and Ma Victoria B. Bascuñana. "Measurement bias detection in linear dynamic systems." Computers & Chemical Engineering 26, no. 9 (2002): 1201–11. http://dx.doi.org/10.1016/s0098-1354(02)00036-4.

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de Castro, Mário, Heleno Bolfarine, Manuel Galea-Rojas, and Márcio V. de Castilho. "An exact test for analytical bias detection." Analytica Chimica Acta 538, no. 1-2 (2005): 375–81. http://dx.doi.org/10.1016/j.aca.2005.01.060.

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Cheng, Edward K. "Detection and Correction of Case-Publication Bias." Journal of Legal Studies 47, no. 1 (2018): 151–80. http://dx.doi.org/10.1086/696881.

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Sjölander, Arvid, Keith Humphreys, and Juni Palmgren. "On informative detection bias in screening studies." Statistics in Medicine 27, no. 14 (2008): 2635–50. http://dx.doi.org/10.1002/sim.3091.

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Barcot, Ognjen, Svjetlana Dosenovic, Matija Boric, et al. "Assessing risk of bias judgments for blinding of outcome assessors in Cochrane reviews." Journal of Comparative Effectiveness Research 9, no. 8 (2020): 585–93. http://dx.doi.org/10.2217/cer-2019-0181.

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Aim: Adequate judging of risk of bias (RoB) for blinding of outcome assessors (detection bias) is important for supporting highest level of evidence. Materials & methods: Judgments and supporting comments for detection bias were retrieved from RoB tables reported in Cochrane reviews. We categorized comments, and then compared judgment and supporting comment with instructions from the Cochrane Handbook. Results: We analyzed 8656 judgments for detection bias from 7626 trials included in 575 reviews. Overall, 1909 judgments (22%) were not in line with the Cochrane Handbook. In 9% of trials, t

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Tamura, Koichi, Chao Tang, Daichi Ogiura, et al. "Fast and sensitive terahertz detection with a current-driven epitaxial-graphene asymmetric dual-grating-gate field-effect transistor structure." APL Photonics 7, no. 12 (2022): 126101. http://dx.doi.org/10.1063/5.0122305.

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We designed and fabricated an epitaxial-graphene-channel field-effect transistor (EG-FET) featuring an asymmetric dual-grating-gate (ADGG) structure working as a current-driven terahertz detector and experimentally demonstrated a 10 ps-order fast response time and a high responsivity of 0.3 mA/W to 0.95 Terahertz (THz) radiation incidence at room temperature. The ADGG and drain–source bias dependencies of the measured photoresponse showed a clear transition between plasmonic detection under periodic electron density modulation conditions with depleted regions and photothermoelectric (PTE) dete

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Frey, Darren, Eric D. Johnson, and Wim De Neys. "Individual differences in conflict detection during reasoning." Quarterly Journal of Experimental Psychology 71, no. 5 (2018): 1188–208. http://dx.doi.org/10.1080/17470218.2017.1313283.

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Decades of reasoning and decision-making research have established that human judgment is often biased by intuitive heuristics. Recent “error” or bias detection studies have focused on reasoners’ abilities to detect whether their heuristic answer conflicts with logical or probabilistic principles. A key open question is whether there are individual differences in this bias detection efficiency. Here we present three studies in which co-registration of different error detection measures (confidence, response time and confidence response time) allowed us to assess bias detection sensitivity at t

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George, Samuel J., Jeroen M. Stil, and Ben W. Keller. "Detection Thresholds and Bias Correction in Polarized Intensity." Publications of the Astronomical Society of Australia 29, no. 3 (2012): 214–20. http://dx.doi.org/10.1071/as11027.

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AbstractDetection thresholds in polarized intensity and polarization bias correction are investigated for surveys where the polarization information is obtained from rotation measure (RM) synthesis. Considering unresolved sources with a single RM, a detection threshold of 8 σQU applied to the Faraday spectrum will retrieve the RM with a false detection rate less than 10−4, but polarized intensity is more strongly biased than Ricean statistics suggest. For a detection threshold of 5 σQU, the false detection rate increases to ∼4%, depending also on λ2 coverage and the extent of the Faraday spect

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Melluso, Nicola, Sara Pardelli, Gualtiero Fantoni, Filippo Chiarello, and Andrea Bonaccorsi. "DETECTING BAD DESIGN AND BIAS FROM PATENTS." Proceedings of the Design Society 1 (July 27, 2021): 1173–82. http://dx.doi.org/10.1017/pds.2021.117.

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AbstractThe representation of the product use context is a well established design practice in Engineering Design. Recently, design theory is studying the product interaction involving several cognitive aspects such as the possible conditions in which a wrong interaction occurs. The aim of this paper is to find a quantitative evidence of the causes of these misuses. In particular, this study focuses on the detection of bad design and biases.In this paper, we propose a method that helps to the automatic detection of bad design and biases from patents. The method is based on an approach that def

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Rönnback, Ronja, Chris Emmery, and Henry Brighton. "Automatic large-scale political bias detection of news outlets." PLOS One 20, no. 5 (2025): e0321418. https://doi.org/10.1371/journal.pone.0321418.

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Political bias is an inescapable characteristic in news and media reporting, and understanding what political biases people are exposed to when interacting with online news is of crucial import. However, quantifying political bias is problematic. To systematically study the political biases of online news, much of previous research has used human-labelled databases. Yet, these databases tend to be costly, and cover only a few thousand instances at most. Additionally, despite the wide recognition that bias can be expressed in a multitude of ways, many have only examined narrow expressions of bi

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Stephens, Rachel G., John C. Dunn, and Brett K. Hayes. "Belief bias is response bias: Evidence from a two-step signal detection model." Journal of Experimental Psychology: Learning, Memory, and Cognition 45, no. 2 (2019): 320–32. http://dx.doi.org/10.1037/xlm0000587.

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Mevel, Katell, Nicolas Poirel, Sandrine Rossi, et al. "Bias detection: Response confidence evidence for conflict sensitivity in the ratio bias task." Journal of Cognitive Psychology 27, no. 2 (2014): 227–37. http://dx.doi.org/10.1080/20445911.2014.986487.

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Ding, Xueying, Rui Xi, and Leman Akoglu. "Outlier Detection Bias Busted: Understanding Sources of Algorithmic Bias through Data-centric Factors." Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society 7 (October 16, 2024): 384–95. http://dx.doi.org/10.1609/aies.v7i1.31644.

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The astonishing successes of ML have raised growing concern for the fairness of modern methods when deployed in real world settings. However, studies on fairness have mostly focused on supervised ML, while unsupervised outlier detection (OD), with numerous applications in finance, security, etc., have attracted little attention. While a few studies proposed fairness-enhanced OD algorithms, they remain agnostic to the underlying driving mechanisms or sources of unfairness. Even within the supervised ML literature, there exists debate on whether unfairness stems solely from algorithmic biases (i

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Jak, Suzanne, Frans J. Oort, and Conor V. Dolan. "Measurement bias and multidimensionality; an illustration of bias detection in multidimensional measurement models." AStA Advances in Statistical Analysis 94, no. 2 (2010): 129–37. http://dx.doi.org/10.1007/s10182-010-0128-z.

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Lardner, Björn, Amy A. Yackel Adams, Adam J. Knox, Julie A. Savidge, and Robert N. Reed. "Do observer fatigue and taxon bias compromise visual encounter surveys for small vertebrates?" Wildlife Research 46, no. 2 (2019): 127. http://dx.doi.org/10.1071/wr18016.

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Context Visual encounter surveying is a standard animal inventory method, modifications of which (e.g. distance sampling and repeated count surveys) are used for modelling population density. However, a variety of factors may bias visual survey counts. Aims The aim of the present study was to evaluate three observer-related biases: (1) whether fatigue compromises detection rate as a survey occasion progresses; (2) whether long-term fatigue or boredom compromise detection rates over the course of a survey period; and (3) whether observers exhibit biases in detection rates of different animal ta

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Benisha, R. B., and S. Raja Ratna. "Design of Intrusion Detection and Prevention in SCADA System for the Detection of Bias Injection Attacks." Security and Communication Networks 2019 (November 22, 2019): 1–12. http://dx.doi.org/10.1155/2019/1082485.

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Intrusion detection and prevention system detects malicious activities that occur in the real-time SCADA systems. This system has a problem without a profound solution. The challenge of the existing intrusion detection is accuracy in the process of detecting the anomalies. In SCADA, wind turbine data are modified by the intruders and forged details are given to the server. To overcome this, the biased intrusion detection system is used for detecting the intrusion with encrypted date, time, and file location with less false-positive and false-negative rates and thereby preventing the SCADA syst

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BÜYÜKKIDIK, Serap. "Purification procedures used for the detection of gender DIF: Item bias in a foreign language test." International Journal of Assessment Tools in Education 10, no. 4 (2023): 765–80. http://dx.doi.org/10.21449/ijate.1250358.

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Differential item functioning (DIF) detection was handled based on “Mantel-Haenszel (MH)”, “Simultaneous item bias test (SIBTEST)”, “Lord's chi-square”, “Raju's area” methods when item purification was performed or item purification was not performed using real data in current study. After detecting gender-related DIF, expert opinions were taken for bias study. It is important to conduct the gender bias research in the English test when purification is performed and when purification is not performed, as there were DIF studies, but there were not completely similar bias studies in the literatu

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Wiśniowski, Piotr, Maciej Nawrocki, Jerzy Wrona, Susana Cardoso, and Paulo P. Freitas. "Bias Voltage Dependence of Sensing Characteristics in Tunneling Magnetoresistance Sensors." Sensors 21, no. 7 (2021): 2495. http://dx.doi.org/10.3390/s21072495.

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One of the characteristic features of tunneling magnetoresistance (TMR) sensors is a strong influence of bias voltage on tunneling current. Since fundamental sensing characteristics of the sensors are primarily determined by the tunneling current, the bias voltage should impact these characteristics. Previous research has indeed showed the influence of the bias voltage on the magnetic field detection and sensitivity. However, the effect has not been investigated for nonlinearity and hysteresis and the influence of bias voltage polarity has not yet been addressed. Therefore, this paper systemat

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Núñez, Isaac, and Anthony A. Matthews. "Detection bias and the role of negative control outcomes." BMJ Medicine 4, no. 1 (2025): e001336. https://doi.org/10.1136/bmjmed-2025-001336.

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Investigators, patients, or clinicians knowing which treatment is assigned in pragmatic randomised trials and observational analyses can lead to detection bias (ie, systematic differences in determining outcomes between groups). A structural definition of detection bias with directed acyclic graphs is provided, together with several published examples. Why negative control outcomes are best placed to assess detection bias is discussed, and how to correctly select a negative control outcome for this purpose is explained.

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Zhang, Chunxiao. "Exploration, detection, and mitigation: Unveiling gender bias in NLP." Applied and Computational Engineering 52, no. 1 (2024): 62–68. http://dx.doi.org/10.54254/2755-2721/52/20241234.

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Natural Language Processing (NLP) systems have a mundane impact, yet they harbour either obvious or potential gender bias. The automation of decision-making in NLP models even exacerbates unfair treatment. In recent years, researchers have started to notice this issue and have made some approaches to detect and mitigate these biases, yet no consensus on the approaches exists. This paper discusses the interdisciplinary field of linguistics and computer sciences by presenting the most common gender bias categories and breaking them down with ethical and artificial intelligence approaches. Specif

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Ravishankar, Pavan, Qingyu Mo, Edward McFowland III, and Daniel B. Neill. "Provable Detection of Propagating Sampling Bias in Prediction Models." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (2023): 9562–69. http://dx.doi.org/10.1609/aaai.v37i8.26144.

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With an increased focus on incorporating fairness in machine learning models, it becomes imperative not only to assess and mitigate bias at each stage of the machine learning pipeline but also to understand the downstream impacts of bias across stages. Here we consider a general, but realistic, scenario in which a predictive model is learned from (potentially biased) training data, and model predictions are assessed post-hoc for fairness by some auditing method. We provide a theoretical analysis of how a specific form of data bias, differential sampling bias, propagates from the data stage to

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Alldredge, Mathew W., Kenneth H. Pollock, and Theodore R. Simons. "Estimating Detection Probabilities From Multiple-Observer Point Counts." Auk 123, no. 4 (2006): 1172–82. http://dx.doi.org/10.1093/auk/123.4.1172.

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Abstract Point counts are commonly used to obtain indices of bird population abundance. We present an independent-observer point-count method, a generalization of the dependent-observer approach, based on closed-population capture- recapture methods. The approach can incorporate individual covariates, such as detection distance, to account for individual differences in detection probabilities associated with measurable sources of variation. We demonstrate a negative bias in two-observer estimates by comparing abundance estimates from two- and four- observer point counts. Models incorporating d

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Tripathi, Manish, and Raghav Agarwal. "Bias Mitigation in NLP: Automated Detection and Correction." International Journal of Research in Modern Engineering & Emerging Technology 13, no. 5 (2025): 45–60. https://doi.org/10.63345/ijrmeet.org.v13.i5.130503.

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Natural Language Processing (NLP) systems have shown remarkable capabilities, but they often inherit biases from the datasets they are trained on, resulting in outcomes that can be unfair or even harmful. These biases can appear in different forms, such as those related to gender, race, or socioeconomic status. Addressing and mitigating bias in NLP has become a critical area of research, aiming to ensure that machine learning models generate fair and impartial results. This paper delves into the automation of bias detection and correction within NLP systems. It reviews current methods for iden

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Gallaher, Joshua P., Alexander J. Kamrud, and Brett J. Borghetti. "Detection and Mitigation of Inefficient Visual Searching." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 64, no. 1 (2020): 47–51. http://dx.doi.org/10.1177/1071181320641015.

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A commonly known cognitive bias is a confirmation bias: the overweighting of evidence supporting a hy- pothesis and underweighting evidence countering that hypothesis. Due to high-stress and fast-paced opera- tions, military decisions can be affected by confirmation bias. One military decision task prone to confirma- tion bias is a visual search. During a visual search, the operator scans an environment to locate a specific target. If confirmation bias causes the operator to scan the wrong portion of the environment first, the search is inefficient. This study has two primary goals: 1) detect

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Abdel-Hafez, Mamoun F. "A Multihypothesis Sequential Probability Test for Fault Detection and Identification of Vehicles' Ultrasonic Parking Sensors." International Journal of Navigation and Observation 2011 (December 28, 2011): 1–11. http://dx.doi.org/10.1155/2011/137671.

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This paper presents a sequential fault detection and identification algorithm for detecting a fault in a vehicle's ultrasonic parking sensors. The algorithm identifies a bias fault in any of the ultrasonic sensors by computing the probability of having that bias fault given a carefully constructed measurement residual that is only a function of the measurement noise and the possible measurement fault. A set of bias hypotheses is assumed and initially given equal alarm probability. It is assumed that only one sensor will acquire a bias at any given time. Once the probability of a hypothesis app

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Navas-Ara, María J., and Juana Gómez-Benito. "Effects of Ability Scale Purification on the Identification of dif." European Journal of Psychological Assessment 18, no. 1 (2002): 9–15. http://dx.doi.org/10.1027//1015-5759.18.1.9.

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Summary: Research related to the detection of item bias or differential item functioning (dif) has proliferated in psychometric and applied psychological literature over the last 25 years. In fact, debate has been heated on the nature and, more particularly, on the methods for bias/dif detection. Today, conditional methods have obtained wide acceptance. However, these methods present a problem of circularity: If the test contains biased items, then a biased measure of the matching variable will be used for investigating dif. Thus, we investigate dif with a biased measure. The aim of this paper

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Reverter, A., B. L. Golden, R. M. Bourdon, and J. S. Brinks. "Technical note: detection of bias in genetic predictions2." Journal of Animal Science 72, no. 1 (1994): 34–37. http://dx.doi.org/10.2527/1994.72134x.

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Tyrisevä, A. M., E. A. Mäntysaari, J. Jakobsen, et al. "Detection of evaluation bias caused by genomic preselection." Journal of Dairy Science 101, no. 4 (2018): 3155–63. http://dx.doi.org/10.3168/jds.2017-13527.

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Norman, Dara J., and Chris D. Impey. "Quasar-Galaxy Correlations: A Detection of Magnification Bias." Astronomical Journal 121, no. 5 (2001): 2392–404. http://dx.doi.org/10.1086/320408.

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Zheng, Wei, Lisa M. Chung, and Hongyu Zhao. "Bias detection and correction in RNA-Sequencing data." BMC Bioinformatics 12, no. 1 (2011): 290. http://dx.doi.org/10.1186/1471-2105-12-290.

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