Готові списки джерел за темами / Bias

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Добірка наукової літератури з теми "Bias"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 14 вересня 2024

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Статті в журналах з теми "Bias"

Bailey, R. A. "Bias bias." New Scientist 199, no. 2664 (July 2008): 22–23. http://dx.doi.org/10.1016/s0262-4079(08)61727-3.

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Tandoc, Edson C., Bruno Takahashi, and Ryan J. Thomas. "Bias vs. Bias." Journalism Practice 12, no. 7 (July 13, 2017): 834–49. http://dx.doi.org/10.1080/17512786.2017.1343095.

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Raine, Rosalind. "Bias measuring bias." Journal of Health Services Research & Policy 7, no. 1 (January 1, 2002): 65–67. http://dx.doi.org/10.1258/1355819021927584.

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The British National Health Service and other publicly funded health systems operate on the principle that health care should be provided solely on the basis of need. Yet the literature abounds with reports of bias in health care use. In order to defend such a charge, two conditions must be met. The first condition is that treatment decisions must be shown to be unfair in that they are not made solely on the basis of need. This paper demonstrates the importance of considering the fair distribution of health care from two, related, perspectives. The first is that people with equal needs should be treated the same (equal use for equal need). This is referred to as the achievement of horizontal equity. The alternative perspective is that people with greater needs should have more treatment than those with lesser needs (unequal use for unequal need). This is referred to as the achievement of vertical equity. Although these perspectives are logically linked, demonstration of equal use for equal need does not necessarily indicate unequal use for unequal need. This is because it cannot be assumed that equal use occurs at every level of need. The second condition that must be met is that clinical judgement must be shown to be influenced by prejudicial notions about patients. Such research is fraught with methodological difficulties, and the charge of biased clinical decision-making is usually made as a result of a process of exclusion. Methods that could be used to examine the extent to which inequalities in health care use are due to bias are described.

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Brighton, Henry, and Gerd Gigerenzer. "The bias bias." Journal of Business Research 68, no. 8 (August 2015): 1772–84. http://dx.doi.org/10.1016/j.jbusres.2015.01.061.

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Harcum, E. Rae, and Ellen Rosen. "Bias, bias, who doesn't have the bias?" Contemporary Psychology: A Journal of Reviews 40, no. 6 (June 1995): 607. http://dx.doi.org/10.1037/005052.

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Gibson, John P. "Bias in naming bias." Nature Reviews Genetics 3, no. 1 (January 2002): 80. http://dx.doi.org/10.1038/nrg700-c1.

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Farkas, Donka F. "Bias and anti-bias." Approaches to Hungarian 18 2, no. 1 (June 19, 2023): 96–126. http://dx.doi.org/10.1075/jul.00016.far.

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Abstract This paper proposes an account of the interpretive effects of two discourse particles in Hungarian, talán and vajon, within the view of context and context change developed in Farkas & Roelofsen (2017), and shows that the restrictions on their distribution follow from their interpretive properties. Building on Gyuris (2022), talán will be treated as signaling epistemic bias in both declaratives and interrogatives. Following Farkas (2022), vajon will be treated as a non-intrusive question marker, which, in the account proposed, is incompatible with bias markers. The restrictions on the sentence types in which these particles occur, as well as the fact that there are restrictions on their co-occurence, will be derived from their interpretive contribution.

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G.S., Vidya, and Vijaygeetha M. "Bias in Epidemiology." Indian Journal of Preventive Medicine 4, no. 2 (2016): 75–81. http://dx.doi.org/10.21088/ijpm.2321.5917.4216.4.

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Baumgartner, Renate, and Sarah Kuhn. "Bias does not equal bias." TATuP - Zeitschrift für Technikfolgenabschätzung in Theorie und Praxis 30, no. 2 (July 26, 2021): 69–70. http://dx.doi.org/10.14512/tatup.30.2.69.

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Faillie, Jean-Luc. "Indication bias or protopathic bias?" British Journal of Clinical Pharmacology 80, no. 4 (September 16, 2015): 779–80. http://dx.doi.org/10.1111/bcp.12705.

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Дисертації з теми "Bias"

Jayetileke, Harshanie Lakshika. "Bias, bias reduction and implications in predictive regression." Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/210863/1/Harshanie%20Lakshika_Jayetileke_Thesis.pdf.

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Predictive regression models are often used to forecast future possibilities of a given finance variable. For that, we rely on statistical inference: estimation and hypothesis testing. Inaccurate estimation results make inaccurate inference for a scientific question. So, it is important to develop methodologies to reduce the bias in the estimation providing a sounder basis for statistical inference. Hence, the contribution of this research is to deliver more reliable estimators in terms of bias and the level of persistence of the predictor variable, and to develop a corresponding inferential framework with time-series and longitudinal data.

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Gretton, Jeremy David. "Perceived Breadth of Bias as a Determinant of Bias Correction." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1499097376679535.

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Lockard, Andrea. "Examining Organizational Bias." Thesis, Lewis and Clark College, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10744391.

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This study examined a feature of organizations that, prior to this work had not been identified or defined: Organizational Bias. If an organization can learn, have an identity and memory, then an organization can hold bias. Organizational bias occurs when an organization’s culture, as defined and reproduced within its interactions with agents and actors, prevents actors from becoming agents by denying them the power required to change the organizational structure. This exclusionary aspect of bias creates a significant obstacle for educational institutions, many of whom define their missions as serving all students and providing a place that builds, supports, and serves community. Organizational ethnography was used as the methodology to examine organizational bias. Data were collected in a committee space designed to be inclusive of community members and other actor voices as they evaluated new high school course proposals. Data were comprised of the following: observations of all meetings, documents, such as correspondence between members and documents produced as a result of or that informed the committee’s work, and interviews with members. The data were analyzed using a combination of interactional analysis and axial coding. Findings illustrated that patterns of exclusion initiated through the institutional structure of talk constituted and reconstituted organizational bias. This structure created relevant opportunities for resource use (e.g., credit information), which agents were able to perform, but from which actors were excluded, that afforded them the agency to reconstitute the practical and tacit knowing of the organization, which then reproduced the initial structure. Implications for this work include a clearer understanding of how educational organizations hold bias, what patterns of interaction to examine, and how to interrupt the reconstitution of those practices to be more inclusive of actors in an effort to work more closely toward the defined mission.

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Brom-Pierzina, Jane. "Bias in children." Online version, 1999. http://www.uwstout.edu/lib/thesis/1999/1999brompierzinaj.pdf.

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Todd, Jemma Lauren. "Exploring the Role of Attention and Interpretation Biases in Understanding and Treating Pain." Thesis, The University of Sydney, 2017. http://hdl.handle.net/2123/17033.

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Анотація:

The processes that lead to the development and maintenance of chronic pain are still not well understood, however prominent theories and growing empirical research indicate that cognitive processes are likely to be relevant to pain. The aim of this thesis was therefore to investigate the role of attentional bias and interpretation bias in the experience of pain. Chapter 2 presents a meta-analysis of dot-probe studies investigating whether attentional biases exist, and found attentional biases towards sensory pain words for chronic pain patients compared to healthy individuals. Chapter 3 presents a systematic review investigating the clinical relevance of attentional bias to pain through prospective and intervention research. This review found that changes in pain outcomes occur when attentional biases are successfully modified, and that avoidance of affective pain information appears particularly relevant for pain chronicity. This review formed the basis for a new theory, the threat interpretation model, which proposes a specific pattern of attentional bias dependent on threat interpretation. This model was tested experimentally. Chapters 4 and 5 explored the effect of threat on interpretation bias, attentional bias and pain using different paradigms accompanied by eye-tracking. Chapter 6 tested an attentional bias modification (ABM) procedure using a randomised controlled trial design. Together, the results suggest that avoidance of affective pain words predicts pain outcomes and can be modified, however mechanisms of change were not established. Overall, attentional biases appear important for pain; sensory pain biases are most reliably detected although avoidance of affective pain information may be more clinically relevant to pain development and maintenance. The clinical and theoretical implications of this research will be beneficial in advancing this field, so that novel interventions can be developed to improve the experience of pain.

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Hoeffel, Elizabeth Marie. "Gender Bias in Engineering: Does More Contact with Female Engineers Reduce Bias?" Thesis, Virginia Tech, 2007. http://hdl.handle.net/10919/31846.

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Status Characteristics Theory and Contact Theory are tested to measure gender bias in engineering students, and to determine if contact with female engineers helps reduce gender bias. To assess this, two versions of a resume, one with a femaleâ s name and one with a maleâ s name, were given to senior mechanical engineering students (n=225) to establish if they would rate the male applicant better than the female applicant. Respondents were asked how qualified they thought the respondent was, how much they would want the respondent on their team, and whether or not they would hire the applicant. Respondents were also questioned about contact with female engineering faculty, having female engineers in the family, and having female engineering co-workers. Results showed that all of the effects that were expected to occur were not significant, except one. The interaction between having a female engineer in the family and the applicant sex of the resume significantly impacted malesâ desire to have the applicant on their senior design team. Therefore, overall there is very little support for Status Characteristics Theory and Contact Theory. Only one result supports both Status Characteristics Theory and Contact Theory â having a female engineer in the family seems to reduce gender bias toward team members among males.
Master of Science

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梁恆新 and Hang-san Steven Leung. "Gender bias in policing." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2002. http://hub.hku.hk/bib/B42576702.

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Cook, Robert S. "Counselor bias against stepfamilies." Virtual Press, 1996. http://liblink.bsu.edu/uhtbin/catkey/1027107.

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Stepfamilies are the fastest growing family type in America. Unfortunately, our society holds unwarranted negative attitudes about and negative stereotypes of stepfamilies and stepfamily members. Research indicates that stepfamilies are not an inherently dysfunctional or deficient type of family. On the contrary, stepfamilies can be as healthy as nuclear families, and they can produce emotionally healthy individuals. Healthy stepfamilies, however, are often different in the roles of family members and the quality of interactions between members. It is this difference between stepfamily functioning and the normative nuclear family expectations of society that appears to perpetuate negative stereotypes of stepfamilies.Some evidence suggests that counselors hold negative stereotypes of stepfamilies. As s-:ich it is likely that they will diagnose and treat stepfamilies from an inappropriate nuclear family model, perceiving stepfamilies to be more pathological than they are and, in treatment, attempting to fit stepfamilies into roles and relationships inappropriate for healthy stepfamily functioning. No research to date, however, has examined whether counselors' attitudes about stepfamilies affect diagnostic and treatment decisions.This dissertation conducted a national survey of counselor attitudes about stepfamilies. It examined three areas where counselor bias may affect service delivery: judgments regarding stepfamily health, diagnostic decisions, and treatment decisions. It found that counselors appear to generate differential ratings of family health and differential diagnostic impressions on the basis of family interaction style (healthy nuclear family versus healthy stepfamily) and on the basis of family label (nuclear family versus stepfamily). These differential ratings and impressions favor a nuclear family style combined with a nuclear family label in comparison to other family style and label combinations. Additionally, the Parent-Child relationships in a nuclear family that acts like a healthy stepfamily were rated to be more in need of treatment and more important to treatment than in other family styles.The results of this study suggest that experienced counseling psychologists may express bias against stepfamilies and against nuclear families that function outside a traditional nuclear model. They appear to use the traditional nuclear family as a standard against which other family interaction styles and types are found lacking.
Department of Counseling Psychology and Guidance Services

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Hilbert, Anja, Sabrina Baldofski, Markus Zenger, Bernd Löwe, and Elmar Brähler. "Weight Bias Internalization Scale." Universitätsbibliothek Leipzig, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-148164.

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Objective: Internalizing the pervasive weight bias commonly directed towards individuals with overweight and obesity, cooccurs with increased psychopathology and impaired quality of life. This study sought to establish population norms and psychometric properties of the most widely used self-report questionnaire, the Weight Bias Internalization Scale (WBIS), in a representative community sample. Design and Methods: In a survey of the German population, N = 1158 individuals with overweight and obesity were assessed with the WBIS and self-report measures for convergent validation. Results: Item analysis revealed favorable item-total correlation of all but one WBIS item. With this item removed, item homogeneity and internal consistency were excellent. The one-factor structure of the WBIS was confirmed using confirmatory factor analysis. Convergent validity was shown through significant associations with measures of depressive and somatoform symptoms. The WBIS contributed to the explanation of variance in depressive and somatoform symptoms over and above body mass index. Higher WBIS scores were found in women than in men, in individuals with obesity than in individuals with overweight, and in those with lower education or income than those with higher education or income. Sex specific norms were provided. Conclusions: The results showed good psychometric properties of the WBIS after removal of one item. Future research is warranted on further indicators of reliability and validity, for example, retest reliability, sensitivity to change, and prognostic validity.

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Leafhead, Katherine M. "Delusions and attentional bias." Thesis, Durham University, 1997. http://etheses.dur.ac.uk/5007/.

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A research method for investigating delusional beliefs is outlined by adopting the delusional belief that one is dead (the Cotard delusion) as a model delusion. Detailed analyses of published case reports of the Cotard delusion demonstrate that the term 'syndrome' as it is currently applied to the belief that one is dead is not helpful in terms of our understanding of the delusion. Four new case studies of the Cotard delusion suggest that preoccupation with belief may play a role in the formation and maintenance of delusions. Preoccupation with delusional belief was investigated using a variant of the 'emotional' Stroop paradigm, commonly used in investigating anxiety disorders. Three individuals with the Cotard delusion, and diagnosed as suffering from depression, showed attentional biases toward words related to the theme of death. Two of the individuals had no history of anxiety and showed no bias toward words related to generalised anxiety. It was therefore suggested that the locus of attentional biases in delusions may be preoccupation with delusional belief, rather than anxiety per se. Consistent with this, a patient with fixed grandiose delusional beliefs, diagnosed with schizophrenia, and not suffering from anxiety, showed attentional bias toward words related to his delusional beliefs. Attentional bias failed to be demonstrated in a group of people with delusions arising in the context of schizophrenia, and reasons for this are discussed. Finally, three groups of individuals, who were free form any form of psychopathology, each showed a trend towards longer colour-naming times towards words related to their respective interests, but none of these were significant. It is concluded that attentional biases in delusions serve to reinforce delusional beliefs by constantly focusing die individual's attention onto delusion- relevant material. Implications for further research are discussed.

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Книги з теми "Bias"

Kligman, Robert D. Bias. Toronto [Ont.]: Butterworths, 1998.

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Yt, Darmanto. Ditunggui naga: Bias-bias kejiwaan. Jakarta: Pustaka Pembangunan Swadaya Nusantara, 1994.

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Ulrich, Hoffrage, and Pohl Rüdiger, eds. Hindsight bias. Hove: Psychology, 2003.

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Christian, Sue Ellen. Overcoming Bias. 2nd ed. Second edition. | New York, NY: Routledge, 2021.: Routledge, 2021. http://dx.doi.org/10.4324/9780429356179.

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Gower, Kimberly, and Barbara Ritter. Understanding Bias. 2455 Teller Road, Thousand Oaks California 91320: SAGE Publications, Inc., 2021. http://dx.doi.org/10.4135/9781071860250.

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Filimowicz, Michael. Systemic Bias. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003173373.

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Chatfield, Tom, and Tom Chatfield. Overcoming Bias. 2455 Teller Road, Thousand Oaks California 91320: SAGE Publications, Inc., 2022. http://dx.doi.org/10.4135/9781071880999.

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Schupp, Jürgen, and Christof Wolf, eds. Nonresponse Bias. Wiesbaden: Springer Fachmedien Wiesbaden, 2015. http://dx.doi.org/10.1007/978-3-658-10459-7.

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Mullainathan, Sendhil. Media bias. Cambridge, MA: Massachusetts Institute of Technology, Dept. of Economics, 2002.

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Ruschmann, Paul. Media bias. Philadelphia: Chelsea House Publishers, 2006.

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Частини книг з теми "Bias"

Mercier, Hugo. "Confirmation bias – myside bias." In Cognitive Illusions, 78–91. 3rd ed. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003154730-7.

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Berry, Colin, Jason M. Meyer, Marjorie A. Hoy, John B. Heppner, William Tinzaara, Clifford S. Gold, Clifford S. Gold, et al. "Bias." In Encyclopedia of Entomology, 474. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-6359-6_292.

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Fangerau, Heiner. "Bias." In Handbuch Ethik und Recht der Forschung am Menschen, 579–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-35099-3_91.

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Heilbronner, Robert L. "Bias." In Encyclopedia of Clinical Neuropsychology, 567–68. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-57111-9_949.

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Walton, Douglas. "Bias." In Applied Logic Series, 221–54. Dordrecht: Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-015-8632-0_7.

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Wideman, Timothy H., Michael J. L. Sullivan, Shuji Inada, David McIntyre, Masayoshi Kumagai, Naoya Yahagi, J. Rick Turner, et al. "Bias." In Encyclopedia of Behavioral Medicine, 215–16. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1005-9_989.

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Nahler, Gerhard. "bias." In Dictionary of Pharmaceutical Medicine, 14–15. Vienna: Springer Vienna, 2009. http://dx.doi.org/10.1007/978-3-211-89836-9_114.

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Heilbronner, Robert L. "Bias." In Encyclopedia of Clinical Neuropsychology, 1–2. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56782-2_949-2.

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Munro, Paul, Hannu Toivonen, Geoffrey I. Webb, Wray Buntine, Peter Orbanz, Yee Whye Teh, Pascal Poupart, et al. "Bias." In Encyclopedia of Machine Learning, 97. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_72.

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Vitek, Olga. "Bias." In Encyclopedia of Systems Biology, 79. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_1506.

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Тези доповідей конференцій з теми "Bias"

Kim, D., G. U. Baek, and H. J. Lee. "The effects of bias voltage waveforms on self-bias in radio frequency capacitively coupled plasmas The effects of bias voltage waveforms on self-bias in radio frequency capacitively coupled plasmas." In 2024 IEEE International Conference on Plasma Science (ICOPS), 1. IEEE, 2024. http://dx.doi.org/10.1109/icops58192.2024.10625929.

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De-Arteaga, Maria, Alexey Romanov, Hanna Wallach, Jennifer Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Geyik, Krishnaram Kenthapadi, and Adam Tauman Kalai. "Bias in Bios." In FAT* '19: Conference on Fairness, Accountability, and Transparency. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3287560.3287572.

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D'Angelo, P., J. A. Pulido, T. Guardabrazo, P. Vieira, P. Silva, and F. Amarillo. "GNSS Bias Calibration System: GNSS-BICS system prototype." In 2012 6th ESA Workshop on Satellite Navigation Technologies (Navitec 2012) & European Workshop on GNSS Signals and Signal Processing. IEEE, 2012. http://dx.doi.org/10.1109/navitec.2012.6423112.

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"Machine-learning discrimination: bias in, bias out." In 2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS). IEEE, 2019. http://dx.doi.org/10.1109/icicis46948.2019.9014827.

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Nakkas, Alper, Kay Yut Chen, and Jie Zhang. "Aggregation Bias." In Hawaii International Conference on System Sciences. Hawaii International Conference on System Sciences, 2019. http://dx.doi.org/10.24251/hicss.2019.814.

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Sohankar, Javad, Koosha Sadeghi, Ayan Banerjee, and Sandeep K. S. Gupta. "E-BIAS." In MSWiM'15: 18th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2815317.2815341.

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Wilkie, Colin, and Leif Azzopardi. "Algorithmic Bias." In CIKM '17: ACM Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3132847.3133135.

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Hajian, Sara, Francesco Bonchi, and Carlos Castillo. "Algorithmic Bias." In KDD '16: The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2939672.2945386.

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Reyes, Rolando P., Oscar Dieste, Efraín R. Fonseca C., and Natalia Juristo. "Publication Bias." In EASE '20: Evaluation and Assessment in Software Engineering. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3383219.3383233.

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Rohani, Aida Sharif, and Ricardo Baeza-Yates. "Measuring Bias." In 2023 IEEE International Conference on Big Data (BigData). IEEE, 2023. http://dx.doi.org/10.1109/bigdata59044.2023.10386679.

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Звіти організацій з теми "Bias"

Mullainathan, Sendhil, and Andrei Shleifer. Media Bias. Cambridge, MA: National Bureau of Economic Research, October 2002. http://dx.doi.org/10.3386/w9295.

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Velsko, S., and W. Thompson. Modeling Context Bias. Office of Scientific and Technical Information (OSTI), September 2013. http://dx.doi.org/10.2172/1097726.

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Hausman, Jerry. Sources of Bias and Solutions to Bias in the CPI. Cambridge, MA: National Bureau of Economic Research, October 2002. http://dx.doi.org/10.3386/w9298.

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Adam K. Baker, Adam K. Baker. Can we 'de-bias' someone? A Neuroscientific approach to decreasing bias. Experiment, July 2016. http://dx.doi.org/10.18258/7373.

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van Wincoop, Eric, and Francis Warnock. Is Home Bias in Assets Related to Home Bias in Goods? Cambridge, MA: National Bureau of Economic Research, December 2006. http://dx.doi.org/10.3386/w12728.

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Huttenlocher, Janellen, and Larry V. Hedges. Bias in Reporting Location. Fort Belvoir, VA: Defense Technical Information Center, September 1993. http://dx.doi.org/10.21236/ada277403.

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Blanchard, R. J. Sampler bias -- Phase 1. Office of Scientific and Technical Information (OSTI), March 1995. http://dx.doi.org/10.2172/41305.

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Pope, Devin, Joseph Price, and Justin Wolfers. Awareness Reduces Racial Bias. Cambridge, MA: National Bureau of Economic Research, December 2013. http://dx.doi.org/10.3386/w19765.

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Gentzkow, Matthew, and Jesse Shapiro. Media Bias and Reputation. Cambridge, MA: National Bureau of Economic Research, October 2005. http://dx.doi.org/10.3386/w11664.

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Acemoglu, Daron. Equilibrium Bias of Technology. Cambridge, MA: National Bureau of Economic Research, December 2005. http://dx.doi.org/10.3386/w11845.

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