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Relevant bibliographies by topics / Human biases

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Academic literature on the topic 'Human biases'

Author: Grafiati

Published: 4 June 2021

Last updated: 18 February 2022

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Journal articles on the topic "Human biases"

1

Biswas, Mriganka, and John Murray. "Can Cognitive Biases in Robots Make More ‘Likeable' Human-Robot Interactions than the Robots Without Such Biases." International Journal of Artificial Life Research 6, no. 1 (January 2016): 1–29. http://dx.doi.org/10.4018/ijalr.2016010101.

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The research presented in the paper aims to develop long-term companionship between cognitively imperfect robots and humans. In order to develop cognitively imperfect robot, the research suggests to implement various cognitive biases in a robot's interactive behaviours. In the authors' understanding, such cognitively biased behaviours in robot will help the participants to relate with it easily. In the current paper, they show comparative results of the experiments using five biased and one non-biased algorithms in a 3D printed humanoid robot MARC. The results from the experiments show that the participants initially liked the robot with biased and imperfect behaviours than the same robots without any mistakes and biases.

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Sibbel, Rainer, and Angelina Huber. "How Human Decision-making Biases Influence Health Outcomes in Patient Care." European Journal of Management Issues 29, no. 2 (April 28, 2021): 64–72. http://dx.doi.org/10.15421/192106.

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Purpose: Medical treatments and medical decision making are mostly human based and therefore in risk of being influenced by cognitive biases. The potential impact could lead to bad medical outcome, unnecessary harm or even death. The aim of this comprehensive literature study is to analyse the evidence whether healthcare professionals are biased, which biases are most relevant in medicine and how these biases may be reduced. Approach/Findings: The results of the comprehensive literature based meta-analysis confirm on the one hand that several biases are relevant in the medical decision and treatment process. On the other hand, the study shows that the empirical evidence on the impact of cognitive biases on clinical outcome is scarce for most biases and that further research is necessary in this field. Value/Practical Implications: Nevertheless, it is important to determine the extent to which biases in healthcare professionals translate into negative clinical outcomes such as misdiagnosis, delayed diagnosis, or mistreatment. Only this way, the importance of incorporating debiasing strategies into the clinical setting, and which biases to focus on, can be properly assessed. Research Limitations/Future Research: Though recent literature puts great emphasis on cognitive debiasing strategies, there are still very few approaches that have proven to be efficient. Due to the increasing degree of specialization in medicine, the relevance of the different biases varies. Paper type: Theoretical.

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Webster, Matthew T., and Nick G. C. Smith. "Fixation biases affecting human SNPs." Trends in Genetics 20, no. 3 (March 2004): 122–26. http://dx.doi.org/10.1016/j.tig.2004.01.005.

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Thompson, Bill, and Thomas L. Griffiths. "Human biases limit cumulative innovation." Proceedings of the Royal Society B: Biological Sciences 288, no. 1946 (March 10, 2021): 20202752. http://dx.doi.org/10.1098/rspb.2020.2752.

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Is technological advancement constrained by biases in human cognition? People in all societies build on discoveries inherited from previous generations, leading to cumulative innovation. However, biases in human learning and memory may influence the process of knowledge transmission, potentially limiting this process. Here, we show that cumulative innovation in a continuous optimization problem is systematically constrained by human biases. In a large ( n = 1250) behavioural study using a transmission chain design, participants searched for virtual technologies in one of four environments after inheriting a solution from previous generations. Participants converged on worse solutions in environments misaligned with their biases. These results substantiate a mathematical model of cumulative innovation in Bayesian agents, highlighting formal relationships between cultural evolution and distributed stochastic optimization. Our findings provide experimental evidence that human biases can limit the advancement of knowledge in a controlled laboratory setting, reinforcing concerns about bias in creative, scientific and educational contexts.

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Dumbalska, Tsvetomira, Vickie Li, Konstantinos Tsetsos, and Christopher Summerfield. "A map of decoy influence in human multialternative choice." Proceedings of the National Academy of Sciences 117, no. 40 (September 21, 2020): 25169–78. http://dx.doi.org/10.1073/pnas.2005058117.

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Human decisions can be biased by irrelevant information. For example, choices between two preferred alternatives can be swayed by a third option that is inferior or unavailable. Previous work has identified three classic biases, known as the attraction, similarity, and compromise effects, which arise during choices between economic alternatives defined by two attributes. However, the reliability, interrelationship, and computational origin of these three biases have been controversial. Here, a large cohort of human participants made incentive-compatible choices among assets that varied in price and quality. Instead of focusing on the three classic effects, we sampled decoy stimuli exhaustively across bidimensional multiattribute space and constructed a full map of decoy influence on choices between two otherwise preferred target items. Our analysis reveals that the decoy influence map is highly structured even beyond the three classic biases. We identify a very simple model that can fully reproduce the decoy influence map and capture its variability in individual participants. This model reveals that the three decoy effects are not distinct phenomena but are all special cases of a more general principle, by which attribute values are repulsed away from the context provided by rival options. The model helps us understand why the biases are typically correlated across participants and allows us to validate a prediction about their interrelationship. This work helps to clarify the origin of three of the most widely studied biases in human decision-making.

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Silk, Joan B. "Nepotistic cooperation in non-human primate groups." Philosophical Transactions of the Royal Society B: Biological Sciences 364, no. 1533 (November 12, 2009): 3243–54. http://dx.doi.org/10.1098/rstb.2009.0118.

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Darwin was struck by the many similarities between humans and other primates and believed that these similarities were the product of common ancestry. He would be even more impressed by the similarities if he had known what we have learned about primates over the last 50 years. Genetic kinship has emerged as the primary organizing force in the evolution of primate social organization and the patterning of social behaviour in non-human primate groups. There are pronounced nepotistic biases across the primate order, from tiny grey mouse lemurs ( Microcebus murinus ) that forage alone at night but cluster with relatives to sleep during the day, to cooperatively breeding marmosets that rely on closely related helpers to rear their young, rhesus macaque ( Macaca mulatta ) females who acquire their mother's rank and form strict matrilineal dominance hierarchies, male howler monkeys that help their sons maintain access to groups of females and male chimpanzees ( Pan troglodytes ) that form lasting relationships with their brothers. As more evidence of nepotism has accumulated, important questions about the evolutionary processes underlying these kin biases have been raised. Although kin selection predicts that altruism will be biased in favour of relatives, it is difficult to assess whether primates actually conform to predictions derived from Hamilton's rule: br > c . In addition, other mechanisms, including contingent reciprocity and mutualism, could contribute to the nepotistic biases observed in non-human primate groups. There are good reasons to suspect that these processes may complement the effects of kin selection and amplify the extent of nepotistic biases in behaviour.

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Mellem, Monika S., Sophie Wohltjen, Stephen J. Gotts, Avniel Singh Ghuman, and Alex Martin. "Intrinsic frequency biases and profiles across human cortex." Journal of Neurophysiology 118, no. 5 (November 1, 2017): 2853–64. http://dx.doi.org/10.1152/jn.00061.2017.

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Recent findings in monkeys suggest that intrinsic periodic spiking activity in selective cortical areas occurs at timescales that follow a sensory or lower order-to-higher order processing hierarchy (Murray JD, Bernacchia A, Freedman DJ, Romo R, Wallis JD, Cai X, Padoa-Schioppa C, Pasternak T, Seo H, Lee D, Wang XJ. Nat Neurosci 17: 1661–1663, 2014). It has not yet been fully explored if a similar timescale hierarchy is present in humans. Additionally, these measures in the monkey studies have not addressed findings that rhythmic activity within a brain area can occur at multiple frequencies. In this study we investigate in humans if regions may be biased toward particular frequencies of intrinsic activity and if a full cortical mapping still reveals an organization that follows this hierarchy. We examined the spectral power in multiple frequency bands (0.5–150 Hz) from task-independent data using magnetoencephalography (MEG). We compared standardized power across bands to find regional frequency biases. Our results demonstrate a mix of lower and higher frequency biases across sensory and higher order regions. Thus they suggest a more complex cortical organization that does not simply follow this hierarchy. Additionally, some regions do not display a bias for a single band, and a data-driven clustering analysis reveals a regional organization with high standardized power in multiple bands. Specifically, theta and beta are both high in dorsal frontal cortex, whereas delta and gamma are high in ventral frontal cortex and temporal cortex. Occipital and parietal regions are biased more narrowly toward alpha power, and ventral temporal lobe displays specific biases toward gamma. Thus intrinsic rhythmic neural activity displays a regional organization but one that is not necessarily hierarchical. NEW & NOTEWORTHY The organization of rhythmic neural activity is not well understood. Whereas it has been postulated that rhythms are organized in a hierarchical manner across brain regions, our novel analysis allows comparison of full cortical maps across different frequency bands, which demonstrate that the rhythmic organization is more complex. Additionally, data-driven methods show that rhythms of multiple frequencies or timescales occur within a particular region and that this nonhierarchical organization is widespread.

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Cuturi, Luigi F., and Paul R. MacNeilage. "Systematic Biases in Human Heading Estimation." PLoS ONE 8, no. 2 (February 15, 2013): e56862. http://dx.doi.org/10.1371/journal.pone.0056862.

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Maldonado, Antonio, Andrés Catena, José César Perales, and Antonio Cándido. "Cognitive Biases in Human Causal Learning." Spanish Journal of Psychology 10, no. 2 (November 2007): 242–50. http://dx.doi.org/10.1017/s1138741600006508.

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The main aim of this work was to look for cognitive biases in human inference of causal relationships in order to emphasize the psychological processes that modulate causal learning. From the effect of the judgment frequency, this work presents subsequent research on cue competition (overshadowing, blocking, and super-conditioning effects) showing that the strength of prior beliefs and new evidence based upon covariation computation contributes additively to predict causal judgments, whereas the balance between the reliability of both, beliefs and covariation knowledge, modulates their relative weight. New findings also showed “inattentional blindness” for negative or preventative causal relationships but not for positive or generative ones, due to failure in codifying and retrieving the necessary information for its computation. Overall results unveil the need of three hierarchical levels of a whole architecture for human causal learning: the lower one, responsible for codifying the events during the task; the second one, computing the retrieved information; finally, the higher level, integrating this evidence with previous causal knowledge. In summary, whereas current theoretical frameworks on causal inference and decision-making usually focused either on causal beliefs or covariation information, the present work shows how both are required to be able to explain the complexity and flexibility involved in human causal learning.

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Ester, Edward F., Thomas C. Sprague, and John T. Serences. "Categorical Biases in Human Occipitoparietal Cortex." Journal of Neuroscience 40, no. 4 (December 20, 2019): 917–31. http://dx.doi.org/10.1523/jneurosci.2700-19.2019.

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Dissertations / Theses on the topic "Human biases"

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Fendley, Mary E. "Human Cognitive Biases and Heuristics in Image Analysis." Wright State University / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=wright1257278185.

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Barston, Julie Linda. "An investigation into belief biases in reasoning." Thesis, University of Plymouth, 1986. http://hdl.handle.net/10026.1/1906.

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This programme of research investigates the effect of belief bias in syllogistic reasoning. Belief bias is conventionally characterised as a non-logical tendency to accept or reject deductive inferences on the basis of belief rather than logical argument. However, some theorists have argued that the effect is weak compared with that of logic and that it arises from misinterpretation of the premises or failure to accept the logical task. Despite the adoption of controls recommended in the recent literature, Experiments 1 to 3 found consistently strong belief bias effects on the syllogistic evaluation task. However, there were equally strong effects of logic and an interaction between the two factors. Verbal protocol analysis revealed some possible misinterpretation of premises. More strikingly, however, it suggested the presence of three different modes of reasoning which were forward, backward or conclusion based and associated respectively with increasing levels of belief bias. Belief bias was not observed in Experiments 4 and 5 which employed similar problem content on the syllogistic construction task. However, in view of findings recently published by other researchers, it appears that more salient beliefs are needed to produce the effect on this type of task. Experiments 6 to 9 investigated the cause of the logic times belief interaction observed here and in earlier published studies: in essence, the effect of belief is stronger on invalid than valid problems. This could be due to misinterpretation of the logical concept of necessity, but extended instruction on logical interpretation failed to eliminate the effect. The findings were more consistent with a selective scrutiny model of belief bias which claims that arguments supporting unbelievable conclusions are more thoroughly analysed than those supporting believable conclusions. This model is discussed with reference to contemporary theories and findings in the psychology of reasoning.

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Smitherman, Todd Alan. "Information-processing biases toward interoceptive stimuli in claustrophobia." Auburn, Ala., 2006. http://repo.lib.auburn.edu/2006%20Summer/Dissertations/SMITHERMAN_TODD_8.pdf.

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Chan, Ka-yiu Daniel, and 陳嘉堯. "The role of information processing biases in psychosis proneness." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/209539.

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Pathology congruent information-processing biases, the tendency for the information processing system to consistently favour materials whose content corresponds to the pathology’s symptoms and concerns, had been found to be implicated in the aetiology and maintenance of multiple clinical disorders, with ample research established in emotional disorders. Relatively, little work had been done on psychosis, in which research to date on biased interpretation mainly focused on interpretation of auditory hallucinations and the associated distress. The aim of the current study was to evaluate the effects of pathology congruent interpretation bias in paranoia on the prediction of psychosis proneness, the subclinical manifestation of psychosis phenotypes. Cross-sectional data on biased information processing and psychosis proneness were collected from 154 individuals recruited in the normal population via cognitive and self-report measures. A series of hierarchical regression analyses were performed and results suggest that negative information processing bias significantly contributes to the prediction of psychosis proneness. There is also evidence that interpretation bias plays a mediating role in the prediction of psychosis proneness only when the direction of bias was congruent to the pathology’s symptoms and concerns. The discerned mediating role of pathology congruent interpretation bias contributes to the understanding of the mechanisms underlying psychosis proneness. Its clinical implications in terms early identification and target for intervention are further discussed.
published_or_final_version
Clinical Psychology
Master
Master of Social Sciences

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Chen, Xiaoyan. "Cognitive and motivational parameters in motivated biases in human judgment." College Park, Md. : University of Maryland, 2009. http://hdl.handle.net/1903/9913.

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Thesis (Ph.D.) -- University of Maryland, College Park, 2009.
Thesis research directed by: Dept of Psychology. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.

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Turpin, Sibella Margaretha. "The role of information systems in decision-making biases." Diss., University of Pretoria, 2003. http://hdl.handle.net/2263/25720.

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Information systems and in particular decision support systems have been developed to supplement human information processing and to assist with decision-making. Human decision-making is facilitated by the often unconscious use of heuristics or rules of thumb in situations where it may not be possible or feasible to search for the best decision. Judgemental heuristics have previously been found to lead to biases in decision-making. When information systems are used as decision aids, they may have an influence on biases. This study investigates the possible role of information systems in introducing, reinforcing or reducing biases of decision-making. It has been found that information systems have the ability to introduce new biases and to reinforce biases. Information systems can also reduce biases, but this requires innovate thinking on the way information is represented and the way human decision-making processes are supported. It has also been found that in the real world, other than the laboratories where biases are usually measured, other constraints on rational decision-making, such as politics or data errors, can overshadow the effects of biases.
Dissertation (MPhil)--University of Pretoria, 2003.
Informatics
unrestricted

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Kim, Emilie. "Investigation of cultural biases in human moral recall : a computationally grounded study." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/53093.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.
Includes bibliographical references (p. 55-56).
I hypothesize that people are experts in the morals of their culture. By "expert," I mean that people index moral stories not on the basis of superficial features, but rather on the moral itself. Not all moral stories would be indexed this way, but only stories congruent with one's culture. This moral expert hypothesis can be measured by examining how people access stored moral stories during a story recall task. Using the idea that experts show stronger analogical retrieval than novices, I investigate analogical access of culturally-based morals. I describe two pilot experiments (n = 8, n = 11) that use a collection of Eastern and Western moral stories to gather story retrieval data from people of Eastern and Western cultures. The results of these pilot experiments were unexpected. Eastern and Western subjects rate similar and sound story pairs comparably, providing supporting evidence that analogical inference is independent of culture. As hypothesized, Eastern subjects exhibit an expert retrieval effect with Eastern didactic stories (p = 0.10) and a novice pattern of retrieval with Western stories (p = 0.05). However, in contradiction of the hypothesis, Western subjects retrieve Western stories as novices (p = 0.07), which is congruent with previous research, and recall Eastern stories showing a slight expert effect (p = 0.11). The preliminary explanation suggested for these results is based on the differences in moral education in Western and Eastern culture; in Western culture, there is a lack of focused moral education, compared to the strong emphasis placed on didactic learning in Eastern culture.
by Emilie Kim.
M.Eng.

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Kliegr, Tomas. "Effect of cognitive biases on human understanding of rule-based machine learning models." Thesis, Queen Mary, University of London, 2017. http://qmro.qmul.ac.uk/xmlui/handle/123456789/31851.

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This thesis investigates to what extent do cognitive biases a ect human understanding of interpretable machine learning models, in particular of rules discovered from data. Twenty cognitive biases (illusions, e ects) are analysed in detail, including identi cation of possibly e ective debiasing techniques that can be adopted by designers of machine learning algorithms and software. This qualitative research is complemented by multiple experiments aimed to verify, whether, and to what extent, do selected cognitive biases in uence human understanding of actual rule learning results. Two experiments were performed, one focused on eliciting plausibility judgments for pairs of inductively learned rules, second experiment involved replication of the Linda experiment with crowdsourcing and two of its modi cations. Altogether nearly 3.000 human judgments were collected. We obtained empirical evidence for the insensitivity to sample size e ect. There is also limited evidence for the disjunction fallacy, misunderstanding of and , weak evidence e ect and availability heuristic. While there seems no universal approach for eliminating all the identi ed cognitive biases, it follows from our analysis that the e ect of many biases can be ameliorated by making rule-based models more concise. To this end, in the second part of thesis we propose a novel machine learning framework which postprocesses rules on the output of the seminal association rule classi cation algorithm CBA [Liu et al, 1998]. The framework uses original undiscretized numerical attributes to optimize the discovered association rules, re ning the boundaries of literals in the antecedent of the rules produced by CBA. Some rules as well as literals from the rules can consequently be removed, which makes the resulting classi er smaller. Benchmark of our approach on 22 UCI datasets shows average 53% decrease in the total size of the model as measured by the total number of conditions in all rules. Model accuracy remains on the same level as for CBA.

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Nighswander, Tristan. "Behavioral Biases in General Equilibrium: Implications for Wealth Inequality and Human Capital Formation." Thesis, University of Oregon, 2018. http://hdl.handle.net/1794/23811.

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My research focuses on the integration of behavioral economics into well understood general equilibrium macroeconomic models populated by overlapping generations of heterogeneous agents. Specifically, I analyze the implications of populating model economies with present-biased agents who are finitely lived, subject to idiosyncratic labor income shocks, and heterogeneous in both exponential and present-biased discount factors. My primary goal is characterizing the contribution of behavioral biases towards resolving several issues in the literature pertaining to human capital investment and aggregate wealth inequality. Further, the inclusion of present bias in carefully calibrated model economies allows me to rationalize empirical differences in consumption, wealth, and education that arise between observationally similar households that models of homogeneous, exponential discounters are unable to match.

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Albalawi, Tahani F. "Quantifying the Effect of Cognitive Biases on Security Decision-Making." Kent State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=kent1532529752353789.

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Books on the topic "Human biases"

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Hadwin, Julie A. Information processing biases and anxiety: A developmental perspective. Chichester, UK: Wiley-Blackwell, 2010.

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Cognition and communication: Judgmental biases, research methods, and the logic of conversation. Mahwah, N.J: L. Erlbaum Associates, 1996.

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Kumar, Sameer, Tanusree Dutta, and Manas K. Mandal. Bias in human behavior. Hauppauge, N.Y: Nova Science Publisher's, 2012.

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Schifter, Richard. Human rights: A western cultural bias? Washington, D.C: U.S. Dept. of State, Bureau of Public Affairs, 1988.

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Schifter, Richard. Human rights: A western cultural bias? Washington, D.C: U.S. Dept. of State, Bureau of Public Affairs, 1988.

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Schifter, Richard. Human rights: A western cultural bias? Washington, D.C: U.S. Dept. of State, Bureau of Public Affairs, 1988.

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Bias in human reasoning: Causes and consequences. London: Erlbaum, 1989.

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Bhat, P. N. Mari. Fertility decline and gender bias in Northern India. Delhi: Population Research Centre, Institute of Economic Growth, 2001.

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Ehrlich, Isaac. Human capital, endogenous information acquisition, and home bias in financial markets. Cambridge, MA: National Bureau of Economic Research, 2010.

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Vaccine epidemic: How corporate greed, biased science, and coercive government threaten our human rights, our health, and our children. New York: Skyhorse Pub., 2011.

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Book chapters on the topic "Human biases"

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Nuthall, P. L. "Biases and stress." In Farm business management: the human factor, 134–53. Wallingford: CABI, 2019. http://dx.doi.org/10.1079/9781789240733.0134.

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Wall, Emily, Leslie M. Blaha, Celeste Lyn Paul, Kristin Cook, and Alex Endert. "Four Perspectives on Human Bias in Visual Analytics." In Cognitive Biases in Visualizations, 29–42. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95831-6_3.

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Lindgaard, Gitte, Cathy Dudek, and Gerry Chan. "Cultural Congruence and Rating Scale Biases in Homepages." In Human-Computer Interaction – INTERACT 2013, 531–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40498-6_42.

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Baudel, Thomas, Manon Verbockhaven, Victoire Cousergue, Guillaume Roy, and Rida Laarach. "ObjectivAIze: Measuring Performance and Biases in Augmented Business Decision Systems." In Human-Computer Interaction – INTERACT 2021, 300–320. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85613-7_22.

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Bieg, Hans-Joachim, Heinrich H. Bülthoff, and Lewis L. Chuang. "Attentional Biases during Steering Behavior." In Digital Human Modeling and Applications in Health, Safety, Ergonomics, and Risk Management. Healthcare and Safety of the Environment and Transport, 21–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39173-6_3.

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Branley-Bell, Dawn, Rebecca Whitworth, and Lynne Coventry. "User Trust and Understanding of Explainable AI: Exploring Algorithm Visualisations and User Biases." In Human-Computer Interaction. Human Values and Quality of Life, 382–99. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49065-2_27.

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Oaksford, Mike, and Nick Chater. "The Probabilistic Retreat From Biases: Implications for Man-Machine Communication?" In Human-Machine Communication for Educational Systems Design, 47–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-85104-9_6.

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Bendul, Julia C., and Melanie Zahner. "Understanding the Influence of Cognitive Biases in Production Planning and Control." In Human Systems Engineering and Design II, 280–85. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-27928-8_42.

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Berlinger, Silvia, and Friederike Wall. "Effects of Combined Human Decision-Making Biases on Organizational Performance." In Lecture Notes in Computer Science, 27–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38859-0_3.

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Cabitza, Federico. "Biases Affecting Human Decision Making in AI-Supported Second Opinion Settings." In Modeling Decisions for Artificial Intelligence, 283–94. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26773-5_25.

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Conference papers on the topic "Human biases"

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Theocharous, Georgios, Jennifer Healey, Sridhar Mahadevan, and Michele Saad. "Personalizing with Human Cognitive Biases." In Adjunct Publication of the 27th Conference. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3314183.3323453.

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Thurnay, Lőrinc, and Thomas J. Lampoltshammer. "Human biases in government algorithms." In ICEGOV 2020: 13th International Conference on Theory and Practice of Electronic Governance. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3428502.3428529.

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Paruchuri, Praveen, Pradeep Varakantham, Katia Sycara, and Paul Scerri. "Effect of Human Biases on Human-Agent Teams." In 2010 IEEE/ACM International Conference on Web Intelligence-Intelligent Agent Technology (WI-IAT). IEEE, 2010. http://dx.doi.org/10.1109/wi-iat.2010.104.

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Avrahami, Daniel, James Fogarty, and Scott E. Hudson. "Biases in human estimation of interruptibility." In the SIGCHI Conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1240624.1240632.

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Lopez, Christian, Scarlett R. Miller, and Conrad S. Tucker. "Human Validation of Computer vs Human Generated Design Sketches." In ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/detc2018-85698.

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The objective of this work is to explore the perceived visual and functional characteristics of computer generated sketches, compared to human created sketches. In addition, this work explores the possible biases that humans may have towards the perceived functionality of computer generated sketches. Recent advancements in deep generative design methods have allowed designers to implement computational tools to automatically generate large pools of new design ideas. However, if computational tools are to co-create ideas and solutions alongside designers, their ability to generate not only novel but also functional ideas, needs to be explored. Moreover, since decision-makers need to select those creative ideas for further development to ensure innovation, their possible biases towards computer generated ideas need to be explored. In this study, 619 human participants were recruited to analyze the perceived visual and functional characteristics of 50 human created 2D sketches, and 50 2D sketches generated by a deep learning generative model (i.e., computer generated). The results indicate that participants perceived the computer generated sketches as more functional than the human generated sketches. This perceived functionality was not biased by the presence of labels that explicitly presented the sketches as either human or computer generated. Moreover, the results reveal that participants were not able to classify the 2D sketches as human or computer generated with accuracies greater than random chance. The results provide evidence that supports the capabilities of deep learning generative design tools and their potential to assist designers in creative tasks such as ideation.

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Azzopardi, Leif. "Cognitive Biases in Search." In CHIIR '21: ACM SIGIR Conference on Human Information Interaction and Retrieval. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3406522.3446023.

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Shergadwala, Murtuza N., and Jitesh H. Panchal. "Human Inductive Biases in Design Decision Making." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22252.

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Abstract Designers make information acquisition decisions, such as where to search and when to stop the search. Such decisions are typically made sequentially, such that at every search step designers gain information by learning about the design space. However, when designers begin acquiring information, their decisions are primarily based on their prior knowledge. Prior knowledge influences the initial set of assumptions that designers use to learn about the design space. These assumptions are collectively termed as inductive biases. Identifying such biases can help us better understand how designers use their prior knowledge to solve problems in the light of uncertainty. Thus, in this study, we identify inductive biases in humans in sequential information acquisition tasks. To do so, we analyze experimental data from a set of behavioral experiments conducted in the past [1–5]. All of these experiments were designed to study various factors that influence sequential information acquisition behaviors. Across these studies, we identify similar decision making behaviors in the participants in their very first decision to “choose x”. We find that their choices of “x” are not uniformly distributed in the design space. Since such experiments are abstractions of real design scenarios, it implies that further contextualization of such experiments would only increase the influence of these biases. Thus, we highlight the need to study the influence of such biases to better understand designer behaviors. We conclude that in the context of Bayesian modeling of designers’ behaviors, utilizing the identified inductive biases would enable us to better model designer’s priors for design search contexts as compared to using non-informative priors.

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Fine, Alex B., Austin F. Frank, T. Florian Jaeger, and Benjamin Van Durme. "Biases in Predicting the Human Language Model." In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2014. http://dx.doi.org/10.3115/v1/p14-2002.

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Guo, Wei, and Aylin Caliskan. "Detecting Emergent Intersectional Biases: Contextualized Word Embeddings Contain a Distribution of Human-like Biases." In AIES '21: AAAI/ACM Conference on AI, Ethics, and Society. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3461702.3462536.

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Boots, Bryan C. "Proposed Computational Classification System of Human Cognitive Biases." In 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI). IEEE, 2018. http://dx.doi.org/10.1109/wi.2018.00013.

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Reports on the topic "Human biases"

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Solomon, Steven, Michael van Lent, Mark Core, Paul Carpenter, and Milton Rosenberg. A Language for Modeling Cultural Norms, Biases and Stereotypes for Human Behavior Models. Fort Belvoir, VA: Defense Technical Information Center, April 2008. http://dx.doi.org/10.21236/ada480481.

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Lavy, Victor, and Edith Sand. On The Origins of Gender Human Capital Gaps: Short and Long Term Consequences of Teachers’ Stereotypical Biases. Cambridge, MA: National Bureau of Economic Research, January 2015. http://dx.doi.org/10.3386/w20909.

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Dvorkin, Maximiliano A., and Alexander Monge-Naranjo. Occupation Mobility, Human Capital and the Aggregate Consequences of Task-Biased Innovations. Federal Reserve Bank of St. Louis, 2019. http://dx.doi.org/10.20955/wp.2019.013.

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Cowgill, Bo. Bias and Productivity in Humans and Machines. W.E. Upjohn Institute, August 2019. http://dx.doi.org/10.17848/wp19-309.

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Ehrlich, Isaac, Jong Kook Shin, and Yong Yin. Private Information, Human Capital, and Optimal "Home Bias" in Financial Markets. Cambridge, MA: National Bureau of Economic Research, January 2010. http://dx.doi.org/10.3386/w15668.

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Gordon, Eleanor, and Briony Jones. Building Success in Development and Peacebuilding by Caring for Carers: A Guide to Research, Policy and Practice to Ensure Effective, Inclusive and Responsive Interventions. University of Warwick Press, April 2021. http://dx.doi.org/10.31273/978-1-911675-00-6.

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The experiences and marginalisation of international organisation employees with caring responsibilities has a direct negative impact on the type of security and justice being built in conflict-affected environments. This is in large part because international organisations fail to respond to the needs of those with caring responsibilities, which leads to their early departure from the field, and negatively affects their work while in post. In this toolkit we describe this problem, the exacerbating factors, and challenges to overcoming it. We offer a theory of change demonstrating how caring for carers can both improve the working conditions of employees of international organisations as well as the effectiveness, inclusivity and responsiveness of peace and justice interventions. This is important because it raises awareness among employers in the sector of the severity of the problem and its consequences. We also offer a guide for employers for how to take the caring responsibilities of their employees into account when developing human resource policies and practices, designing working conditions and planning interventions. Finally, we underscore the importance of conducting research on the gendered impacts of the marginalisation of employees with caring responsibilities, not least because of the breadth and depth of resultant individual, organisational and sectoral harms. In this regard, we also draw attention to the way in which gender stereotypes and gender biases not only inform and undermine peacebuilding efforts, but also permeate research in this field. Our toolkit is aimed at international organisation employees, employers and human resources personnel, as well as students and scholars of peacebuilding and international development. We see these communities of knowledge and action as overlapping, with insights to be brought to bear as well as challenges to be overcome in this area. The content of the toolkit is equally relevant across these knowledge communities as well as between different specialisms and disciplines. Peacebuilding and development draw in experts from economics, politics, anthropology, sociology and law, to name but a few. The authors of this toolkit have come together from gender studies, political science, and development studies to develop a theory of change informed by interdisciplinary insights. We hope, therefore, that this toolkit will be useful to an inclusive and interdisciplinary set of knowledge communities. Our core argument - that caring for carers benefits the individual, the sectors, and the intended beneficiaries of interventions - is relevant for students, researchers, policy makers and practitioners alike.

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McCarthy, Noel, Eileen Taylor, Martin Maiden, Alison Cody, Melissa Jansen van Rensburg, Margaret Varga, Sophie Hedges, et al. Enhanced molecular-based (MLST/whole genome) surveillance and source attribution of Campylobacter infections in the UK. Food Standards Agency, July 2021. http://dx.doi.org/10.46756/sci.fsa.ksj135.

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This human campylobacteriosis sentinel surveillance project was based at two sites in Oxfordshire and North East England chosen (i) to be representative of the English population on the Office for National Statistics urban-rural classification and (ii) to provide continuity with genetic surveillance started in Oxfordshire in October 2003. Between October 2015 and September 2018 epidemiological questionnaires and genome sequencing of isolates from human cases was accompanied by sampling and genome sequencing of isolates from possible food animal sources. The principal aim was to estimate the contributions of the main sources of human infection and to identify any changes over time. An extension to the project focussed on antimicrobial resistance in study isolates and older archived isolates. These older isolates were from earlier years at the Oxfordshire site and the earliest available coherent set of isolates from the national archive at Public Health England (1997/8). The aim of this additional work was to analyse the emergence of the antimicrobial resistance that is now present among human isolates and to describe and compare antimicrobial resistance in recent food animal isolates. Having identified the presence of bias in population genetic attribution, and that this was not addressed in the published literature, this study developed an approach to adjust for bias in population genetic attribution, and an alternative approach to attribution using sentinel types. Using these approaches the study estimated that approximately 70% of Campylobacter jejuni and just under 50% of C. coli infection in our sample was linked to the chicken source and that this was relatively stable over time. Ruminants were identified as the second most common source for C. jejuni and the most common for C. coli where there was also some evidence for pig as a source although less common than ruminant or chicken. These genomic attributions of themselves make no inference on routes of transmission. However, those infected with isolates genetically typical of chicken origin were substantially more likely to have eaten chicken than those infected with ruminant types. Consumption of lamb’s liver was very strongly associated with infection by a strain genetically typical of a ruminant source. These findings support consumption of these foods as being important in the transmission of these infections and highlight a potentially important role for lamb’s liver consumption as a source of Campylobacter infection. Antimicrobial resistance was predicted from genomic data using a pipeline validated by Public Health England and using BIGSdb software. In C. jejuni this showed a nine-fold increase in resistance to fluoroquinolones from 1997 to 2018. Tetracycline resistance was also common, with higher initial resistance (1997) and less substantial change over time. Resistance to aminoglycosides or macrolides remained low in human cases across all time periods. Among C. jejuni food animal isolates, fluoroquinolone resistance was common among isolates from chicken and substantially less common among ruminants, ducks or pigs. Tetracycline resistance was common across chicken, duck and pig but lower among ruminant origin isolates. In C. coli resistance to all four antimicrobial classes rose from low levels in 1997. The fluoroquinolone rise appears to have levelled off earlier and among animals, levels are high in duck as well as chicken isolates, although based on small sample sizes, macrolide and aminoglycoside resistance, was substantially higher than for C. jejuni among humans and highest among pig origin isolates. Tetracycline resistance is high in isolates from pigs and the very small sample from ducks. Antibiotic use following diagnosis was relatively high (43.4%) among respondents in the human surveillance study. Moreover, it varied substantially across sites and was highest among non-elderly adults compared to older adults or children suggesting opportunities for improved antimicrobial stewardship. The study also found evidence for stable lineages over time across human and source animal species as well as some tighter genomic clusters that may represent outbreaks. The genomic dataset will allow extensive further work beyond the specific goals of the study. This has been made accessible on the web, with access supported by data visualisation tools.

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Saldanha, Ian J., Wangnan Cao, Justin M. Broyles, Gaelen P. Adam, Monika Reddy Bhuma, Shivani Mehta, Laura S. Dominici, Andrea L. Pusic, and Ethan M. Balk. Breast Reconstruction After Mastectomy: A Systematic Review and Meta-Analysis. Agency for Healthcare Research and Quality (AHRQ), July 2021. http://dx.doi.org/10.23970/ahrqepccer245.

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Objectives. This systematic review evaluates breast reconstruction options for women after mastectomy for breast cancer (or breast cancer prophylaxis). We addressed six Key Questions (KQs): (1) implant-based reconstruction (IBR) versus autologous reconstruction (AR), (2) timing of IBR and AR in relation to chemotherapy and radiation therapy, (3) comparisons of implant materials, (4) comparisons of anatomic planes for IBR, (5) use versus nonuse of human acellular dermal matrices (ADMs) during IBR, and (6) comparisons of AR flap types. Data sources and review methods. We searched Medline®, Embase®, Cochrane CENTRAL, CINAHL®, and ClinicalTrials.gov from inception to March 23, 2021, to identify comparative and single group studies. We extracted study data into the Systematic Review Data Repository Plus (SRDR+). We assessed the risk of bias and evaluated the strength of evidence (SoE) using standard methods. The protocol was registered in PROSPERO (registration number CRD42020193183). Results. We found 8 randomized controlled trials, 83 nonrandomized comparative studies, and 69 single group studies. Risk of bias was moderate to high for most studies. KQ1: Compared with IBR, AR is probably associated with clinically better patient satisfaction with breasts and sexual well-being but comparable general quality of life and psychosocial well-being (moderate SoE, all outcomes). AR probably poses a greater risk of deep vein thrombosis or pulmonary embolism (moderate SoE), but IBR probably poses a greater risk of reconstructive failure in the long term (1.5 to 4 years) (moderate SoE) and may pose a greater risk of breast seroma (low SoE). KQ 2: Conducting IBR either before or after radiation therapy may result in comparable physical well-being, psychosocial well-being, sexual well-being, and patient satisfaction with breasts (all low SoE), and probably results in comparable risks of implant failure/loss or need for explant surgery (moderate SoE). We found no evidence addressing timing of IBR or AR in relation to chemotherapy or timing of AR in relation to radiation therapy. KQ 3: Silicone and saline implants may result in clinically comparable patient satisfaction with breasts (low SoE). There is insufficient evidence regarding double lumen implants. KQ 4: Whether the implant is placed in the prepectoral or total submuscular plane may not be associated with risk of infections that are not explicitly implant related (low SoE). There is insufficient evidence addressing the comparisons between prepectoral and partial submuscular and between partial and total submuscular planes. KQ 5: The evidence is inconsistent regarding whether human ADM use during IBR impacts physical well-being, psychosocial well-being, or satisfaction with breasts. However, ADM use probably increases the risk of implant failure/loss or need for explant surgery (moderate SoE) and may increase the risk of infections not explicitly implant related (low SoE). Whether or not ADM is used probably is associated with comparable risks of seroma and unplanned repeat surgeries for revision (moderate SoE for both), and possibly necrosis (low SoE). KQ 6: AR with either transverse rectus abdominis (TRAM) or deep inferior epigastric perforator (DIEP) flaps may result in comparable patient satisfaction with breasts (low SoE), but TRAM flaps probably increase the risk of harms to the area of flap harvest (moderate SoE). AR with either DIEP or latissimus dorsi flaps may result in comparable patient satisfaction with breasts (low SoE), but there is insufficient evidence regarding thromboembolic events and no evidence regarding other surgical complications. Conclusion. Evidence regarding surgical breast reconstruction options is largely insufficient or of only low or moderate SoE. New high-quality research is needed, especially for timing of IBR and AR in relation to chemotherapy and radiation therapy, for comparisons of implant materials, and for comparisons of anatomic planes of implant placement.

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