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Bayesian Bias Mitigation for Crowdsourcing
2011
Neural Information Processing Systems
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We present Bayesian Bias Mitigation for Crowdsourcing (BBMC), a Bayesian model to unify all three. ...
Biased labelers are a systemic problem in crowdsourcing, and a comprehensive toolbox for handling their responses is still being developed. ...
Acknowledgements We would like to thank Purnamrita Sarkar for helpful discussions and Dave Golland for assistance in developing the Amazon Mechanical Turk HITs. ...
dblp:conf/nips/WauthierJ11
fatcat:457747co7feafe4p6yas7w6nde
A Checklist to Combat Cognitive Biases in Crowdsourcing
2021
Proceedings of the AAAI Conference on Human Computation and Crowdsourcing
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Recent research has demonstrated that cognitive biases such as the confirmation bias or the anchoring effect can negatively affect the quality of crowdsourced data. ...
In practice, however, such biases go unnoticed unless specifically assessed or controlled for. ...
Acknowledgments This activity is financed by IBM and the Allowance for Top Consortia for Knowledge and Innovation (TKI's) of the Dutch ministry of economic affairs. ...
doi:10.1609/hcomp.v9i1.18939
fatcat:njywzvxcvjfjvejjlwy3tz2e2i
Co-destruction Patterns in Crowdsourcing
[chapter]
2020
Lecture Notes in Computer Science
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Crowdsourcing has been a successful paradigm in organising a large number of actors to work on specific tasks and contribute to knowledge collectively. ...
This collection of so-called co-destruction patterns allows for an-depth analysis of corwdsourcing systems which can benefit a comparative analysis and also assist with improvements of existing systems ...
The patterns can be used for benchmarking purposes, for the purpose of selecting, configuring or even developing a crowdsourcing system, and for the development of new detection and mitigation methods ...
doi:10.1007/978-3-030-49435-3_4
fatcat:rhx62wls5fem7oetwcns2mtkyq
Language Understanding in the Wild
2015
Proceedings of the 24th International Conference on World Wide Web - WWW '15
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To overcome this problem, we present a novel Bayesian approach to language understanding that relies on aggregated crowdsourced judgements. ...
However, the subjectivity and bias of human interpreters raise challenges in inferring the semantics expressed by the text. ...
A key challenge in such crowdsourcing applications is to mitigate the bias of subjective labellers. ...
doi:10.1145/2736277.2741689
dblp:conf/www/SimpsonVRKGRJ15
fatcat:hsrmqa4zw5f5lngrecrhodixkq
Assessing the Effect of Visualizations on Bayesian Reasoning through Crowdsourcing
2012
IEEE Transactions on Visualization and Computer Graphics
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In this study, a textual and six visual representations for three classic problems were compared using a diverse subject pool through crowdsourcing. ...
We discuss our findings and the need for more such experiments to be carried out on heterogeneous populations of non-experts. ...
ACKNOWLEDGMENTS We thank Yvonne Jansen for her useful feedback on this paper. ...
doi:10.1109/tvcg.2012.199
pmid:26357162
fatcat:yrscxdysbrdmhabpdwekwk7qum
Evaluating Complex Task through Crowdsourcing: Multiple Views Approach
[article]
2017
arXiv
pre-print
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Bias pattern determines how the behavior is biased among graders, which is detected by a statistical technique. The proposed approach is analyzed on a synthetic data set. ...
However, for getting grades for complex tasks, which require specific skills and efforts for grading, crowdsourcing encounters a restriction of insufficient knowledge of the workers from the crowd. ...
In [26] , a Bayesian model, named as Bayesian Bias Mitigation for Crowdsourcing (BBMC), is proposed to capture the sources of bias. ...
arXiv:1703.10579v1
fatcat:gee2kdthizbnvhokbywziucf4u
A Trust-based Mixture of Gaussian Processes Model for Reliable Regression in Participatory Sensing
2017
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
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We develop a Markov chain Monte Carlo (MCMC)-based algorithm to efficiently perform Bayesian inference of the model. ...
We propose a novel trust-based mixture of Gaussian processes (GP) model for spatial regression to jointly detect such misbehavior and accurately estimate the spatial field. ...
Acknowledgements We wish to acknowledge the funding for this project from Nanyang Technological University under the Undergraduate Research Experience on CAmpus (URECA) programme. ...
doi:10.24963/ijcai.2017/540
dblp:conf/ijcai/XiangZNZ17
fatcat:2zrwckucjbbcdagma4zlo5h5ba
Let's Agree to Disagree: Fixing Agreement Measures for Crowdsourcing
2017
AAAI Conference on Human Computation & Crowdsourcing
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While many measures of agreement between annotators have been proposed, they are known for suffering from many problems and abnormalities. ...
In the context of micro-task crowdsourcing, each task is usually performed by several workers. ...
We want to thank the Erasmus+ traineeships program for facilitating collaborations, and the European Science Foundation for funding the Science Meeting SM 5917 under the ELIAS Research Networking Programme ...
dblp:conf/hcomp/CheccoRMMD17
fatcat:dmin3vwxf5bbfe2kdw4acl2fam
Mitigating Bias in Algorithmic Systems – A Fish-Eye View
[article]
2022
arXiv
pre-print
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2021 pre-print arXiv:2103.16953v1 fatcat:4mqsx2lwbjclvez6uqj2fz3tya
Mitigating bias in algorithmic systems is a critical issue drawing attention across communities within the information and computer sciences. ...
Given the complexity of the problem and the involvement of multiple stakeholders -- including developers, end-users, and third parties -- there is a need to understand the landscape of the sources of bias ...
bias mitigation. ...
arXiv:2103.16953v2
fatcat:b27zb3zusnfmzcspyl2njbivkq
Taken By Surprise? Evaluating how Bayesian Weighting Influences Peoples' Takeaways in Map Visualizations
[article]
2023
arXiv
pre-print
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Choropleth maps have been studied and extended in many ways to counteract the many biases that can occur when using them. ...
We report a crowdsourced experiment where n = 300 participants are assigned to one of Choropleth, Surprise (only), and VSUP conditions (depicting rates and Surprise in a suppressed palette). ...
To mitigate biases for particular dataset contexts discovered in pilot studies (e.g. vaccine skepticism) we reframe both datasets as a sales and marketing task. ...
arXiv:2307.15138v1
fatcat:5tkvq6honjcy5mvmvvnywwyhci
A Bayesian Best-Worst Method-Based Multicriteria Competence Analysis of Crowdsourcing Delivery Personnel
2020
Complexity
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Crowdsourcing delivery is becoming a prevalent tool for tackling delivery problems by building a large labor-intensive service network. ...
of a crowdsourcing platform. ...
Majid Mohammadi for his help in the process of revising this paper. ey would like to thank Miss Fang Li for her help in data collection, the managers for answering questionnaires, and the crowdsourcing ...
doi:10.1155/2020/4250417
fatcat:acnoofi4nnfoxnjnpo2ygxznqu
Low Resource Sequence Tagging with Weak Labels
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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In this paper, we propose a domain adaptation method using Bayesian sequence combination to exploit pre-trained models and unreliable crowdsourced data that does not require high resource data in a different ...
Current methods for sequence tagging depend on large quantities of domain-specific training data, limiting their use in new, user-defined tasks with few or no annotations. ...
Zhou et al. (2019) tackle this problem through adversarial training to mitigate overfitting on the target domain. ...
doi:10.1609/aaai.v34i05.6415
fatcat:nlsxinfntjcwjmgj3eaul4q334
Trustworthy Human Computation: A Survey
[article]
2022
arXiv
pre-print
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This survey lays the groundwork for the realization of trustworthy human computation. ...
Finally, future challenges and research directions for realizing trustworthy human computation are discussed. ...
By making people aware of possible biases, they are effectively mitigated. On the other hand, Duan et al. ...
arXiv:2210.12324v1
fatcat:fe53lwjvcjbqdaqjxaixjrjsoe
Competing Models: Inferring Exploration Patterns and Information Relevance via Bayesian Model Selection
[article]
2020
arXiv
pre-print
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Our results indicate that depending on the application, our method outperforms established baselines for bias detection and future interaction prediction. ...
In this paper, we construct a series of models based on the dataset and pose user exploration modeling as a Bayesian model selection problem where we maintain a belief over numerous competing models that ...
Emily Wall for her conversation on the bias detection metric. This material is based upon work supported by the National Science Foundation under grant numbers 1755734, 1845434, and 1940224. ...
arXiv:2009.06042v1
fatcat:dyzg3n7fpvdkpgkwcj2isz7y6a
A Bayesian Perspective on Theory-Blind Data Collection
2021
Qualitative & Multi-Method Research
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Bayesian reasoning provides its own safeguards against the problems of confirmation bias and ad hoc hypothesizing, without imposing procedural constraints that would interfere with the inherently iterative ...
essence entails placing a firewall between data collection and hypothesis testing1—is an interesting addition to a growing list of proposals made in recent years that aim to address potential sources of bias ...
Second, adjusting publication norms regarding requisite levels of confidence in findings would mitigate incentives for falsely bolstering results. ...
doi:10.5281/zenodo.4046614
fatcat:kny4q4uqaneqnd5eavjakkku64
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