Cost-Effective Quality Assurance in Crowd Labeling
Published Online:9 Feb 2017https://doi.org/10.1287/isre.2016.0661
References
- (2009) Designing intelligent software agents for auctions with limited information feedback. Inform. Systems Res. 20(4):507–526.Link, Google Scholar
- (2010) Optimal windows for aggregating ratings in electronic marketplaces. Management Sci. 56(5):864–880.Link, Google Scholar
- (2011) Deriving the pricing power of product features by mining consumer reviews. Management Sci. 57(8):1485–1509.Link, Google Scholar
- (2012) How to grade a test without knowing the answers—A Bayesian graphical model for adaptive crowdsourcing and aptitude testing. Proc. 29th Internat. Conf. Machine Learning (Omnipress, Madison, WI), 1183–1190.Google Scholar
- (1982) Multiparameter hypothesis testing and acceptance sampling. Technometrics 24(4):295–300.Crossref, Google Scholar
- (2008) Multilevel Bayesian models of categorical data annotation. https://lingpipe.files.wordpress.com/2008/11/carp-bayesian-multilevel-annotation.pdf.Google Scholar
- (2015) Statistical decision making for optimal budget allocation in crowd labeling. J. Machine Learning Res. 16:1–46.Google Scholar
- (2004) A fault threshold policy to manage software development projects. Inform. Systems Res. 15(1):3–21.Link, Google Scholar
- (2014) STEP: A scalable testing and evaluation platform. Second AAAI Conf. Human Comput. Crowdsourcing (AAAI Press, Palo Alto, CA), 41–49.Google Scholar
- (1994) Improving generalization with active learning. Machine Learning 15(2):201–221.Crossref, Google Scholar
- (2002) A survey of convergence results on particle filtering methods for practitioners. IEEE Trans. Signal Processing 50(3):736–746.Crossref, Google Scholar
- (2006) Introduction to Classical and Modern Test Theory (Wadsworth, Belmont, CA).Google Scholar
- (1979) Maximum likelihood estimation of observer error-rates using the EM algorithm. Appl. Statist. 28(1):20–28.Crossref, Google Scholar
- (2010) Item Response Theory (Oxford University Press, Oxford, UK).Crossref, Google Scholar
- (2010) A probabilistic framework to learn from multiple annotators with time-varying accuracy. Proc. 10th SIAM Internat. Conf. Data Mining (SDM) (SIAM, Philadelphia), 826–837.Google Scholar
- (2014) Editor’s comments: Design science research in top information systems journals. MIS Quart. 38(1):iii–viii.Google Scholar
- (2013) Positioning and presenting design science research for maximum impact. MIS Quart. 37(2):337–356.Crossref, Google Scholar
- (2004) Design science in information systems research. MIS Quart. 28(1):75–105.Crossref, Google Scholar
- (2015) Incentivizing high quality crowdwork. Proc. 24th Internat. Conf. World Wide Web (ACM, New York), 419–429.Crossref, Google Scholar
- (2010) Analyzing the Amazon Mechanical Turk marketplace. XRDS: Crossroads, ACM Magazine Students 17(2):16–21.Crossref, Google Scholar
- (2010) Quality management on Amazon Mechanical Turk. Proc. ACM SIGKDD Workshop on Human Comput. (ACM, New York), 64–67.Crossref, Google Scholar
- (2014) Repeated labeling using multiple noisy labelers. Data Mining Knowledge Discovery 28(2):402–441.Crossref, Google Scholar
- (2011) Iterative learning for reliable crowdsourcing systems. Proc. 24th Internat. Neural Inform. Processing Systems (Curran Associates, Red Hook, NY), 1953–1961.Google Scholar
- (2012) Real-time tactical and strategic sales management for intelligent agents guided by economic regimes. Inform. Systems Res. 23(4):1263–1283.Link, Google Scholar
- (2015) Reputation transferability in online labor markets. Management Sci. 62(6):1687–1706.Link, Google Scholar
- (2012) A framework for theory development in design science research: Multiple perspectives. J. Assoc. Inform. Systems 13(6):395–423.Google Scholar
- (1994) A sequential algorithm for training text classifiers. Proc. 17th Annual Internat. ACM SIGIR Conf. Res. Development Inform. Retrieval (Springer, New York), 3–12.Crossref, Google Scholar
- (2003) Budgeted learning of naive-Bayes classifiers. Proc. 19th Conf. Uncertainty Artificial Intelligence (Morgan Kaufmann, San Francisco), 378–385.Google Scholar
- (2009) Harnessing crowds: Mapping the genome of collective intelligence. Working paper, Massachusetts Institute of Technology, Cambridge, http://ssrn.com/abstract=1381502.Google Scholar
- (2008) Design science in the information systems discipline: An introduction to the special issue on design science research. MIS Quart. 32(4):725–730.Crossref, Google Scholar
- (1986) A model of decision-making with sequential information-acquisition (part 1). Decision Support Systems 2(4):285–307.Crossref, Google Scholar
- (1987) A model of decision-making with sequential information-acquisition (part 2). Decision Support Systems 3(1):47–72.Crossref, Google Scholar
- (2014) Doing business with strangers: Reputation in online service marketplaces. Inform. Systems Res. 25(4):865–886.Link, Google Scholar
- (2010) Learning from crowds. J. Machine Learning Res. 11(April):1297–1322.Google Scholar
- (2001) Toward optimal active learning through sampling estimation of error reduction. Proc. 18th Internat. Conf. Machine Learning (Morgan Kaufmann, San Francisco),441–448.Google Scholar
- (2004) Active sampling for class probability estimation and ranking. Machine Learning 54(2):153–178.Crossref, Google Scholar
- (2007) Decision-centric active learning of binary-outcome models. Inform. Systems Res. 18(1):4–22.Link, Google Scholar
- (2009) Active feature-value acquisition. Management Sci. 55(4):664–684.Link, Google Scholar
- (1982) Acceptance Sampling in Quality Control (CRC Press, Boca Raton, FL).Google Scholar
- (2008) Get another label? Improving data quality and data mining using multiple, noisy labelers. Proc. 14th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 614–622.Crossref, Google Scholar
- (2008) Cheap and fast—But is it good? Evaluating non-expert annotations for natural language tasks. Proc. Conf. Empirical Methods Natural Language Processing (Association for Computational Linguistics, Stroudsburg, PA), 254–263.Google Scholar
- (2010) Towards building a high-quality workforce with Mechanical Turk. Proc. NIPS Workshop Comput. Soc. Sci. Wisdom Crowds (Curran Associates, Red Hook, NY), 1–5.Google Scholar
- (2012) Bonus, disclosure, and choice: What motivates the creation of high-quality paid reviews? Proc. 33rd Internat. Conf. Inform. Systems (AIS, Atlanta).Google Scholar
- (2010) Online crowdsourcing: Rating annotators and obtaining cost-effective labels. 2010 IEEE Comput. Soc. Conf. Comput. Vision Pattern Recognition-Workshops (IEEE, New York), 25–32.Crossref, Google Scholar
- (2010) The multidimensional wisdom of crowds. Proc. 23rd Internat. Conf. Neural Inform. Processing Systems (Curran Associates, Red Hook, NY),2424–2432.Google Scholar
- (1975) A review of acceptance sampling schemes with emphasis on the economic aspect. Internat. Statist. Rev. 43(2):191–210.Crossref, Google Scholar
- (2009) Whose vote should count more: Optimal integration of labels from labelers of unknown expertise. Proc. 22nd Internat. Conf. Adv. Neural Inform. Processing Systems (Curran Associates, Red Hook, NY), 2035–2043.Google Scholar
- (2006) Selectively acquiring customer information: A new data acquisition problem and an active learning-based solution. Management Sci. 52(5):697–712.Link, Google Scholar

