Fetching the paper…
Reading the bibliography…
In hybrid human-machine deferral frameworks, a classifier can defer uncertain cases to human decision-makers (who are often themselves fallible).
Zensors: Adaptive, rapidly deployable, human-intelligent sensor feeds. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems . 1935–1944
Gierad Laput, Walter S Lasecki, Jason Wiese, Robert Xiao, Jeffrey P Bigham, and Chris Harrison. 2015 · 1944
Earlier work this paper cites.
Are Emily and Greg more employable than Lakisha and Jamal? A field experiment on labor market discrimination
Marianne Bertrand and Sendhil Mullainathan. 2004 · 2004
Earlier work this paper cites.
AI gets a brain: New technology allows software to tap real human intelligence
Jeff Barr and Luis Felipe Cabrera. 2006 · 2006
Earlier work this paper cites.
Online linear optimization and adaptive routing
Baruch Awerbuch and Robert Kleinberg. 2008 · 2008
Earlier work this paper cites.
Classifier ensembles: Select real-world applications
Nikunj C Oza and Kagan Tumer. 2008 · 2008
Earlier work this paper cites.
Semi-supervised Learning
Olivier Chapelle, Bernhard Schölkopf, and Alexander Zien. 2010 · 2010
Earlier work this paper cites.
Contextual multi-armed bandits. In Proceedings of the Thirteenth international conference on Artificial Intelligence and Statistics . JMLR Workshop and Conference Proceedings, 485–492
Tyler Lu, Dávid Pál, and Martin Pál. 2010 · 2010
Earlier work this paper cites.
Who should label what? Instance allocation in multiple expert active learning. In Proceedings of the 2011 SIAM International Conference on Data Mining . SIAM, 176–187
Byron C Wallace, Kevin Small, Carla E Brodley, and Thomas A Trikalinos. 2011 · 2011
Earlier work this paper cites.
Active learning from crowds. In International Conference of Machine Learning
Yan Yan, Romer Rosales, Glenn Fung, and Jennifer G Dy. 2011 · 2011
Earlier work this paper cites.
The multiplicative weights update method: A meta-algorithm and applications
Sanjeev Arora, Elad Hazan, and Satyen Kale. 2012 · 2012
Earlier work this paper cites.
Mechanical Turk vs oDesk: My experiences
Panos Ipeirotis. 2012 · 2012
Earlier work this paper cites.
A convex formulation for learning from crowds. In Twenty-Sixth AAAI Conference on Artificial Intelligence
Hiroshi Kajino, Yuta Tsuboi, and Hisashi Kashima. 2012 · 2012
Earlier work this paper cites.
Online machine learning
Óscar Fontenla-Romero, Bertha Guijarro-Berdiñas, David Martinez-Rego, Beatriz Pérez-Sánchez, and Diego Peteiro-Barral. 2013 · 2013
Earlier work this paper cites.
Adaptive Task Assignment for Crowdsourced Classification. In International Conference on Machine Learning . 534–542
Chien-Ju Ho, Shahin Jabbari, and Jennifer Wortman Vaughan. 2013 · 2013
Earlier work this paper cites.
SQUARE: A Benchmark for Research on Computing Crowd Consensus. In Proceedings of the 1st AAAI Conference on Human Computation (HCOMP) . 156–164
Aashish Sheshadri and Matthew Lease. 2013 · 2013
Earlier work this paper cites.
Budget-optimal task allocation for reliable crowdsourcing systems
David R Karger, Sewoong Oh, and Devavrat Shah. 2014 · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
Cited alongside, same era.
GloVe: Global Vectors for Word Representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) . 1532–1543
Jeffrey Pennington, Richard Socher, and Christopher D Manning. 2014 · 2014
Cited alongside, same era.
Combining crowd and expert labels using decision theoretic active learning. In Third AAAI conference on human computation and crowdsourcing
An Thanh Nguyen, Byron C Wallace, and Matthew Lease. 2015 · 2015
Cited alongside, same era.
Self-labeled techniques for semi-supervised learning: taxonomy, software and empirical study
Isaac Triguero, Salvador García, and Francisco Herrera. 2015 · 2015
Cited alongside, same era.
Learning with rejection. In International Conference on Algorithmic Learning Theory . Springer, 67–82
A Tutorial on Thompson Sampling
Daniel J Russo, Benjamin Van Roy, Abbas Kazerouni, Ian Osband, and Zheng Wen. 2018 · 2018
Later among the works it cites.
Enhancing the Accuracy and Fairness of Human Decision Making
Isabel Valera, Adish Singla, and Manuel Gomez Rodriguez. 2018 · 2018
Later among the works it cites.
Racial Bias in Hate Speech and Abusive Language Detection Datasets. In Proceedings of the Workshop on Abusive Language Online
Thomas Davidson, Debasmita Bhattacharya, and Ingmar Weber. 2019 · 2019
Later among the works it cites.
Crowdsourcing with fairness, diversity and budget constraints. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society
Naman Goel and Boi Faltings. 2019 · 2019
Later among the works it cites.
Deep gamblers: Learning to abstain with portfolio theory. In Advances in Neural Information Processing Systems . 10623–10633
Ziyin Liu, Zhikang Wang, Paul Pu Liang, Russ R Salakhutdinov, Louis-Philippe Morency, and Masahito Ueda. 2019 · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Corinna Cortes, Giulia DeSalvo, and Mehryar Mohri. 2016 · 2016
Cited alongside, same era.
Trust for the doctor-in-the-loop
Peter Kieseberg, Edgar Weippl, and Andreas Holzinger. 2016 · 2016
Cited alongside, same era.
A simple but tough-to-beat baseline for sentence embeddings. In ICLR 2017
Sanjeev Arora, Yingyu Liang, and Tengyu Ma. 2017 · 2017
Cited alongside, same era.
A dataset and classifier for recognizing social media English. In Proceedings of the 3rd Workshop on Noisy User-generated Text . 56–61
Su Lin Blodgett, Johnny Wei, and Brendan O’Connor. 2017 · 2017
Cited alongside, same era.
Automated hate speech detection and the problem of offensive language. In Eleventh International AAAI Conference on Web and Social Media
Thomas Davidson, Dana Warmsley, Michael Macy, and Ingmar Weber. 2017 · 2017
Cited alongside, same era.
Noise-tolerant interactive learning using pairwise comparisons
Yichong Xu, Hongyang Zhang, Kyle Miller, Aarti Singh, and Artur Dubrawski. 2017 · 2017
Cited alongside, same era.
Truth inference in crowdsourcing: Is the problem solved?
Yudian Zheng, Guoliang Li, Yuanbing Li, Caihua Shan, and Reynold Cheng. 2017 · 2017
Cited alongside, same era.
A case study of algorithm-assisted decision making in child maltreatment hotline screening decisions. In Conference on Fairness, Accountability and Transparency . 134–148
Alexandra Chouldechova, Diana Benavides-Prado, Oleksandr Fialko, and Rhema Vaithianathan. 2018 · 2018
Cited alongside, same era.
A survey on bias and fairness in machine learning
Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan. 2019 · 2019
Later among the works it cites.
Direct uncertainty prediction for medical second opinions. In International Conference on Machine Learning . 5281–5290
Maithra Raghu, Katy Blumer, Rory Sayres, Ziad Obermeyer, Bobby Kleinberg, Sendhil Mullainathan, and Jon Kleinberg. 2019 · 2019
Later among the works it cites.
The risk of racial bias in hate speech detection. In Proceedings of ACL . 1668–1678
Maarten Sap, Dallas Card, Saadia Gabriel, Yejin Choi, and Noah A Smith. 2019 · 2019
Later among the works it cites.
Algorithmic content moderation: Technical and political challenges in the automation of platform governance
Robert Gorwa, Reuben Binns, and Christian Katzenbach. 2020 · 2020
Later among the works it cites.
Consistent estimators for learning to defer to an expert. In International Conference on Machine Learning . PMLR, 7076–7087
Hussein Mozannar and David Sontag. 2020 · 2020
Later among the works it cites.
We need fairness and explainability in algorithmic hiring. In International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS)
Candice Schumann, Jeffrey Foster, Nicholas Mattei, and John Dickerson. 2020 · 2020
Later among the works it cites.
Matchmaker: Stable Task Assignment With Bounded Constraints for Crowdsourcing Platforms
Xiaoyan Yin, Yanjiao Chen, Cheng Xu, Sijia Yu, and Baochun Li. 2020 · 2020
Later among the works it cites.
Auditing for Diversity using Representative Examples
Vijay Keswani and L Elisa Celis. 2021 · 2021
Later among the works it cites.
Towards Unbiased and Accurate Deferral to Multiple Experts. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society
Vijay Keswani, Matthew Lease, and Krishnaram Kenthapadi. 2021 · 2021
Later among the works it cites.
The adoption of artificial intelligence in employee recruitment: The influence of contextual factors
Yuan Pan, Fabian Froese, Ni Liu, Yunyang Hu, and Maolin Ye. 2021 · 2021
Later among the works it cites.