Quantifying and Avoiding Unfair Qualification Labour in Crowdsourcing

26 May 2021

Papers citing "Quantifying and Avoiding Unfair Qualification Labour in Crowdsourcing"

6 / 6 papers shown

Title
If in a Crowdsourced Data Annotation Pipeline, a GPT-4 Zeyu He Huang Chieh-Yang C. C. Ding Shaurya Rohatgi Ting-Hao 'Kenneth' Huang 33 30 0 26 Feb 2024
Socio-economic landscape of digital transformation & public NLP systems: A critical review Satyam Mohla Anupam Guha 35 1 0 04 Apr 2023
Possible Stories: Evaluating Situated Commonsense Reasoning under Multiple Possible Scenarios Mana Ashida Saku Sugawara 62 6 0 16 Sep 2022
Resolving the Human Subjects Status of Machine Learning's Crowdworkers Divyansh Kaushik Zachary Chase Lipton A. London 25 2 0 08 Jun 2022
What Makes Reading Comprehension Questions Difficult? Saku Sugawara Nikita Nangia Alex Warstadt Sam Bowman ELM RALM 20 13 0 12 Mar 2022
Beyond Fair Pay: Ethical Implications of NLP Crowdsourcing Boaz Shmueli Jan Fell Soumya Ray Lun-Wei Ku 108 86 0 20 Apr 2021