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Addressing repetition in Crowdsourcing: a concept for fast-form entry

Addressing repetition in Crowdsourcing: a concept for fast-form entry
Addressing repetition in Crowdsourcing: a concept for fast-form entry
This workshop paper outlines a conceptual browser plugin that enables crowdworkers to store and later rapidly provide personal information frequently requested in crowdsourcing tasks. Personal data, including demographic data such as age and ethnicity, as well as responses to commonly used personality-related survey instruments, is often critical to collect in crowdsourcing tasks but results in a repetitive experience for crowdworkers. From a requesters perspective, this repetition can result in reduced data quality or the decision to abstain from collecting extensive information on the workers completing a given task. Moreover, given the extensive role of crowdworkers in labelling training data for artificial intelligence applications, ensuring awareness of the workers’ characteristics can help alleviate future biases. In this work, we present the motivation and design requirements for this (hypothetical) plugin and seek input from the community towards its future development.
Berkel, Niels van
281a21e5-5a4c-499b-916f-1bd63898bb4b
Schneiders, Eike
9da80af0-1e27-4454-90e2-eb1abf7108bd
Jacobsen, Rune Møberg
a77fa915-d8c7-4d95-9ac3-72e804737fd9
Berkel, Niels van
281a21e5-5a4c-499b-916f-1bd63898bb4b
Schneiders, Eike
9da80af0-1e27-4454-90e2-eb1abf7108bd
Jacobsen, Rune Møberg
a77fa915-d8c7-4d95-9ac3-72e804737fd9

Berkel, Niels van, Schneiders, Eike and Jacobsen, Rune Møberg (2022) Addressing repetition in Crowdsourcing: a concept for fast-form entry. In Adjunct Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems (CHI’22 EA).

Record type: Conference or Workshop Item (Paper)

Abstract

This workshop paper outlines a conceptual browser plugin that enables crowdworkers to store and later rapidly provide personal information frequently requested in crowdsourcing tasks. Personal data, including demographic data such as age and ethnicity, as well as responses to commonly used personality-related survey instruments, is often critical to collect in crowdsourcing tasks but results in a repetitive experience for crowdworkers. From a requesters perspective, this repetition can result in reduced data quality or the decision to abstain from collecting extensive information on the workers completing a given task. Moreover, given the extensive role of crowdworkers in labelling training data for artificial intelligence applications, ensuring awareness of the workers’ characteristics can help alleviate future biases. In this work, we present the motivation and design requirements for this (hypothetical) plugin and seek input from the community towards its future development.

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More information

Published date: 2 March 2022

Identifiers

Local EPrints ID: 494594
URI: http://eprints.soton.ac.uk/id/eprint/494594
PURE UUID: 76a1a1fe-090e-4dc0-9bd4-f532838e834a
ORCID for Eike Schneiders: ORCID iD orcid.org/0000-0002-8372-1684

Catalogue record

Date deposited: 10 Oct 2024 17:03
Last modified: 11 Oct 2024 02:11

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Contributors

Author: Niels van Berkel
Author: Eike Schneiders ORCID iD
Author: Rune Møberg Jacobsen

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