The Daemo crowdsourcing marketplace

Book chapter


Gaikwad, S. S., Whiting, M., Gamage, D., Mullings, C. A., Majeti, D., Goyal, S., Gilbee, A., Chhibber, N., Ginzberg, A., Ballav, A., Matin, A., Richmond-Fuller, A., Sehgal, V., Sarma, T., Nasser, A., Regino, J., Zhou, S., Stolzoff, A., Mananova, K., Dhakal, D., Srinivas, P., Ziulkoski, K., Niranga, S. S., Salih, M., Sinha, A., Vaish, R. and Bernstein, M. S. 2017. The Daemo crowdsourcing marketplace. in: Lee, C.P. and Poltrock, S. (ed.) CSCW '17 Companion: Companion of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing New York Association for Computing Machinery.
AuthorsGaikwad, S. S., Whiting, M., Gamage, D., Mullings, C. A., Majeti, D., Goyal, S., Gilbee, A., Chhibber, N., Ginzberg, A., Ballav, A., Matin, A., Richmond-Fuller, A., Sehgal, V., Sarma, T., Nasser, A., Regino, J., Zhou, S., Stolzoff, A., Mananova, K., Dhakal, D., Srinivas, P., Ziulkoski, K., Niranga, S. S., Salih, M., Sinha, A., Vaish, R. and Bernstein, M. S.
EditorsLee, C.P. and Poltrock, S.
Abstract

The success of crowdsourcing markets is dependent on a
strong foundation of trust between workers and requesters. In current marketplaces, workers and requesters are often unable to trust each other’s quality, and their mental models of tasks are misaligned due to ambiguous instructions or confusing edge cases.

This breakdown of trust typically arises from (1) flawed reputation systems which do not accurately reflect worker and requester quality, and from (2) poorly designed tasks. In this demo, we present how Boomerang and Prototype Tasks, the fundamental building blocks of the Daemo crowdsourcing marketplace, help restore trust between workers and requesters. Daemo’s Boomerang reputation system incentivizes alignment between opinion and ratings by determining the likelihood that workers and requesters will work together in the future based on how they rate each other. Daemo’s Prototype tasks require that new tasks go through a feedback iteration phase with a small number of workers so that requesters can revise their instructions and task designs before launch.

KeywordsCrowdsourcing; Human computation
Year2017
Book titleCSCW '17 Companion: Companion of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing
PublisherAssociation for Computing Machinery
Output statusPublished
File
File Access Level
Open
Place of publicationNew York
ISBN9781450346887
Publication dates
OnlineFeb 2017
Publication process dates
Deposited16 Nov 2020
Digital Object Identifier (DOI)https://doi.org/10.1145/3022198.3023270
Official URLhttps://doi.org/10.1145/3022198.3023270
Related URLhttps://dl.acm.org/doi/proceedings/10.1145/3022198
Related Output
Cites9781450346887
Additional information

Stanford Crowd Research Collective
Stanford University
daemo@cs.stanford.edu

EventThe 20th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2017)
Web address (URL) of conference proceedingshttp://cscw.acm.org/2017/program/program_content/Companion.html
Permalink -

https://repository.canterbury.ac.uk/item/8wqv2/the-daemo-crowdsourcing-marketplace

Download files

  • 64
    total views
  • 71
    total downloads
  • 0
    views this month
  • 1
    downloads this month

Export as

Related outputs

The role of use cases when adopting augmented reality into higher education pedagogy
Ward, G., Turner, S., Pitt, C., Qi, M., Richmond-Fuller, A. and Jackson, T. 2024. The role of use cases when adopting augmented reality into higher education pedagogy.
Adaptive and flexible online learning during Covid19 lockdown
Manna, S., Nortcliffe, A., Sheikholeslami, G. and Richmond-Fuller, A. 2021. Adaptive and flexible online learning during Covid19 lockdown.
Together apart: nurturing inclusive, accessible and diverse connections within the Canterbury Christ Church University (CCCU) community during COVID-19
Richmond-Fuller, A. 2020. Together apart: nurturing inclusive, accessible and diverse connections within the Canterbury Christ Church University (CCCU) community during COVID-19.
Prototype tasks: Improving crowdsourcing results through rapid, iterative task design
Gaikwad, S.S., Chhibber, N., Sehgal, V., Ballav, A., Mullings, C., Nasser, A., Richmond-Fuller, A., Gilbee, A., Gamage, D., Whiting, M., Zhou, S., Matin, S., Niranga, S., Goyal, S., Majeti, M., Srinivas, P., Ginzberg, A., Mananova, K., Ziulkoski, K., Regino, J., Sarma, S., Sinha, A., Paul, A., Diemer, C., Murag, M., Dai, W., Pandey, M., Vaish, R. and Bernstein, M. 2017. Prototype tasks: Improving crowdsourcing results through rapid, iterative task design.
Crowd guilds: Worker-led reputation and feedback on crowdsourcing platforms
Whiting, M . E., Gamage, D., Gaikwad, S. S., Gilbee, A., Goyal, S., Ballav, A., Majeti, D., Chhibber, N., Richmond-Fuller, A., Vargus, F., Sharma, T. S., Chandrakanthan, V., Moura, T., Salih, M. H., Kalejaiye, G. B. T., Ginzberg, A., Mullings, C. A., Dayan, Y., Milland, K., Orefice, H., Regino, J., Parsi, S., Mainali, K., Sehgal, V., Matin, S., Sinha, A., Vaish, R. and Bernstein, M. S. 2017. Crowd guilds: Worker-led reputation and feedback on crowdsourcing platforms. in: CSCW '17: Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing New York Association for Computing Machinery. pp. 1902-1913
Boomerang: Rebounding the consequences of reputation feedback on crowdsourcing platforms
Gaikwad, N.S., Morina, D., Ginzberg, A., Mullings, C., Goyal, S., Gamage, D., Diemert, C., Burton, M., Zhou, S., Whiting, M., Ziulkoski, K., Gilbee, A., Niranga, S. S., Sehgal, V., Lin, J., Kristianto, L., Richmond-Fuller, A., Regino, J., Chhibber, N., Majeti, D., Sharma, S., Mananova, K., Dhakal, D., Dai, W., Purynova, V., Sandeep, S., Chandrakanthan, V., Sarma, T., Matin, S., Nasser, A., Nistala, R., Stolzoff, A., Milland, K., Mathur, V., Vaish, R. and Bernstein, M. S. 2016. Boomerang: Rebounding the consequences of reputation feedback on crowdsourcing platforms. in: Rekimoto, J. and Igarashi, T. (ed.) UIST '16: Proceedings of the 29th Annual Symposium on User Interface Software and Technology New York Association for Computing Machinery. pp. 625-637