Boomerang: Rebounding the consequences of reputation feedback on crowdsourcing platforms

Book chapter


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
AuthorsGaikwad, 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.
EditorsRekimoto, J. and Igarashi, T.
Abstract

Paid crowdsourcing platforms suffer from low-quality workand unfair rejections, but paradoxically, most workers and requesters have high reputation scores. These inflated scores, which make high-quality work and workers difficult to find,stem from social pressure to avoid giving negative feedback. We introduce Boomerang, a reputation system for crowdsourcing that elicits more accurate feedback by rebounding the consequences of feedback directly back onto the person who gave it. With Boomerang, requesters find that their highly rated workers gain earliest access to their future tasks, and workers find tasks from their highly-rated requesters at the top of their task feed. Field experiments verify that Boomerang causes both workers and requesters to provide feedback that is more closely aligned with their private opinions. Inspired by a game-theoretic notion of incentive-compatibility, Boomerang opens opportunities for interaction design to incentivize honest reporting over strategic dishonesty.

KeywordsReputation systems; Crowdsourcing platforms; Human computation; Game theory
Page range625-637
Year2016
Book titleUIST '16: Proceedings of the 29th Annual Symposium on User Interface Software and Technology
PublisherAssociation for Computing Machinery
Output statusPublished
File
File Access Level
Open
Place of publicationNew York
ISBN9781450341899
Publication dates
Online16 Oct 2016
Publication process dates
Deposited16 Nov 2020
Official URLhttps://doi.org/10.1145/2984511.2984542
Related URLhttps://hci.stanford.edu/publications/2016/boomerang/boomerang-uist.pdf
https://acm-prod-cdn.literatumonline.com/2984511.2984542/f734fea8-0a93-4038-af71-58616e379467/uist2629-file4.mp4?b92b4ad1b4f274c70877518315abb28be831d54738a81f1de54388f7ef0eefe70a912b90dead24367f6dc4fb01df839b53f592f0801a213d68e8ae0bfa7b31ec099fd82d684e186b7f05ff6f251952903d9fa8fa38e7bbdd4dcaa65a7c68d24faa0732f849
https://www.youtube.com/watch?v=C6L7J2DUFz8
https://www.youtube.com/watch?v=8tX1XNq96pQ
http://st.sigchi.org/publications/toc/uist-2016.html
https://uist.acm.org/uist2016/
FunderNational Science Foundation
Stanford Cyber-Social Systems Grant
References

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Additional information

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

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