Zhang, Angie, Boltz, Alexander, Lynn, Jonathan, Wang, Chun-Wei, and Lee, Min Kyung. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. Retrieved from https://par.nsf.gov/biblio/10463954. Proceedings of the ACM/SIGCHI Conference on Human Factors in Computing Systems (CHI 2023) . Web. doi:10.1145/3544548.3581354.

Zhang, Angie, Boltz, Alexander, Lynn, Jonathan, Wang, Chun-Wei, & Lee, Min Kyung. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. Proceedings of the ACM/SIGCHI Conference on Human Factors in Computing Systems (CHI 2023), (). Retrieved from https://par.nsf.gov/biblio/10463954. https://doi.org/10.1145/3544548.3581354

Zhang, Angie, Boltz, Alexander, Lynn, Jonathan, Wang, Chun-Wei, and Lee, Min Kyung. "Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes". Proceedings of the ACM/SIGCHI Conference on Human Factors in Computing Systems (CHI 2023) (). Country unknown/Code not available. https://doi.org/10.1145/3544548.3581354. https://par.nsf.gov/biblio/10463954.

@article{osti_10463954,
place = {Country unknown/Code not available}, title = {Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes}, url = {https://par.nsf.gov/biblio/10463954}, DOI = {10.1145/3544548.3581354}, abstractNote = {}, journal = {Proceedings of the ACM/SIGCHI Conference on Human Factors in Computing Systems (CHI 2023)}, author = {Zhang, Angie and Boltz, Alexander and Lynn, Jonathan and Wang, Chun-Wei and Lee, Min Kyung}, }