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Jessie Smith
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2020 – today
- 2024
- [c12]Jessie J. Smith, Aishwarya Satwani, Robin Burke, Casey Fiesler:
Recommend Me? Designing Fairness Metrics with Providers. FAccT 2024: 2389-2399 - 2023
- [c11]Jessie J. Smith, Anas Buhayh, Anushka Kathait, Pradeep Ragothaman, Nicholas Mattei, Robin Burke, Amy Voida:
The Many Faces of Fairness: Exploring the Institutional Logics of Multistakeholder Microlending Recommendation. FAccT 2023: 1652-1663 - [c10]Jessie J. Smith, Blakeley H. Payne, Shamika Klassen, Dylan Thomas Doyle, Casey Fiesler:
Incorporating Ethics in Computing Courses: Barriers, Support, and Perspectives from Educators. SIGCSE (1) 2023: 367-373 - [c9]Jessie J. Smith, Lex Beattie, Henriette Cramer:
Scoping Fairness Objectives and Identifying Fairness Metrics for Recommender Systems: The Practitioners' Perspective. WWW 2023: 3648-3659 - 2022
- [c8]Jessie J. Smith, Saleema Amershi, Solon Barocas, Hanna M. Wallach, Jennifer Wortman Vaughan:
REAL ML: Recognizing, Exploring, and Articulating Limitations of Machine Learning Research. FAccT 2022: 587-597 - [c7]Jessie J. Smith, Lucia Jayne, Robin Burke:
Recommender Systems and Algorithmic Hate. RecSys 2022: 592-597 - [c6]Jessie J. Smith:
Developing a Human-Centered Framework for Transparency in Fairness-Aware Recommender Systems. RecSys 2022: 737-738 - [i8]Jessie J. Smith, Saleema Amershi, Solon Barocas, Hanna M. Wallach, Jennifer Wortman Vaughan:
REAL ML: Recognizing, Exploring, and Articulating Limitations of Machine Learning Research. CoRR abs/2205.08363 (2022) - [i7]Jessie J. Smith, Lucia Jayne, Robin Burke:
Recommender Systems and Algorithmic Hate. CoRR abs/2209.02159 (2022) - [i6]Jessie J. Smith, Lex Beattie:
RecSys Fairness Metrics: Many to Use But Which One To Choose? CoRR abs/2209.04011 (2022) - [i5]Jessie J. Smith, Blakeley H. Payne, Shamika Klassen, Dylan Thomas Doyle, Casey Fiesler:
Incorporating Ethics in Computing Courses: Perspectives from Educators. CoRR abs/2212.06220 (2022) - 2021
- [c5]Casey Fiesler, Mikhaila Friske, Natalie Garrett, Felix Muzny, Jessie J. Smith, Jason Zietz:
Integrating Ethics into Introductory Programming Classes. SIGCSE 2021: 1027-1033 - [c4]Nasim Sonboli, Jessie J. Smith, Florencia Cabral Berenfus, Robin Burke, Casey Fiesler:
Fairness and Transparency in Recommendation: The Users' Perspective. UMAP 2021: 274-279 - [i4]Nasim Sonboli, Jessie J. Smith, Florencia Cabral Berenfus, Robin Burke, Casey Fiesler:
Fairness and Transparency in Recommendation: The Users' Perspective. CoRR abs/2103.08786 (2021) - 2020
- [c3]Masoud Mansoury, Himan Abdollahpouri, Jessie Smith, Arman Dehpanah, Mykola Pechenizkiy, Bamshad Mobasher:
Investigating Potential Factors Associated with Gender Discrimination in Collaborative Recommender Systems. FLAIRS 2020: 193-196 - [c2]Anthony T. Pinter, Jacob M. Paul, Jessie Smith, Jed R. Brubaker:
P4KxSpotify: A Dataset of Pitchfork Music Reviews and Spotify Musical Features. ICWSM 2020: 895-902 - [c1]Vito Walter Anelli, Amra Delic, Gabriele Sottocornola, Jessie Smith, Nazareno Andrade, Luca Belli, Michael M. Bronstein, Akshay Gupta, Sofia Ira Ktena, Alexandre Lung-Yut-Fong, Frank Portman, Alykhan Tejani, Yuanpu Xie, Xiao Zhu, Wenzhe Shi:
RecSys 2020 Challenge Workshop: Engagement Prediction on Twitter's Home Timeline. RecSys 2020: 623-627 - [i3]Masoud Mansoury, Himan Abdollahpouri, Jessie Smith, Arman Dehpanah, Mykola Pechenizkiy, Bamshad Mobasher:
Investigating Potential Factors Associated with Gender Discrimination in Collaborative Recommender Systems. CoRR abs/2002.07786 (2020) - [i2]Jessie Smith, Nasim Sonboli, Casey Fiesler, Robin Burke:
Exploring User Opinions of Fairness in Recommender Systems. CoRR abs/2003.06461 (2020) - [i1]Luca Belli, Sofia Ira Ktena, Alykhan Tejani, Alexandre Lung-Yut-Fong, Frank Portman, Xiao Zhu, Yuanpu Xie, Akshay Gupta, Michael M. Bronstein, Amra Delic, Gabriele Sottocornola, Vito Walter Anelli, Nazareno Andrade, Jessie Smith, Wenzhe Shi:
Privacy-Preserving Recommender Systems Challenge on Twitter's Home Timeline. CoRR abs/2004.13715 (2020)
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last updated on 2024-06-19 21:03 CEST by the dblp team
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