| Documenting large webtext corpora: A case study on the colossal clean crawled corpus J Dodge, M Sap, A Marasović, W Agnew, G Ilharco, D Groeneveld, ... arXiv preprint arXiv:2104.08758, 2021 | 441 | 2021 |
| The values encoded in machine learning research A Birhane, P Kalluri, D Card, W Agnew, R Dotan, M Bao 2022 ACM Conference on Fairness, Accountability, and Transparency, 173-184, 2022 | 327 | 2022 |
| Evaluating the Social Impact of Generative AI Systems in Systems and Society I Solaiman, Z Talat, W Agnew, L Ahmad, D Baker, SL Blodgett, ... arXiv preprint arXiv:2306.05949, 2023 | 117* | 2023 |
| Robots Enact Malignant Stereotypes A Hundt, W Agnew, V Zeng, S Kacianka, M Gombolay 2022 ACM Conference on Fairness, Accountability, and Transparency, 743-756, 2022 | 56 | 2022 |
| The illusion of artificial inclusion W Agnew, AS Bergman, J Chien, M Díaz, S El-Sayed, J Pittman, ... Proceedings of the CHI Conference on Human Factors in Computing Systems, 1-12, 2024 | 30* | 2024 |
| Queer In AI: A Case Study in Community-Led Participatory AI OO Queerinai, A Ovalle, A Subramonian, A Singh, C Voelcker, ... Proceedings of the 2023 ACM Conference on Fairness, Accountability, and …, 2023 | 30* | 2023 |
| Amodal 3d reconstruction for robotic manipulation via stability and connectivity W Agnew, C Xie, A Walsman, O Murad, Y Wang, P Domingos, S Srinivasa Conference on Robot Learning, 1498-1508, 2021 | 24 | 2021 |
| Representation in AI Evaluations AS Bergman, LA Hendricks, M Rauh, B Wu, W Agnew, M Kunesch, I Duan, ... Proceedings of the 2023 ACM Conference on Fairness, Accountability, and …, 2023 | 18 | 2023 |
| The Surveillance AI Pipeline PR Kalluri, W Agnew, M Cheng, K Owens, L Soldaini, A Birhane arXiv preprint arXiv:2309.15084, 2023 | 12 | 2023 |
| Bound by the Bounty: Collaboratively Shaping Evaluation Processes for Queer AI Harms N Dennler, A Ovalle, A Singh, L Soldaini, A Subramonian, H Tu, W Agnew, ... Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society, 375-386, 2023 | 12 | 2023 |
| Who's in and who's out? A case study of multimodal CLIP-filtering in DataComp R Hong, W Agnew, T Kohno, J Morgenstern arXiv preprint arXiv:2405.08209, 2024 | 5 | 2024 |
| Unsupervised Object-Level Deep Reinforcement Learning W Agnew, P Domingos NeurIPS Workshop on Deep RL, 2018 | 4 | 2018 |
| An ensemble-based recommendation engine for scientific data transfers W Agnew, M Fischer, I Foster, K Chard 2016 Seventh International Workshop on Data-Intensive Computing in the …, 2016 | 3 | 2016 |
| Relevance-Guided Modeling of Object Dynamics for Reinforcement Learning W Agnew, P Domingos arXiv preprint arXiv:2003.01384, 2020 | 2* | 2020 |
| Technologies of Resistance to AI W Agnew, KR McKee, J Kay | 2* | |
| Data Defenses Against Large Language Models W Agnew, HH Jiang, C Sum, M Sap, S Das arXiv preprint arXiv:2410.13138, 2024 | | 2024 |
| Sound Check: Auditing Audio Datasets W Agnew, J Barnett, A Chu, R Hong, M Feffer, R Netzorg, HH Jiang, ... arXiv preprint arXiv:2410.13114, 2024 | | 2024 |
| 'Simulacrum of Stories': Examining Large Language Models as Qualitative Research Participants S Kapania, W Agnew, M Eslami, H Heidari, S Fox arXiv preprint arXiv:2409.19430, 2024 | | 2024 |
| What Can AI Ethics Learn from Anarchism? W Agnew XRDS: Crossroads, The ACM Magazine for Students 30 (4), 22-25, 2024 | | 2024 |
| The Surveillance AI Pipeline P Ria Kalluri, W Agnew, M Cheng, K Owens, L Soldaini, A Birhane arXiv e-prints, arXiv: 2309.15084, 2023 | | 2023 |
