The ongoing war in Ukraine has severely disrupted the lives of hundreds of thousands of people, significantly impacting university students by displacing many and interrupting their education. To address this, NYU R/AI launched #RAIforUkraine, a fully remote academic research program in partnership with the Ukrainian Catholic University (UCU) in Lviv, Ukraine. In the short term, the program aims to provide a sense of normalcy, and high-quality research opportunities, to students in Ukraine. Its long-term goal is to strengthen Ukraine’s research capacity in responsible AI.
#RAIforUkraine launched in June 2022, and has been running on the academic calendar (September through August) since Fall 2023. The program is open to undergraduate and graduate students who live in Ukraine and are enrolled in degree programs in computer science, information systems and related fields at accredited Ukrainian universities. These students – RAI Research Fellows – are mentored by academic researchers from U.S. and European universities, and conduct cutting-edge collaborative research on a range of responsible AI topics. Students receive academic credit and competitive stipends.
Since the program’s inception, over 50 RAI Research Fellows have participated in the program. They worked with mentors from 10 academic institutions in the U.S., the U.K., France, The Netherlands, Portugal, and Hungary. We plan to enroll the next cohort in September 2024.
For Prospective Research Fellows
Gain research experience: You will work on a RAI research project of your choice, under the guidance of distinguished faculty from universities across the U.S. and Europe, and in close collaboration with their doctoral students or postdocs.
Build your resume: You will receive an affiliation as a Research Fellow at the NYU Center for Responsible AI, enhancing your academic profile with international credibility.
Receive academic credit / stipend: You will receive academic credit towards your degree in the Fall semester; and a competitive stipend if you are selected to continue in the Spring and Summer terms.
Establish global collaborations: You will collaborate with peers from diverse backgrounds, gaining valuable international perspectives and cultural insights.
“This program had a great balance between practical and theory based learning. We spoke a lot about ethics, general fairness, philosophy and sociology. But it was also practical because we were looking at data based on everything we had discussed.”Anastasia Holovenko, 2022 RAI Research Fellow, graduate student at Ukrainian Catholic University
"I met new people and I had never worked in this field and it was new to me. I got to learn what its about and we read others papers. Previously, I only had exposure to Machine Learning research, but this time I got to read papers on sociology which gave me a new perspective because I had no idea about it before. Just learnt a bunch of new things I had never tried or experienced."Andrii Standnik, 2022 RAI Research Fellow, undergraduate student at Ukrainian Catholic University
Fill out this form no later than September 4, 2024, to apply to the program for the Fall 2024 cycle. Should you be selected to proceed to the next stage, we’ll contact you shortly after the deadline to invite you to attend a project open house, and then be matched with a research project that aligns with your interests and skills.
For Prospective Mentors
Support Ukraine: You will support Ukrainian students during critical times, helping maintain and elevate educational standards.
Establish global collaborations: You will engage in cutting-edge research with motivated and talented students, leading to potential co-authored publications. These students will receive academic credit and competitive stipends, with funding provided by NYU R/AI.
Enhance your mentorship skills: You will enhance your mentorship skills by guiding students through collaborative research projects, and receive acknowledgement for your mentorship efforts, bolstering your academic and professional profile.
Participate in cultural exchange: You will gain insights into diverse cultural perspectives, enhancing your global understanding and intercultural skills.
Fill out this form no later than August 31, 2024, to propose a research project. By mentoring RAI Research Fellows, you will play a crucial role in their academic and professional development. We are excited to hear from you!
For Prospective Donors
Support Ukraine: You will leave a lasting impact on the educational landscape in Ukraine by empowering and supporting the next generation of scholars during a critical time.
Build a better future: You will contribute to the development of responsible AI principles and practices that will shape the future of technology and society. Your contribution will help make “responsible AI” synonymous with “AI”.
Strengthen international collaboration: You will support a unique program that bridges cultural and geographical gaps, promoting peace and cooperation between nations. By doing so, you will help create a diverse, international research community, fostering cross-cultural understanding and collaboration.
Click here to support #RAIforUkraine. We greatly appreciate your generosity and commitment to empowering the next generation of Ukrainian scholars!
Selected Publications
Responsible Model Selection with Virny and VirnyView
Denys Herasymuk, Falaah Arif Khan, and Julia Stoyanovich
In Companion of the International Conference on Management of Data, SIGMOD/PODS, Santiago, Chile 2024
@inproceedings{DBLP:conf/sigmod/HerasymukKS24,author={Herasymuk, Denys and Khan, Falaah Arif and Stoyanovich, Julia},editor={Barcel{\'{o}}, Pablo and Pi, Nayat S{\'{a}}nchez and Meliou, Alexandra and Sudarshan, S.},title={Responsible Model Selection with Virny and VirnyView},booktitle={Companion of the International Conference on Management of Data,
{SIGMOD/PODS}, Santiago, Chile},pages={488--491},publisher={{ACM}},year={2024},url={https://doi.org/10.1145/3626246.3654738},doi={10.1145/3626246.3654738},keywords={conference,demo,RAIforUkraine,data},author+an={1=self;2=self;3=self}}
Epistemic Parity: Reproducibility as an Evaluation Metric for Differential Privacy
Lucas Rosenblatt, Bernease Herman, Anastasia Holovenko, Wonkwon Lee, Joshua R. Loftus, Elizabeth McKinnie, Taras Rumezhak, Andrii Stadnik, Bill Howe, and Julia Stoyanovich
@article{DBLP:journals/sigmod/RosenblattHHLLMRSHS24,author={Rosenblatt, Lucas and Herman, Bernease and Holovenko, Anastasia and Lee, Wonkwon and Loftus, Joshua R. and McKinnie, Elizabeth and Rumezhak, Taras and Stadnik, Andrii and Howe, Bill and Stoyanovich, Julia},title={Epistemic Parity: Reproducibility as an Evaluation Metric for Differential
Privacy},journal={{SIGMOD} Rec.},volume={53},number={1},pages={65--74},year={2024},url={https://doi.org/10.1145/3665252.3665267},doi={10.1145/3665252.3665267},keywords={journal,privacy,RAIforUkraine},author+an={1=self;3=self;4=self;6=self;7=self;8=self;10=self}}
The Possibility of Fairness: Revisiting the Impossibility Theorem in Practice
Andrew Bell, Lucius Bynum, Nazarii Drushchak, Tetiana Zakharchenko, Lucas Rosenblatt, and Julia Stoyanovich
In Proceedings of the ACM Conference on Fairness, Accountability, and Transparency, FAccT, Chicago, IL, USA 2023
@inproceedings{DBLP:conf/fat/BellBDZRS23,author={Bell, Andrew and Bynum, Lucius and Drushchak, Nazarii and Zakharchenko, Tetiana and Rosenblatt, Lucas and Stoyanovich, Julia},title={The Possibility of Fairness: Revisiting the Impossibility Theorem
in Practice},booktitle={Proceedings of the {ACM} Conference on Fairness, Accountability,
and Transparency, FAccT, Chicago, IL, USA},pages={400--422},publisher={{ACM}},year={2023},doi={10.1145/3593013.3594007},keywords={conference,fairness,RAIforUkraine},author+an={1=self;2=self;3=self;4=self;5=self;6=self}}
An Interactive Introduction to Causal Inference
Lucius E.J. Bynum, Falaah Arif Khan, Oleksandra Konopatska, Joshua R. Loftus, and Julia Stoyanovich
VISxAI: Workshop on Visualization for AI Explainability 2022
@article{bynum2022interactive,author={Bynum, Lucius E.J. and Khan, Falaah Arif and Konopatska, Oleksandra and Loftus, Joshua R. and Stoyanovich, Julia},title={An Interactive Introduction to Causal Inference},journal={VISxAI: Workshop on Visualization for AI Explainability},year={2022},site={https://r-ai.co/ci-playground},publisher={IEEE},keywords={workshop,education,playground,RAIforUkraine},}
Epistemic Parity: Reproducibility as an Evaluation Metric for Differential Privacy
Lucas Rosenblatt, Bernease Herman, Anastasia Holovenko, Wonkwon Lee, Joshua R. Loftus, Elizabeth Mckinnie, Taras Rumezhak, Andrii Stadnik, Bill Howe, and Julia Stoyanovich
@article{DBLP:journals/pvldb/RosenblattHHLLM23,author={Rosenblatt, Lucas and Herman, Bernease and Holovenko, Anastasia and Lee, Wonkwon and Loftus, Joshua R. and Mckinnie, Elizabeth and Rumezhak, Taras and Stadnik, Andrii and Howe, Bill and Stoyanovich, Julia},title={Epistemic Parity: Reproducibility as an Evaluation Metric for Differential
Privacy},journal={Proc. {VLDB} Endow.},volume={16},number={11},pages={3178--3191},year={2023},doi={10.14778/3611479.3611517},timestamp={Mon, 23 Oct 2023 16:16:16 +0200},biburl={https://dblp.org/rec/journals/pvldb/RosenblattHHLLM23.bib},keywords={journal,privacy,RAIforUkraine},author+an={1=self;3=self;4=self;6=self;7=self;8=self;10=self}}
On Fairness and Stability: Is Estimator Variance a Friend or a Foe?
Falaah Arif Khan, Denys Herasymuk, and Julia Stoyanovich
@article{DBLP:journals/corr/abs-2302-04525,author={Khan, Falaah Arif and Herasymuk, Denys and Stoyanovich, Julia},title={On Fairness and Stability: Is Estimator Variance a Friend or a Foe?},journal={CoRR},volume={abs/2302.04525},year={2023},doi={10.48550/arXiv.2302.04525},eprinttype={arXiv},eprint={2302.04525},keywords={working,fairness,stability,RAIforUkraine},}
An Interactive Introduction to Causal InferenceVISxAI: Workshop on Visualization for AI Explainability 2022
