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Welcome to the Center for Responsible AI at New York University
Welcome to the Center for Responsible AI at New York University
What is responsible AI? We use this term to refer to making the design, development and use of AI socially sustainable: using technology for good while controlling the risks. Responsible AI is about respecting human values, ensuring fairness, maintaining transparency, and upholding accountability. It’s about taking hype and magical thinking out of the conversation about AI. And about giving people the ability to understand, control and take responsibility for AI-assisted decisions.
News
Sep 26, 2025
Launch of the New York AI Exchange, our ambitious new initiative bringing together education, research, policy and practice to strengthen New York’s AI ecosystem. Learn more here.
Sep 3, 2025
The #RAIforUkraine program kicked off Fall 2025 with a record number of applicants: 40 new Research Fellows selected from nearly 90 submissions and 21 new mentors, alongside many returning colleagues representing 12 countries. Watch the Open House recording here.
Applications for the #RAIforUkraine Fall 2025 Cohort are NOW open! Are you a prospective student or mentor? Click here to learn more and get involved.
Jul 28, 2025
NYU Tandon Profs. Julia Stoyanovich and Ludovic Righetti receive funding from the NSF’s Ethical and Responsible Research (ER2) program for a new project.
Learn more here.
May 21, 2025
On May 21, Julia Stoyanovich delivered an AI 101 presentation to New York State legislators and staff as part of New York State’s AI Week.
On May 1, Julia Stoyanovich delivered the CISE Distinguished Lecture, titled “Follow the Data! Responsible AI Starts with Responsible Data Management,” hosted by the NSF.
Apr 8, 2025
On April 8, Julia Stoyanovich testified before the U.S. House of Representatives at a Research & Technology Subcommittee Hearing of the Committee on Science, Space and Technology, titled “DeepSeek: A Deep Dive.”
Feb 28, 2025
On February 28, Julia Stoyanovich led a session on AI’s fundamentals and ethical implications at a high level U.N. workshop on AI and International Humanitarian Law (IHL)–a gathering of diplomats, U.N. representatives, and legal experts shaping global discussions on AI’s role in warfare.
Selected Publications
Estimating the impact of the Russian invasion on the displacement of graduating high school students in Ukraine
Tetiana Zakharchenko, Andrew Bell, Nazarii Drushchak, Oleksandra Konopatska, Falaah Arif Khan, and Julia Stoyanovich
@article{ukraine_edu25,title={Estimating the impact of the {Russian} invasion on the displacement of graduating high school students in {Ukraine}},author={Zakharchenko, Tetiana and Bell, Andrew and Drushchak, Nazarii and Konopatska, Oleksandra and Khan, Falaah Arif and Stoyanovich, Julia},year={2025},journal={Nature Communications},keywords={journal,education,policy,RAIforUkraine},}
ShaRP: Explaining Rankings and Preferences with Shapley Values
Venetia Pliatsika, João Fonseca, Kateryna Akhynko, Ivan Shevchenko, and Julia Stoyanovich
@article{sharp,author={Pliatsika, Venetia and Fonseca, Jo{\~{a}}o and Akhynko, Kateryna and Shevchenko, Ivan and Stoyanovich, Julia},title={{ShaRP}: Explaining Rankings and Preferences with Shapley Values},journal={Proc. {VLDB} Endow.},volume={18},number={11},year={2025},doi={10.14778/3749646.3749682},keywords={journal,data,ranking,explainability,transparency,RAIforUkraine}}
Still More Shades of Null: An Evaluation Suite for Responsible Missing Value Imputation
Falaah Arif Khan, Denys Herasymuk, Nazar Protsiv, and Julia Stoyanovich
@article{shades,title={Still More Shades of {Null}: An Evaluation Suite for Responsible Missing Value Imputation},author={{Arif Khan}, Falaah and Herasymuk, Denys and Protsiv, Nazar and Stoyanovich, Julia},year={2025},journal={Proc. {VLDB} Endow.},volume={18},number={9},doi={14778/3746405.3746416},keywords={journal,RAIforUkraine,data}}
SHAP-based Explanations are Sensitive to Feature Representation
Hyunseung Hwang, Andrew Bell, Joao Fonseca, Venetia Pliatsika, Julia Stoyanovich, and Steven Euijong Whang
In Conference on Fairness, Accountability, and Transparency, ACM FAccT 2025
@inproceedings{sharp_attack,title={{SHAP}-based Explanations are Sensitive to Feature Representation},author={Hwang, Hyunseung and Bell, Andrew and Fonseca, Joao and Pliatsika, Venetia and Stoyanovich, Julia and Whang, Steven Euijong},year={2025},booktitle={Conference on Fairness, Accountability, and Transparency, {ACM FAccT}},keywords={conference,data,explainability,transparency}}
CREDAL: Close Reading of Data Models
George Fletcher, Olha Nahurna, Matvii Prytula, and Julia Stoyanovich
In Proceedings of the Workshop on Human-In-the-Loop Data Analytics (HILDA) at ACM SIGMOD 2025
@inproceedings{credal2025,title={{CREDAL}: Close Reading of Data Models},author={Fletcher, George and Nahurna, Olha and Prytula, Matvii and Stoyanovich, Julia},year={2025},booktitle={Proceedings of the Workshop on Human-In-the-Loop Data Analytics (HILDA) at ACM SIGMOD},url={https://dl.acm.org/doi/10.1145/3736733.3736737},keywords={data,RAIforUkraine}}
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}}
Responsible AI literacy: A stakeholder-first approach
@article{DominguezStoyanovich23,author={Dominguez, Daniel and Stoyanovich, Julia},title={Responsible AI literacy: A stakeholder-first approach},journal={Big Data and Society},year={2023},keywords={journal,education,rds},doi={https://doi.org/10.1177/20539517231219958},}
@article{10.1145/3533379,author={Zehlike, Meike and Yang, Ke and Stoyanovich, Julia},title={Fairness in Ranking, Part I: Score-Based Ranking},journal={{ACM} Computing Surveys},volume={55},number={6},pages={118:1--118:36},year={2023},doi={10.1145/3533379},keywords={journal,fair,ranking},author+an={2=self;3=self},}
A Simple and Practical Method for Reducing the Disparate Impact of
Differential Privacy
Lucas Rosenblatt, Julia Stoyanovich, and Christopher Musco
In Thirty-Eighth AAAI Conference on Artificial Intelligence 2024
@inproceedings{DBLP:conf/aaai/RosenblattSM24,author={Rosenblatt, Lucas and Stoyanovich, Julia and Musco, Christopher},editor={Wooldridge, Michael J. and Dy, Jennifer G. and Natarajan, Sriraam},title={A Simple and Practical Method for Reducing the Disparate Impact of
Differential Privacy},booktitle={Thirty-Eighth {AAAI} Conference on Artificial Intelligence},pages={21554--21562},publisher={{AAAI} Press},year={2024},url={https://doi.org/10.1609/aaai.v38i19.30153},doi={10.1609/AAAI.V38I19.30153},keywords={fairness,privacy,conference},author+an={1=self;2=self},}
A Simple and Practical Method for Reducing the Disparate Impact of Differential PrivacyIn Thirty-Eighth AAAI Conference on Artificial Intelligence 2024
