Welcome to the Center for Responsible AI at New York University

Our goal is to build a future in which responsible AI is synonymous with AI. Our work centers around interdisciplinary research, technology policy, and education and training for AI practitioners, decision makers, and the public at large.

What is responsible AI? We use this terms 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

Jul 31, 2024	On July 29-31, NYU R/AI and NYU CDS co-hosted a workshop on community-informed policies and best-practices for the National Artificial Intelligence Research Resource (NAIRR). Learn more about the event and read the press release.
Mar 18, 2024	Join us on April 19, 2024 for the Spring 2024 NYU Privacy Day. Learn more about the event and RSVP to attend.
Dec 23, 2023	Juia Stoyanovich published an opinion article in The Hill with recommendations for the US Congress regarding high-impact AI regulation in 2024.
Dec 4, 2023	On December 15, 2023, we held the Fall 2023 Responsible AI (RAI) Research Program Showcase. Learn more about the event.
Dec 4, 2023	We are running a free algorithmic transparency playbook workshop at the NYU Tandon Future Labs on December 12. Learn more about the workshop and RSVP to attend!
Nov 8, 2023	Juia Stoyanovich spoke at the 2023 IBM Security Summit \| AI: Today’s Cybersecurity Battleground
Nov 1, 2023	Julia Stoyanovich participated in the US Senate AI Insight Forum on High Impact AI. Read her statement and learn more about this event.
Nov 1, 2023	Andrew Bell presented Setting the Right Expectations: Algorithmic Recourse Over Time at EAAMO 2023 in Boston. The paper received the Best Paper in the AI Track award!

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

Cite PDF Github

@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

SIGMOD Rec. 2024

Cite PDF Github

@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

Daniel Dominguez, and Julia Stoyanovich

Big Data and Society 2023

Cite PDF

@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},
}

Setting the Right Expectations: Algorithmic Recourse Over Time

João Fonseca, Andrew Bell, Carlo Abrate, Francesco Bonchi, and Julia Stoyanovich

In Proceedings of the 3rd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization, EAAMO 2023, Boston, MA, USA, 30 October 2023 - 1 November 2023 2023

Cite PDF

@inproceedings{DBLP:conf/eaamo/FonsecaBABS23,
  author = {Fonseca, Jo{\~{a}}o and Bell, Andrew and Abrate, Carlo and Bonchi, Francesco and Stoyanovich, Julia},
  title = {Setting the Right Expectations: Algorithmic Recourse Over Time},
  booktitle = {Proceedings of the 3rd {ACM} Conference on Equity and Access in Algorithms,
                    Mechanisms, and Optimization, {EAAMO} 2023, Boston, MA, USA, 30 October
                    2023 - 1 November 2023},
  pages = {29:1--29:11},
  publisher = {{ACM}},
  year = {2023},
  url = {https://doi.org/10.1145/3617694.3623251},
  doi = {10.1145/3617694.3623251},
  timestamp = {Thu, 09 Nov 2023 21:12:58 +0100},
  biburl = {https://dblp.org/rec/conf/eaamo/FonsecaBABS23.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  keywords = {conference, fairness},
}

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

Proc. VLDB Endow. 2023

Cite PDF Github

@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}
}

Fairness in Ranking, Part I: Score-Based Ranking

Meike Zehlike, Ke Yang, and Julia Stoyanovich

ACM Computing Surveys 2023

Cite PDF

@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},
}

Responsible Data Management

Julia Stoyanovich, Serge Abiteboul, Bill Howe, H. V. Jagadish, and Sebastian Schelter

Communications of the ACM 2022

Cite PDF

@article{DBLP:journals/cacm/StoyanovichAHJS22,
  author = {Stoyanovich, Julia and Abiteboul, Serge and Howe, Bill and Jagadish, H. V. and Schelter, Sebastian},
  title = {Responsible Data Management},
  journal = {Communications of the {ACM}},
  volume = {65},
  number = {6},
  pages = {64--74},
  year = {2022},
  doi = {10.1145/3488717},
  timestamp = {Wed, 25 May 2022 10:40:29 +0200},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  keywords = {data},
  author+an = {1=self},
}

Counterfactuals for the Future

Lucius E. J. Bynum, Joshua R. Loftus, and Julia Stoyanovich

In Proceedings of the AAAI Conference on Artificial Intelligence 2023

Cite PDF

@inproceedings{bynum2023counterfactuals,
  author = {Bynum, Lucius E. J. and Loftus, Joshua R. and Stoyanovich, Julia},
  title = {Counterfactuals for the Future},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26655},
  doi = {10.1609/aaai.v37i12.26655},
  number = {12},
  booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
  year = {2023},
  pages = {14144-14152},
  keywords = {journal,fairness,causal,counterfactuals},
}

Towards Substantive Conceptions of Algorithmic Fairness: Normative Guidance from Equal Opportunity Doctrines

Falaah Arif Khan, Eleni Manis, and Julia Stoyanovich

In Equity and Access in Algorithms, Mechanisms, and Optimization, EAAMO 2022, Arlington, VA, USA, October 6-9, 2022 2022

Cite PDF

@inproceedings{DBLP:conf/eaamo/ArifKhanMS22,
  author = {{Arif Khan}, Falaah and Manis, Eleni and Stoyanovich, Julia},
  title = {Towards Substantive Conceptions of Algorithmic Fairness: Normative
                    Guidance from Equal Opportunity Doctrines},
  booktitle = {Equity and Access in Algorithms, Mechanisms, and Optimization, {EAAMO}
                    2022, Arlington, VA, USA, October 6-9, 2022},
  pages = {18:1--18:10},
  publisher = {{ACM}},
  year = {2022},
  url = {https://doi.org/10.1145/3551624.3555303},
  doi = {10.1145/3551624.3555303},
  keywords = {conference, fairness},
}

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

Cite

@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}
}

The Game Of Recourse: Simulating Algorithmic Recourse over Time to Improve Its Reliability and Fairness

Andrew Bell, João Fonseca, and Julia Stoyanovich

In Companion of the 2024 International Conference on Management of Data, SIGMOD/PODS 2024, Santiago AA, Chile, June 9-15, 2024 2024

Cite Github

@inproceedings{DBLP:conf/sigmod/BellFS24,
  author = {Bell, Andrew and Fonseca, Jo{\~{a}}o and Stoyanovich, Julia},
  editor = {Barcel{\'{o}}, Pablo and Pi, Nayat S{\'{a}}nchez and Meliou, Alexandra and Sudarshan, S.},
  title = {The Game Of Recourse: Simulating Algorithmic Recourse over Time to
                    Improve Its Reliability and Fairness},
  booktitle = {Companion of the 2024 International Conference on Management of Data,
                    {SIGMOD/PODS} 2024, Santiago AA, Chile, June 9-15, 2024},
  pages = {464--467},
  publisher = {{ACM}},
  year = {2024},
  url = {https://doi.org/10.1145/3626246.3654742},
  doi = {10.1145/3626246.3654742},
  keywords = {fairness,conference,demo},
  author+an = {1=self;2=self;3=self},
}