Publications

Jump to full list, reports, blogs and popular press.

Highlights

A Nutritional Label for Rankings

A Web-based application that generates a “nutritional label” for rankings. Ranking Facts is made up of a collection of visual widgets that implement our latest research results on fairness, stability, and transparency for rankings, and that communicate details of the ranking methodology, or of the output, to the end user.

Ke Yang, Julia Stoyanovich, Abolfazl Asudeh, Bill Howe, HV Jagadish and Gerome Miklau

Proceedings of ACM SIGMOD (demo), 2018

Online Set Selection with Fairness and Diversity Constraints

Selection algorithms usually score individual items in isolation, and then select the top scoring items. However, often there is an additional diversity objective. Since diversity is a group property, it does not easily jibe with individual item scoring.

Julia Stoyanovich, Ke Yang and HV Jagadish

Proceedings of EDBT, 2018

DataSynthesizer: Privacy-preserving synthetic datasets

To facilitate collaboration over sensitive data, we present DataSynthesizer, a tool that takes a sensitive dataset as input and generates a structurally and statistically similar synthetic dataset with strong privacy guarantees.

Haoyue Ping, Julia Stoyanovich and Bill Howe

Proceedings of SSDBM, 2017

Fides: A platform for responsible data science

We see a need for a data sharing and collaborative analytics platform with features to encourage (and in some cases, enforce) best practices at all stages of the data science lifecycle. We propose Fides, in the context of urban analytics, outlining a systems research agenda in responsible data science.

Bill Howe, Julia Stoyanovichi, Serge Abiteboul, Gerome Miklau, Arnaud Sahuguet and Gerhard Weikum

Proceedings of SSDBM, 2017

 

Full List

Teaching Responsible Data Science: Charting New Pedagogical Territory
Julia Stoyanovich and Armanda Lewis

International Journal of Artificial Intelligence in Education (IJAIED), 2021

Causal Intersectionality and Fair Ranking
Ke Yang, Joshua R. Loftus, and Julia Stoyanovich

Proceedings of FORC 2021

Algorithmic Techniques for Necessary and Possible Winners
Vishal Chakraborty, Theo Delemazure, Benny Kimelfeld, Phokion G. Kolaitis, Kunal Relia, and Julia Stoyanovich

ACM/IMS Transactions on Data Science

Lightweight Inspection of Data Preprocessing in Native Machine Learning Pipelines
Stefan Grafberger, Julia Stoyanovich, and Sebastian Schelter

Proceedings of CIDR 2021

Taming Technical Bias in Machine Learning Pipelines
Sebastian Schelter and Julia Stoyanovich

IEEE Data Engineering Bulletin 43(4): 2020

Fairness in Ranking: A Survey
Meike Zehlike, Ke Yang, and Julia Stoyanovich

arXiv

Impact Remediation: Optimal Interventions to Reduce Inequality
Lucius E. J. Bynum, Joshua R. Loftus, and Julia Stoyanovich

arXiv

Fairness as Equality of Opportunity: Normative Guidance from Political Philosophy
Falaah Arif Khan, Eleni Manis, and Julia Stoyanovich

arXiv

Fairness and Friends
Falaah Arif Khan, Eleni Manis, and Julia Stoyanovich

ACM FAccT (2021), tutorial slides

Responsible Data Management
Julia Stoyanovich, Bill Howe, and H.V. Jagadish

PVLDB 13(12): 3474-3489 (2020), invited paper accompanying VLDB 2020 keynote presentation

The Imperative of Interpretable Machines
Julia Stoyanovich, Jay J. Van Bavel, and Tessa V. West

Nature Machine Intelligence, April 2020

Fairness-Aware Instrumentation of Preprocessing Pipelines for Machine Learning
Ke Yang, Biao Huang, Julia Stoyanovich, and Sebastian Schelter

Proceedings of HILDA 2020 (an ACM SIGMOD workshop)

FairPrep: Promoting Data to a First-Class Citizen in Studies on Fairness-Enhancing Interventions
Sebastian Schelter, Yuxuan He, Jatin Khilnani, and Julia Stoyanovich

EDBT 2020 (short paper), arXiv, November 2019, EDBT talk video

Balanced Ranking with Diversity Constraints
Ke Yang, Vasilis Gkatzelis, and Julia Stoyanovich

Proceedings of IJCAI 2019

Designing Fair Ranking Schemes
Abolfazl Asudeh, H. V. Jagadish, Julia Stoyanovich, and Gautam Das

Proceedings of ACM SIGMOD, 2019

MithraRanking: A System for Responsible Ranking Design (demonstration)
Yifan Guan, Abolfazl Asudeh, Pranav Mayuram, H. V. Jagadish, Julia Stoyanovich, Gerome Miklau, and Gautam Das

Proceedings of ACM SIGMOD, 2019

Transparency, Fairness, Data Protection, Neutrality: Data Management Challenges in the Face of New Regulation
Serge Abiteboul and Julia Stoyanovich

ACM Journal of Data and Information Quality, 2019

Nutritional Labels for Data and Models
Julia Stoyanovich and Bill Howe

IEEE Data Engineering Bulletin 42(3): 13-23 (2019)

Towards Responsible Data-driven Decision Making in Score-Based Systems
Abolfazl Asudeh, H. V. Jagadish, and Julia Stoyanovich

IEEE Data Engineering Bulletin 42(3): 76-87 (2019)

TransFAT: Translating Fairness, Accountably and Transparency into Data Science Practice
Julia Stoyanovich

International Workshop on Processing Information Ethically (PIE@CAiSE) (2019)

On Obtaining Stable Rankings
Abolfazl Asudeh, H. V. Jagadish, Gerome Miklau, and Julia Stoyanovich

PVLDB 12(3): 237-250 (2018)

Panel: A Debate on Data and Algorithmic Ethics
Julia Stoyanovich, Bill Howe, H. V. Jagadish, Gerome Miklau

PVLDB 11(12): 2165-2167 (2018)

Research Directions for Principles of Data Management (Dagstuhl Perspectives Workshop 16151)
Serge Abiteboul, Marcelo Arenas, Pablo Barceló, Meghyn Bienvenu, Diego Calvanese, Claire David, Richard Hull, Eyke Hüllermeier, Benny Kimelfeld, Leonid Libkin, Wim Martens, Tova Milo, Filip Murlak, Frank Neven, Magdalena Ortiz, Thomas Schwentick, Julia Stoyanovich, Jianwen Su, Dan Suciu, Victor Vianu, Ke Yi

Dagstuhl Manifestos 7(1): 1-29 (2018)

A Technical Research Agenda in Data Ethics and Responsible Data Management
Julia Stoyanovich, Bill Howe, and HV Jagadish

Proceedings of ACM SIGMOD, 2018

RC-Index: Diversifying Answers to Range Queries
Yue Wang, Alexandra Meliou, and Gerome Miklau

PVLDB 11(7), 2018

A Nutritional Label for Rankings
Ke Yang, Julia Stoyanovich, Abolfazl Asudeh, Bill Howe, HV Jagadish and Gerome Miklau

Proceedings of ACM SIGMOD (demo), 2018

Online Set Selection with Fairness and Diversity Constraints
Julia Stoyanovich, Ke Yang and HV Jagadish

Proceedings of EDBT, 2018

MobilityMirror: Bias-Adjusted Synthetic Transportation Datasets
Luke Rodriguez, Babak Salimi, Haoyue Ping, Julia Stoyanovich and Bill Howe

BiDU2018

Diversity in Big Data: A Review
Marina Drosou, HV Jagadish, Evaggelia Pitoura and Julia Stoyanovich

Big Data Special Issue on Social and Technical Trade-Offs, June 2017

Measuring fairness in ranked outputs
Ke Yang and Julia Stoyanovich

Proceedings of SSDBM, 2017

DataSynthesizer: Privacy-preserving synthetic datasets
Haoyue Ping, Julia Stoyanovich and Bill Howe

Proceedings of SSDBM, 2017

Fides: A platform for responsible data science
Bill Howe, Julia Stoyanovichi, Serge Abiteboul, Gerome Miklau, Arnaud Sahuguet and Gerhard Weikum

Proceedings of SSDBM, 2017

Data, responsibly: fairness, neutrality and transparency in data analysis
Julia Stoyanovich, Serge Abiteboul and Gerome Miklau

Proceedings of EDBT, 2016

Collaborative Access Control in WebdamLog
Vera Zaychik Moffitt, Julia Stoyanovich, Serge Abiteboul, Gerome Miklau

Proceedings of ACM SIGMOD, 2015

Rule-Based Application Development using Webdamlog
Serge Abiteboul, Emilien Antoine, Gerome Miklau, Julia Stoyanovich, and Jules Testard

Proceedings of SIGMOD, 2013

Making Interval-Based Clustering Rank-Aware
Julia Stoyanovich, Sihem Amer-Yahia and Tova Milo

Proceedings of EDBT, 2011

On Provenance and Privacy
Susan Davidson, Sanjeev Khanna, Sudeepa Roy, Julia Stoyanovich, Val Tannen, Yi Chen

Proceedings of ICDT, 2011

Reports

Data, Responsibly (Dagstuhl Seminar 16291)
Serge Abiteboul, Gerome Miklau, Julia Stoyanovich and Gerhard Weikum

Schloss Dagstuhl Seminar Report 2016

Research Directions for Principles of Data Management (Dagstuhl Perspectives Workshop 16151)
Serge Abiteboul, Marcelo Arenas, Pablo Barceló, Meghyn Bienvenu, Diego Calvanese, Claire David, Richard Hull, Eyke Hüllermeier, Benny Kimelfeld, Leonid Libkin, Wim Martens, Tova Milo, Filip Murlak, Frank Neven, Magdalena Ortiz, Thomas Schwentick, Julia Stoyanovich, Jianwen Su, Dan Suciu, Victor Vianu, Ke Yi

Schloss Dagstuhl Seminar Report 2016 (full), SIGMOD Record (abridged)

Testimony of Julia Stoyanovich before the New York City Department of Consumer and Worker Protection regarding Local Law 144 of 2021 in Relation to Automated Employment Decision Tools
Julia Stoyanovich June 6, 2022

We need laws to take on racism and sexism in hiring technology
Alexandra Reeve Givens, Hilke Schellmann, and Julia Stoyanovich New York Times, March 17, 2021

Public Engagement Showreel, Int 1894
NYU Center for Responsible AI December 15, 2020

Testimony of Julia Stoyanovich before New York City Council Committee on Technology regarding Int 1894-2020, Sale of automated employment decision tools
Julia Stoyanovich November 12, 2020

Testimony of Julia Stoyanovich and Solon Barocas before New York City Council Committee on Technology, regarding Update on Local Law 49 of 2018 in Relation to Automated Decision Systems (ADS) Used by Agencies
Julia Stoyanovich and Solon Barocas April 4, 2019

Testimony of Julia Stoyanovich before New York City Council Committee on Technology and the Commission on Public Information and Communication (COPIC)
February 12, 2019

Follow the Data! Algorithmic Transparency Starts with Data Transparency
Julia Stoyanovich and Bill Howe The Ethical Machine, November 27, 2018

An Algorithmic Approach to Correct Bias in Urban Transportation Datasets
October 30, 2018

NYC Has An Algorithm Ethics Task Force, And Drexel Prof Julia Stoyanovich Is Involved
May 30, 2018

Testimony of Julia Stoyanovich before the New York City Council Committee on Technology regarding Automated Processing of Data (Int. 1696-2017)
October 16, 2017

Refining the Concept of a Nutritional Label for Data and Models
Julia Stoyanovich and Bill Howe Freedom to Tinker, Princeton CITP

University Researchers Use ‘Fake’ Data for Social Good
Bill Howe November 7, 2017

Julia Stoyanovich on the importance of many perspectives at the Data for Good Exchange
Tech at Bloomberg, July 7, 2017

Plaidoyer pour une analyse responsable des données
Serge Abiteboul and Julia Stoyanovich Le Monde, October 12, 2015

Revealing Algorithmic Rankers
Julia Stoyanovich and Ellen P. Goodman Freedom to Tinker, Princeton CITP

The Data, Responsibly Manifesto
Serge Abiteboul and Julia Stoyanovich ACM SIGMOD blog