Our Work

Ranking Facts

Ranking Facts is a standardized, human-interpretable summary of the ranking methodology and of its result.

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Fair Prep

FairPrep is a design and evaluation framework for fairness-enhancing interventions that treats data as a first-class citizen.

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Data Synthesizer

DataSynthesizer generates synthetic data that simulates a given dataset.

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Teaching Responsible Data Science: Charting New Pedagogical Territory

Armanda Lewis

Julia Stoyanovich

International Journal of Artificial Intelligence in Education

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Public Engagement Showreel Int 1894

Julia Stoyanovich

Steven Kuyan

Meghan McDermott

Maria Grillo

Mona Sloane

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Teaching responsible data science

Julia Stoyanovich

Armanda Lewis

International Journal of Artificial Intelligence in Education (IJAIED), 2021, Note: Special Issue: The FATE of AI in Education: Fairness, Accountability, Transparency, and Ethics

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Lightweight inspection of data preprocessing in native machine learning pipelines

Stefan Grafberger

Julia Stoyanovich

Sebastian Schelter

CIDR 2021, 11th Conference on Innovative Data Systems Research, Online Proceedings

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Taming technical bias in machine learning pipelines

Sebastian Schelter

Julia Stoyanovich

IEEE Data Eng. Bull., vol. 43, 2020

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FairPrep: Promoting Data to a First-Class Citizen in Studies on Fairness-Enhancing Interventions

Sebastian Schelter

Yuxuan He

Jatin Khilnani

Julia Stoyanovich

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

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Fairness-Aware Instrumentation of Preprocessing Pipelines for Machine Learning

Ke Yang, Biao Huang

Julia Stoyanovich

Sebastian Schelter

Proceedings of HILDA 2020 (an ACM SIGMOD workshop)

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Responsible Data Management

Julia Stoyanovich

Bill Howe

H.V. Jagadish

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

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The Imperative of Interpretable Machines

Julia Stoyanovich

Jay J. Van Bavel

Tessa V. West

Nature Machine Intelligence, April 2020

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Causal Intersectionality for Fair Ranking

Ke Yang

Joshua R. Loftus

Julia Stoyanovich

arXiv, June 2020

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Balanced Ranking with Diversity Constraints

Ke Yang

Vasilis Gkatzelis

Julia Stoyanovich

Proceedings of IJCAI 2019

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Designing Fair Ranking Schemes

Abolfazl Asudeh

H. V. Jagadish

Julia Stoyanovich

Gautam Das

Proceedings of ACM SIGMOD, 2019

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MithraRanking: A System for Responsible Ranking Design

Yifan Guan

Abolfazl Asudeh

Pranav Mayuram

Hosagrahar V. Jagadish

Julia Stoyanovich

Gerome Miklau

Gautam Das

Proceedings of ACM SIGMOD, 2019

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Transparency, Fairness, Data Protection, Neutrality: Data Management Challenges in the Face of New Regulation

Serge Abiteboul

Julia Stoyanovich

ACM Journal of Data and Information Quality, 2019

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Nutritional Labels for Data and Models

Julia Stoyanovich

Bill Howe

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

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Towards Responsible Data-driven Decision Making in Score-Based Systems

Abolfazl Asudeh

H. V. Jagadish

Julia Stoyanovich

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

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

Julia Stoyanovich

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

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WE are AI 5-week learning circle course – Introduction to the basics of AI and the social and ethical dimensions of the use of AI in modern life.

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Undergraduate and Graduate Responsible Data Science Courses at NYU CDS

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AI Ethics: Global Perspectives

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The Data, Responsibly Comic Series

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