Our Work
Innovating Public Procurement to Mitigate the Risks of Artificial Intelligence Systems
Artificial Intelligence (AI) systems are increasingly deployed in the public sector. Existing public procurement processes and standards are in urgent need of innovation to address potential risks and harms to citizens. Read our primer based on our research and on input from leading experts in the public sector, data science, civil society, policy, social science, and the law to learn about pathways forward.
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Terra Incognita NYC
Terra Incognita NYC set out as a collaboration of New_Public and principal investigator Mona Sloane, a Fellow with New York Universityâs Institute for Public Knowledge to learn from this public migration online and and map out the unknown digital public spacesâterra incognitaâforming in the nationâs largest city. With a team of digital ethnographers, we spent the summer of 2020 listening to New Yorkers, across the five boroughs of New York City, about their digital lives since the pandemic began.
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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
International Journal of Artificial Intelligence in Education
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Public Engagement Showreel Int 1894
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Teaching responsible data science
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
CIDR 2021, 11th Conference on Innovative Data Systems Research, Online Proceedings
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FairPrep: Promoting Data to a First-Class Citizen in Studies on Fairness-Enhancing Interventions
EDBT 2020 (short paper), arXiv, November 2019, EDBT talk video
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Fairness-Aware Instrumentation of Preprocessing Pipelines for Machine Learning
Proceedings of HILDA 2020 (an ACM SIGMOD workshop)
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Responsible Data Management
PVLDB 13(12): 3474-3489 (2020), invited paper accompanying VLDB 2020 keynote presentation
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Transparency, Fairness, Data Protection, Neutrality: Data Management Challenges in the Face of New Regulation
ACM Journal of Data and Information Quality, 2019
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Nutritional Labels for Data and Models
IEEE Data Engineering Bulletin 42(3): 13-23 (2019)
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Towards Responsible Data-driven Decision Making in Score-Based Systems
IEEE Data Engineering Bulletin 42(3): 76-87 (2019)
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TransFAT: Translating Fairness, Accountably and Transparency into Data Science Practice
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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