J. Stoyanovich, S. Abiteboul, B. Howe, H. V. Jagadish, and S. Schelter Communications of the ACM, Vol 65, Issue 6 (2022)read online
Resume format, Linkedin urls and other unexpected influences on AI personality prediction in hiring: Results of an audit
A. K. Rhea, K. Markey, L. D’Arinzo, H. Schellmann, M. Sloane, P. Squires, and J. Stoyanovich Proceedings of the Fifth AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES), 2022, forthcoming
An external stability audit framework to test the validity of personality prediction in AI hiring
A. K. Rhea, K. Markey, L. D’Arinzo, H. Schellmann, M. Sloane, P. Squires, F. Arif Khan, and J. Stoyanovich Data Mining and Knowledge Discovery, Special Issue on Bias and Fairness in AI, 2022, forthcoming
Andrew Bell, Ian Solano-Kamaiko, Oded Nov & Julia Stoyanovich FAccT ’22: 2022 ACM Conference on Fairness, Accountability, and Transparency (2022)read online
We Are AI is a 5-week learning circle course that provides an introduction to the basics of AI and the social and ethical dimensions of the use of AI in modern life.
Responsible Data Science Courses at the NYU Center for Data Science
Responsible Data Science is a technical course for graduate and undergraduate students at NYU that tackles the issues of ethics, legal compliance, data quality, algorithmic fairness and diversity, transparency of data and algorithms, privacy, and data protection.
This course is designed to raise awareness of the societal impacts of technology and to give individuals and institutions the tools to pursue responsible AI use.
Ranking Facts is a nutritional label for rankings. The tool allows you to upload data, design a ranking methodology, and understand the fairness, diversity, and stability of the result. Use it through a web-based UI, or incorporate it into your python code.
FairPrep is a design and evaluation framework for fairness-enhancing interventions that treats data as a first-class citizen in complex data science pipelines.
The Data Synthesizer is a tool that generates privacy-preserving synthetic data, using the framework of differential privacy. Synthetic data can facilitate collaborations between data scientists and owners of sensitive data, and it can support algorithmic transparency and disclosure. Use it through a web-based UI, or incorporate it into your python code.
MLInspect is a data distribution debugger. Use it to tame technical bias in your complex data science pipelines that use common libraries like pandas and scikit-learn.