54 citations · 150 across the 7 of their papers we have counts for
7 papers
What to Consider When Considering Differential Privacy for Policy
Priyanka Nanayakkara, Jessica Hullman
Differential privacy (DP) is a mathematical definition of privacy that can be widely applied when publishing data. DP has been recognized as a potential means of adhering to variou…
Measure-Observe-Remeasure: An Interactive Paradigm for Differentially-Private Exploratory Analysis
Priyanka Nanayakkara, Hyeok Kim, Yifan Wu +4
Differential privacy (DP) has the potential to enable privacy-preserving analysis on sensitive data, but requires analysts to judiciously spend a limited ``privacy loss budget'' $ε…
REFORMS: Reporting Standards for Machine Learning Based Science
Sayash Kapoor, Emily Cantrell, Kenny Peng +16
Machine learning (ML) methods are proliferating in scientific research. However, the adoption of these methods has been accompanied by failures of validity, reproducibility, and ge…
The worst of both worlds: A comparative analysis of errors in learning from data in psychology and machine learning
Jessica Hullman, Sayash Kapoor, Priyanka Nanayakkara +2
Recent arguments that machine learning (ML) is facing a reproducibility and replication crisis suggest that some published claims in ML research cannot be taken at face value. Thes…
Visualizing Privacy-Utility Trade-Offs in Differentially Private Data Releases
Priyanka Nanayakkara, Johes Bater, Xi He +2
Organizations often collect private data and release aggregate statistics for the public's benefit. If no steps toward preserving privacy are taken, adversaries may use released st…
Unpacking the Expressed Consequences of AI Research in Broader Impact Statements
Priyanka Nanayakkara, Jessica Hullman, Nicholas Diakopoulos
The computer science research community and the broader public have become increasingly aware of negative consequences of algorithmic systems. In response, the top-tier Neural Info…