162 citations · 585 across the 42 of their papers we have counts for
8 papers · 1 filter
Counterfactuals for the Future
Lucius E. J. Bynum, Joshua R. Loftus, Julia Stoyanovich
Counterfactuals are often described as 'retrospective,' focusing on hypothetical alternatives to a realized past. This description relates to an often implicit assumption about the…
Rankers, Rankees, & Rankings: Peeking into the Pandora's Box from a Socio-Technical Perspective
Jun Yuan, Julia Stoyanovich, Aritra Dasgupta
Algorithmic rankers have a profound impact on our increasingly data-driven society. From leisurely activities like the movies that we watch, the restaurants that we patronize; to h…
Epistemic Parity: Reproducibility as an Evaluation Metric for Differential Privacy
Lucas Rosenblatt, Bernease Herman, Anastasia Holovenko +7
Differential privacy (DP) data synthesizers support public release of sensitive information, offering theoretical guarantees for privacy but limited evidence of utility in practica…
Towards Substantive Conceptions of Algorithmic Fairness: Normative Guidance from Equal Opportunity Doctrines
Falaah Arif Khan, Eleni Manis, Julia Stoyanovich
In this work we use Equal Oppportunity (EO) doctrines from political philosophy to make explicit the normative judgements embedded in different conceptions of algorithmic fairness.…
Think About the Stakeholders First! Towards an Algorithmic Transparency Playbook for Regulatory Compliance
Andrew Bell, Oded Nov, Julia Stoyanovich
Increasingly, laws are being proposed and passed by governments around the world to regulate Artificial Intelligence (AI) systems implemented into the public and private sectors. M…
Temporal graph patterns by timed automata
Amir Pouya Aghasadeghi, Jan Van den Bussche, Julia Stoyanovich
Temporal graphs represent graph evolution over time, and have been receiving considerable research attention. Work on expressing temporal graph patterns or discovering temporal mot…
Spending Privacy Budget Fairly and Wisely
Lucas Rosenblatt, Joshua Allen, Julia Stoyanovich
Differentially private (DP) synthetic data generation is a practical method for improving access to data as a means to encourage productive partnerships. One issue inherent to DP i…
An External Stability Audit Framework to Test the Validity of Personality Prediction in AI Hiring
Alene K. Rhea, Kelsey Markey, Lauren D'Arinzo +5
Automated hiring systems are among the fastest-developing of all high-stakes AI systems. Among these are algorithmic personality tests that use insights from psychometric testing,…