162 citations · 585 across the 42 of their papers we have counts for
47 papers
Seasoning Data Modeling Education with GARLIC: A Participatory Co-Design Framework
Viktoriia Makovska, Ihor Michurin, Mariia Tokhtamysh +2
Entity-Relationship (ER) modeling is commonly taught as a primarily technical activity, despite its central role in shaping how data systems represent people, processes, and instit…
The Cambridge Report on Database Research
Anastasia Ailamaki, Samuel Madden, Daniel Abadi +43
On October 19 and 20, 2023, the authors of this report convened in Cambridge, MA, to discuss the state of the database research field, its recent accomplishments and ongoing challe…
Are Data Experts Buying into Differentially Private Synthetic Data? Gathering Community Perspectives
Lucas Rosenblatt, Bill Howe, Julia Stoyanovich
Data privacy is a core tenet of responsible computing, and in the United States, differential privacy (DP) is the dominant technical operationalization of privacy-preserving data a…
Query Refinement for Diverse Top- Selection
Felix S. Campbell, Alon Silberstein, Julia Stoyanovich +1
Database queries are often used to select and rank items as decision support for many applications. As automated decision-making tools become more prevalent, there is a growing rec…
Fairness in Algorithmic Recourse Through the Lens of Substantive Equality of Opportunity
Andrew Bell, Joao Fonseca, Carlo Abrate +2
Algorithmic recourse -- providing recommendations to those affected negatively by the outcome of an algorithmic system on how they can take action and change that outcome -- has ga…
A New Paradigm for Counterfactual Reasoning in Fairness and Recourse
Lucius E. J. Bynum, Joshua R. Loftus, Julia Stoyanovich
Counterfactuals and counterfactual reasoning underpin numerous techniques for auditing and understanding artificial intelligence (AI) systems. The traditional paradigm for counterf…
A Simple and Practical Method for Reducing the Disparate Impact of Differential Privacy
Lucas Rosenblatt, Julia Stoyanovich, Christopher Musco
Differentially private (DP) mechanisms have been deployed in a variety of high-impact social settings (perhaps most notably by the U.S. Census). Since all DP mechanisms involve add…
Setting the Right Expectations: Algorithmic Recourse Over Time
Joao Fonseca, Andrew Bell, Carlo Abrate +2
Algorithmic systems are often called upon to assist in high-stakes decision making. In light of this, algorithmic recourse, the principle wherein individuals should be able to take…
Social network modeling and applications, a tutorial
Lisette Espín-Noboa, Tiago Peixoto, Fariba Karimi
Social networks have been widely studied over the last century from multiple disciplines to understand societal issues such as inequality in employment rates, managerial performanc…
The Unbearable Weight of Massive Privilege: Revisiting Bias-Variance Trade-Offs in the Context of Fair Prediction
Falaah Arif Khan, Julia Stoyanovich
In this paper we revisit the bias-variance decomposition of model error from the perspective of designing a fair classifier: we are motivated by the widely held socio-technical bel…
The Possibility of Fairness: Revisiting the Impossibility Theorem in Practice
Andrew Bell, Lucius Bynum, Nazarii Drushchak +3
The ``impossibility theorem'' -- which is considered foundational in algorithmic fairness literature -- asserts that there must be trade-offs between common notions of fairness and…
An Epistemic and Aleatoric Decomposition of Arbitrariness to Constrain the Set of Good Models
Falaah Arif Khan, Denys Herasymuk, Nazar Protsiv +1
Recent research reveals that machine learning (ML) models are highly sensitive to minor changes in their training procedure, such as the inclusion or exclusion of a single data poi…
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,…
Temporal Regular Path Queries
Marcelo Arenas, Pedro Bahamondes, Amir Aghasadeghi +1
In the last decade, substantial progress has been made towards standardizing the syntax of graph query languages, and towards understanding their semantics and complexity of evalua…
Disaggregated Interventions to Reduce Inequality
Lucius E. J. Bynum, Joshua R. Loftus, Julia Stoyanovich
A significant body of research in the data sciences considers unfair discrimination against social categories such as race or gender that could occur or be amplified as a result of…
Fairness as Equality of Opportunity: Normative Guidance from Political Philosophy
Falaah Arif Khan, Eleni Manis, Julia Stoyanovich
Recent interest in codifying fairness in Automated Decision Systems (ADS) has resulted in a wide range of formulations of what it means for an algorithmic system to be fair. Most o…
Most Expected Winner: An Interpretation of Winners over Uncertain Voter Preferences
Haoyue Ping, Julia Stoyanovich
It remains an open question how to determine the winner of an election when voter preferences are incomplete or uncertain. One option is to assume some probability space over the v…