10 citations · 18 across the 9 of their papers we have counts for
9 papers
Productionized Fairness Measurement Under Privacy Constraints
Osonde A. Osoba, Yuzi He, Saikrishna Badrinarayanan +3
Fairness measurements in the form of disaggregated evaluations often rely on demographic signals that are legally constrained or culturally sensitive. Race and ethnicity signals ar…
Privacy-Preserving Race/Ethnicity Estimation for Algorithmic Bias Measurement in the U.S
Saikrishna Badrinarayanan, Osonde Osoba, Miao Cheng +4
AI fairness measurements, including tests for equal treatment, often take the form of disaggregated evaluations of AI systems. Such measurements are an important part of Responsibl…
Deep Generative Modeling in Network Science with Applications to Public Policy Research
Gavin S. Hartnett, Raffaele Vardavas, Lawrence Baker +5
Network data is increasingly being used in quantitative, data-driven public policy research. These are typically very rich datasets that contain complex correlations and inter-depe…
A Generative Machine Learning Approach to Policy Optimization in Pursuit-Evasion Games
Shiva Navabi, Osonde A. Osoba
We consider a pursuit-evasion game [11] played between two agents, 'Blue' (the pursuer) and 'Red' (the evader), over time steps. Red aims to attack Blue's territory. Blue's obj…
Policy-focused Agent-based Modeling using RL Behavioral Models
Osonde A. Osoba, Raffaele Vardavas, Justin Grana +2
Agent-based Models (ABMs) are valuable tools for policy analysis. ABMs help analysts explore the emergent consequences of policy interventions in multi-agent decision-making settin…
Steps Towards Value-Aligned Systems
Osonde A. Osoba, Benjamin Boudreaux, Douglas Yeung
Algorithmic (including AI/ML) decision-making artifacts are an established and growing part of our decision-making ecosystem. They are indispensable tools for managing the flood of…
Beyond DAGs: Modeling Causal Feedback with Fuzzy Cognitive Maps
Osonde Osoba, Bart Kosko
Fuzzy cognitive maps (FCMs) model feedback causal relations in interwoven webs of causality and policy variables. FCMs are fuzzy signed directed graphs that allow degrees of causal…
Noisy Expectation-Maximization: Applications and Generalizations
Osonde Osoba, Bart Kosko
We present a noise-injected version of the Expectation-Maximization (EM) algorithm: the Noisy Expectation Maximization (NEM) algorithm. The NEM algorithm uses noise to speed up the…
Noise Benefits in Expectation-Maximization Algorithms
Osonde Adekorede Osoba
This dissertation shows that careful injection of noise into sample data can substantially speed up Expectation-Maximization algorithms. Expectation-Maximization algorithms are a c…