Nic Fishman

The Fragility of Fairness: Causal Sensitivity Analysis for Fair Machine Learning

Jake Fawkes, Nic Fishman, M. B. Andrews, Zachary C. Lipton

Fairness metrics are a core tool in the fair machine learning literature (FairML), used to determine that ML models are, in some sense, "fair". Real-world data, however, are typically plagued by various measurement biases and other violated assumptions, which can render fairness …

The Fragility of Fairness: Causal Sensitivity Analysis for Fair Machine Learning

Jake Fawkes, Nic Fishman, M. B. Andrews, Zachary C. Lipton

Human mobility networks reveal increased segregation in large cities

Hamed Nilforoshan, Wenli Looi, Emma Pierson, Blanca Villanueva et autres

Abstract A long-standing expectation is that large, dense and cosmopolitan areas support socioeconomic mixing and exposure among diverse individuals 1–6 . Assessing this hypothesis has been difficult because previous measures of socioeconomic mixing have relied on static residential housing data rather than …

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Metropolis Sampling for Constrained Diffusion Models

Nic Fishman, Leo Klarner, Émile Mathieu, Michael Hutchinson et autres

Denoising diffusion models have recently emerged as the predominant paradigm for generative modelling on image domains. In addition, their extension to Riemannian manifolds has facilitated a range of applications across the natural sciences. While many of these problems stand to benefit from …

Diffusion Models for Constrained Domains

Nic Fishman, Leo Klarner, Valentin De Bortoli, Émile Mathieu et autres

Denoising diffusion models are a novel class of generative algorithms that achieve state-of-the-art performance across a range of domains, including image generation and text-to-image tasks. Building on this success, diffusion models have recently been extended to the Riemannian manifold setting, broadening their …

Human mobility networks reveal increased segregation in large cities

Hamed Nilforoshan, Wenli Looi, Emma Pierson, Blanca Villanueva et autres

A long-standing expectation is that large, dense, and cosmopolitan areas support socioeconomic mixing and exposure between diverse individuals. It has been difficult to assess this hypothesis because past approaches to measuring socioeconomic mixing have relied on static residential housing data rather than …

Should attention be all we need? The epistemic and ethical implications of unification in machine learning

Nic Fishman, Leif Hancox-Li

“Attention is all you need” has become a fundamental precept in machine learning research. Originally designed for machine translation, transformers and the attention mechanisms that underpin them now find success across many problem domains. With the apparent domain-agnostic success of transformers, many …

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Change We Can Believe In: Structural and Content Dynamics within Belief Networks

Nic Fishman, Nicholas T. Davis

Abstract Scholars have used network analysis to explore the structural properties of mass ideology. This article incorporates two important, though ignored features in past research by investigating howtimeshapes the properties of belief networksfor different populations of people—those who exhibit high and low …

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Deep Ecological Inference

Nic Fishman, Colin McAuliffe

We introduce an efficient approximation to the loss function for the ecological inference problem, where individual labels are predicted from aggregates. This allows us to construct ecological versions of linear models, deep neural networks, and Bayesian neural networks. Using these models we …

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Replication Data for: Change We Can Believe In: Structural and Content Dynamics within Belief Networks

Nic Fishman, Nicholas T. Davis

Scholars use network analysis as an analytical framework for exploring the structural properties of mass ideology. This manuscript incorporates two important, though ignored features in past research by investigating how time shapes the properties of belief networks for different populations of people—those …

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Change We Can Believe In: Structural and Content Dynamics within Belief Networks

Nic Fishman, Nicholas T. Davis

Presumably, network analysis supplies an analytical framework for exploring the empirical properties of ideology. This manuscript incorporates two important, though ignored features in past research by inves- tigating how time shapes the properties of belief networks for different populations of people – …

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Fleeing from Terror: Considering Safety When Designing Public Spaces in the Age of Mass Murder

Nic Fishman