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Transparency: Motivations and Challenges? Adrian…
https://mlg.eng.cam.ac.uk/adrian/transparency.pdf21 Mar 2024: truth.”. Defining criteria and tests for practical faithfulness are important open pro-blems. ... 53. Prat, A.: The wrong kind of transparency. American Economic Review 95(3),862–877 (2005). -
Discovering Interpretable Representations for Both Deep Generative…
https://mlg.eng.cam.ac.uk/adrian/ICML18-Discovering.pdf21 Mar 2024: Significant re-sults are identified using a paired t-test with p = 0.05. ... The SVHN dataset contains 73,257training digits (instances) and 26,032 test digits. -
From Parity to Preference-based Notionsof Fairness in Classification…
https://mlg.eng.cam.ac.uk/adrian/NeurIPS17-from-parity-to-preference.pdf21 Mar 2024: In this paper, we draw inspiration from the fair-division and envy-freeness literature in economics and game theory and proposepreference-based notions of fairness—given the choice between various sets ... Finally,we train the five classifiers -
A Unified Approach to Quantifying Algorithmic Unfairness: Measuring…
https://mlg.eng.cam.ac.uk/adrian/KDD2018_inequality_indices.pdf21 Mar 2024: In this paper, we propose to quantify unfairness using inequal-ity indices that have been extensively studied in economics andsocial welfare [3, 10, 19]. ... For all experiments, we repeatedly split the data into 70%-30%train-test sets 10 times and -
Human Perceptions of Fairness in Algorithmic Decision Making: A Case…
https://mlg.eng.cam.ac.uk/adrian/WWW18-HumanPerceptions.pdf21 Mar 2024: We draw these latentproperties from the existing literature in social-economic-political-moral sciences, philosophy, and the law, as detailed below.I. ... To evaluate the model, we randomly split thedata into 50%/50% train/test folds five times, and -
The Case for Process Fairness in Learning:Feature Selection for ...
https://mlg.eng.cam.ac.uk/adrian/grgic.pdf21 Mar 2024: 3. Prior literature in social, economic, legal, and political sciences distinguishing between directdiscrimination and indirect discrimination makes similar observations as we do in this paper. ... For each of the classifiers, we also compute -
Beyond Distributive Fairness in Algorithmic Decision Making: Feature…
https://mlg.eng.cam.ac.uk/adrian/AAAI18-BeyondDistributiveFairness.pdf21 Mar 2024: 2011). For all reported results, we ran-domly split the data into 50%/50% train/test folds 5 times and re-port average statistics. ... In Univer-sity of Michigan Law & Economics Research Paper No. 16012.Ahmed, F.; Dickerson, J. -
Working Draft 1 Accountability of AI Under the Law: ...
https://mlg.eng.cam.ac.uk/adrian/SSRN-id3064761-Dec19.pdf21 Mar 2024: Working Draft. 1. Accountability of AI Under the Law: The Role of Explanation. Finale Doshi-Velez, Mason Kortz, Ryan Budish, Chris Bavitz, Sam Gershman, David O’Brien, Kate Scott, Stuart Shieber, James Waldo, David Weinberger, Adrian Weller,. -
Clamping Variables and Approximate Inference Adrian WellerColumbia…
https://mlg.eng.cam.ac.uk/adrian/NeurIPS14-clamp.pdf21 Mar 2024: 6. Test models were constructed as follows: For n variables, singleton potentials were drawn θi U[Tmax,Tmax]; edge weights were drawn Wij U[0,Wmax] for attractive models, or Wij ... In UAI, 2009. P. Milgrom. The envelope theorems. Department of Economics -
Orthogonal Estimation of Wasserstein Distances Mark Rowland∗1 Jiri…
https://mlg.eng.cam.ac.uk/adrian/AISTATS19-slicedwasserstein.pdf21 Mar 2024: physics (Jordan et al., 1998) and economics(Galichon, 2016), and are increasingly used in machinelearning (Arjovsky et al., 2017; Gulrajani et al., 2017;Peyré and Cuturi, 2018). ... 5.1 Distance estimation. We begin with a test bed of small-scale
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