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pro vyhledávání: '"Khan, Falaah Arif"'
Data missingness is a practical challenge of sustained interest to the scientific community. In this paper, we present Shades-of-Null, an evaluation suite for responsible missing value imputation. Our work is novel in two ways (i) we model realistic
Externí odkaz:
http://arxiv.org/abs/2409.07510
Autor:
Khan, Falaah Arif, Stoyanovich, Julia
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 belief that noise variance in large datasets in social domains tracks dem
Externí odkaz:
http://arxiv.org/abs/2302.08704
The error of an estimator can be decomposed into a (statistical) bias term, a variance term, and an irreducible noise term. When we do bias analysis, formally we are asking the question: "how good are the predictions?" The role of bias in the error d
Externí odkaz:
http://arxiv.org/abs/2302.04525
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. We contrast formal EO approaches that narrowly focus on fair contests
Externí odkaz:
http://arxiv.org/abs/2207.02912
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 of these propositions are inspired by, but inadequately grounded in, po
Externí odkaz:
http://arxiv.org/abs/2106.08259
Autor:
Gupta, Abhishek, Royer, Alexandrine, Wright, Connor, Khan, Falaah Arif, Heath, Victoria, Galinkin, Erick, Khurana, Ryan, Ganapini, Marianna Bergamaschi, Fancy, Muriam, Sweidan, Masa, Akif, Mo, Butalid, Renjie
The 3rd edition of the Montreal AI Ethics Institute's The State of AI Ethics captures the most relevant developments in AI Ethics since October 2020. It aims to help anyone, from machine learning experts to human rights activists and policymakers, qu
Externí odkaz:
http://arxiv.org/abs/2105.09059
Autor:
Gupta, Abhishek, Royer, Alexandrine, Heath, Victoria, Wright, Connor, Lanteigne, Camylle, Cohen, Allison, Ganapini, Marianna Bergamaschi, Fancy, Muriam, Galinkin, Erick, Khurana, Ryan, Akif, Mo, Butalid, Renjie, Khan, Falaah Arif, Sweidan, Masa, Balogh, Audrey
The 2nd edition of the Montreal AI Ethics Institute's The State of AI Ethics captures the most relevant developments in the field of AI Ethics since July 2020. This report aims to help anyone, from machine learning experts to human rights activists a
Externí odkaz:
http://arxiv.org/abs/2011.02787
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