Sorelle A. Friedler

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Haverford College

Energy Usage Reports: Environmental awareness as part of algorithmic accountability

The carbon footprint of algorithms must be measured and transparently re...
11/19/2019 ∙ by Kadan Lottick, et al. ∙ 0 ∙ share

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Disentangling Influence: Using Disentangled Representations to Audit Model Predictions

Motivated by the need to audit complex and black box models, there has b...
06/20/2019 ∙ by Charles T. Marx, et al. ∙ 8 ∙ share

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Assessing the Local Interpretability of Machine Learning Models

The increasing adoption of machine learning tools has led to calls for a...
02/09/2019 ∙ by Sorelle A. Friedler, et al. ∙ 0 ∙ share

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Fairness in representation: quantifying stereotyping as a representational harm

While harms of allocation have been increasingly studied as part of the ...
01/28/2019 ∙ by Mohsen Abbasi, et al. ∙ 8 ∙ share

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A comparative study of fairness-enhancing interventions in machine learning

Computers are increasingly used to make decisions that have significant ...
02/13/2018 ∙ by Sorelle A. Friedler, et al. ∙ 0 ∙ share

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Interpretable Active Learning

Active learning has long been a topic of study in machine learning. Howe...
07/31/2017 ∙ by Richard L. Phillips, et al. ∙ 0 ∙ share

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Runaway Feedback Loops in Predictive Policing

Predictive policing systems are increasingly used to determine how to al...
06/29/2017 ∙ by Danielle Ensign, et al. ∙ 0 ∙ share

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On the (im)possibility of fairness

What does it mean for an algorithm to be fair? Different papers use diff...
09/23/2016 ∙ by Sorelle A. Friedler, et al. ∙ 0 ∙ share

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Auditing Black-box Models for Indirect Influence

Data-trained predictive models see widespread use, but for the most part...
02/23/2016 ∙ by Philip Adler, et al. ∙ 0 ∙ share

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Sorelle A. Friedler

Featured Co-authors

Suresh Venkatasubramanian

Carlos Scheidegger

Dylan Slack

Mohsen Abbasi

Chitradeep Dutta Roy

Charles T. Marx

Gabriel Rybeck

Sonam Choudhary

Philip Adler

Casey Falk

Brandon Smith

Energy Usage Reports: Environmental awareness as part of algorithmic accountability

Disentangling Influence: Using Disentangled Representations to Audit Model Predictions

Assessing the Local Interpretability of Machine Learning Models

Fairness in representation: quantifying stereotyping as a representational harm

A comparative study of fairness-enhancing interventions in machine learning

Interpretable Active Learning

Runaway Feedback Loops in Predictive Policing

On the (im)possibility of fairness

Auditing Black-box Models for Indirect Influence