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

Scott Neville

Charles T. Marx

Richard Lanas Phillips

Chitradeep Dutta Roy

Mohsen Abbasi

Jonathan P. Wilson

Danielle Ensign

Richard L. Phillips

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