267 citations · 710 across the 27 of their papers we have counts for
6 papers · 1 filter
Energy Usage Reports: Environmental awareness as part of algorithmic accountability
Kadan Lottick, Silvia Susai, Sorelle A. Friedler +1
The carbon footprint of algorithms must be measured and transparently reported so computer scientists can take an honest and active role in environmental sustainability. In this pa…
Fair Meta-Learning: Learning How to Learn Fairly
Dylan Slack, Sorelle Friedler, Emile Givental
Data sets for fairness relevant tasks can lack examples or be biased according to a specific label in a sensitive attribute. We demonstrate the usefulness of weight based meta-lear…
Fairness Warnings and Fair-MAML: Learning Fairly with Minimal Data
Dylan Slack, Sorelle Friedler, Emile Givental
Motivated by concerns surrounding the fairness effects of sharing and transferring fair machine learning tools, we propose two algorithms: Fairness Warnings and Fair-MAML. The firs…
Disentangling Influence: Using Disentangled Representations to Audit Model Predictions
Charles T. Marx, Richard Lanas Phillips, Sorelle A. Friedler +2
Motivated by the need to audit complex and black box models, there has been extensive research on quantifying how data features influence model predictions. Feature influence can b…
Assessing the Local Interpretability of Machine Learning Models
Dylan Slack, Sorelle A. Friedler, Carlos Scheidegger +1
The increasing adoption of machine learning tools has led to calls for accountability via model interpretability. But what does it mean for a machine learning model to be interpret…
Fairness in representation: quantifying stereotyping as a representational harm
Mohsen Abbasi, Sorelle A. Friedler, Carlos Scheidegger +1
While harms of allocation have been increasingly studied as part of the subfield of algorithmic fairness, harms of representation have received considerably less attention. In this…