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Energy Usage Reports: Environmental awareness as part of algorithmic accountability
The carbon footprint of algorithms must be measured and transparently re...
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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...
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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...
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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 ...
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A comparative study of fairness-enhancing interventions in machine learning
Computers are increasingly used to make decisions that have significant ...
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Interpretable Active Learning
Active learning has long been a topic of study in machine learning. Howe...
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Runaway Feedback Loops in Predictive Policing
Predictive policing systems are increasingly used to determine how to al...
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On the (im)possibility of fairness
What does it mean for an algorithm to be fair? Different papers use diff...
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Auditing Black-box Models for Indirect Influence
Data-trained predictive models see widespread use, but for the most part...
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