Search Results for author:
Found 11 papers, 7 papers with code
Measuring and mitigating voting access disparities: a study of race and polling locations in Florida and North Carolina
In this paper, we quantify access to polling locations, developing a methodology for the calibrated measurement of racial disparities in polling location "load" and distance to polling locations.
Information access representations and social capital in networks
Social network position confers power and social capital.
Social and Information Networks
Energy Usage Reports: Environmental awareness as part of algorithmic accountability
The carbon footprint of algorithms must be measured and transparently reported so computer scientists can take an honest and active role in environmental sustainability.
Disentangling Influence: Using Disentangled Representations to Audit Model Predictions
Specifically, we show that disentangled representations provide a mechanism to identify proxy features in the dataset, while allowing an explicit computation of feature influence on either individual outcomes or aggregate-level outcomes.
Assessing the Local Interpretability of Machine Learning Models
Through a user study with 1, 000 participants, we test whether humans perform well on tasks that mimic the definitions of simulatability and "what if" local explainability on models that are typically considered locally interpretable.
Fairness in representation: quantifying stereotyping as a representational harm
While harms of allocation have been increasingly studied as part of the subfield of algorithmic fairness, harms of representation have received considerably less attention.
A comparative study of fairness-enhancing interventions in machine learning
Concretely, we present the results of an open benchmark we have developed that lets us compare a number of different algorithms under a variety of fairness measures, and a large number of existing datasets.
Interpretable Active Learning
We demonstrate how LIME can be used to generate locally faithful explanations for an active learning strategy, and how these explanations can be used to understand how different models and datasets explore a problem space over time.
Runaway Feedback Loops in Predictive Policing
Predictive policing systems are increasingly used to determine how to allocate police across a city in order to best prevent crime.
On the (im)possibility of fairness
We show that in order to prove desirable properties of the entire decision-making process, different mechanisms for fairness require different assumptions about the nature of the mapping from construct space to decision space.
Auditing Black-box Models for Indirect Influence
It is therefore hard to acquire a deeper understanding of model behavior, and in particular how different features influence the model prediction.
