Search Results for author:
Found 13 papers, 6 papers with code
Persistent Classification: A New Approach to Stability of Data and Adversarial Examples
This article proposes a new framework for studying adversarial examples that does not depend directly on the distance to the decision boundary.
UnProjection: Leveraging Inverse-Projections for Visual Analytics of High-Dimensional Data
Projection techniques are often used to visualize high-dimensional data, allowing users to better understand the overall structure of multi-dimensional spaces on a 2D screen.
The ANTARES Astronomical Time-Domain Event Broker
We describe the Arizona-NOIRLab Temporal Analysis and Response to Events System (ANTARES), a software instrument designed to process large-scale streams of astronomical time-domain alerts.
Instrumentation and Methods for Astrophysics
Problems with Shapley-value-based explanations as feature importance measures
Game-theoretic formulations of feature importance have become popular as a way to "explain" machine learning models.
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.
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.
