Explainable ai: A review of machine learning interpretability methods
Recent advances in artificial intelligence (AI) have led to its widespread industrial adoption,
with machine learning systems demonstrating superhuman performance in a significant …
with machine learning systems demonstrating superhuman performance in a significant …
Trustworthy AI: From principles to practices
The rapid development of Artificial Intelligence (AI) technology has enabled the deployment
of various systems based on it. However, many current AI systems are found vulnerable to …
of various systems based on it. However, many current AI systems are found vulnerable to …
Bias mitigation for machine learning classifiers: A comprehensive survey
This article provides a comprehensive survey of bias mitigation methods for achieving
fairness in Machine Learning (ML) models. We collect a total of 341 publications concerning …
fairness in Machine Learning (ML) models. We collect a total of 341 publications concerning …
Extremely simple activation shaping for out-of-distribution detection
The separation between training and deployment of machine learning models implies that
not all scenarios encountered in deployment can be anticipated during training, and …
not all scenarios encountered in deployment can be anticipated during training, and …
Assessing social and intersectional biases in contextualized word representations
Social bias in machine learning has drawn significant attention, with work ranging from
demonstrations of bias in a multitude of applications, curating definitions of fairness for …
demonstrations of bias in a multitude of applications, curating definitions of fairness for …
Fair algorithms for clustering
S Bera, D Chakrabarty, N Flores… - Advances in Neural …, 2019 - proceedings.neurips.cc
We study the problem of finding low-cost {\em fair clusterings} in data where each data point
may belong to many protected groups. Our work significantly generalizes the seminal work …
may belong to many protected groups. Our work significantly generalizes the seminal work …
Fairness in credit scoring: Assessment, implementation and profit implications
N Kozodoi, J Jacob, S Lessmann - European Journal of Operational …, 2022 - Elsevier
The rise of algorithmic decision-making has spawned much research on fair machine
learning (ML). Financial institutions use ML for building risk scorecards that support a range …
learning (ML). Financial institutions use ML for building risk scorecards that support a range …
WEIRD FAccTs: How Western, Educated, Industrialized, Rich, and Democratic is FAccT?
Studies conducted on Western, Educated, Industrialized, Rich, and Democratic (WEIRD)
samples are considered atypical of the world's population and may not accurately represent …
samples are considered atypical of the world's population and may not accurately represent …
Is there a trade-off between fairness and accuracy? a perspective using mismatched hypothesis testing
A trade-off between accuracy and fairness is almost taken as a given in the existing literature
on fairness in machine learning. Yet, it is not preordained that accuracy should decrease …
on fairness in machine learning. Yet, it is not preordained that accuracy should decrease …