3,927 research outputs found
AI for social good: unlocking the opportunity for positive impact
Advances in machine learning (ML) and artificial intelligence (AI) present an opportunity to build better tools and solutions to help address some of the world’s most pressing challenges, and deliver positive social impact in accordance with the priorities outlined in the United Nations’ 17 Sustainable Development Goals (SDGs). The AI for Social Good (AI4SG) movement aims to establish interdisciplinary partnerships centred around AI applications towards SDGs. We provide a set of guidelines for establishing successful long-term collaborations between AI researchers and application-domain experts, relate them to existing AI4SG projects and identify key opportunities for future AI applications targeted towards social good
Proportionally Representative Clustering
In recent years, there has been a surge in effort to formalize notions of
fairness in machine learning. We focus on clustering -- one of the fundamental
tasks in unsupervised machine learning. We propose a new axiom ``proportional
representation fairness'' (PRF) that is designed for clustering problems where
the selection of centroids reflects the distribution of data points and how
tightly they are clustered together. Our fairness concept is not satisfied by
existing fair clustering algorithms. We design efficient algorithms to achieve
PRF both for unconstrained and discrete clustering problems. Our algorithm for
the unconstrained setting is also the first known polynomial-time approximation
algorithm for the well-studied Proportional Fairness (PF) axiom (Chen, Fain,
Lyu, and Munagala, ICML, 2019). Our algorithm for the discrete setting also
matches the best known approximation factor for PF.Comment: Revised version includes a new author (Jeremy Vollen) and new
results: Our algorithm for the unconstrained setting is also the first known
polynomial-time approximation algorithm for the well-studied Proportional
Fairness (PF) axiom (Chen, Fain, Lyu, and Munagala, ICML, 2019). Our
algorithm for the discrete setting also matches the best known approximation
factor for P
Studying the Effects of Sex-related Differences on Brain Age Prediction using brain MR Imaging
While utilizing machine learning models, one of the most crucial aspects is
how bias and fairness affect model outcomes for diverse demographics. This
becomes especially relevant in the context of machine learning for medical
imaging applications as these models are increasingly being used for diagnosis
and treatment planning. In this paper, we study biases related to sex when
developing a machine learning model based on brain magnetic resonance images
(MRI). We investigate the effects of sex by performing brain age prediction
considering different experimental designs: model trained using only female
subjects, only male subjects and a balanced dataset. We also perform evaluation
on multiple MRI datasets (Calgary-Campinas(CC359) and CamCAN) to assess the
generalization capability of the proposed models. We found disparities in the
performance of brain age prediction models when trained on distinct sex
subgroups and datasets, in both final predictions and decision making (assessed
using interpretability models). Our results demonstrated variations in model
generalizability across sex-specific subgroups, suggesting potential biases in
models trained on unbalanced datasets. This underlines the critical role of
careful experimental design in generating fair and reliable outcomes
Bursting the Burden Bubble? An Assessment of Sharma et al.'s Counterfactual-based Fairness Metric
Machine learning has seen an increase in negative publicity in recent years,
due to biased, unfair, and uninterpretable models. There is a rising interest
in making machine learning models more fair for unprivileged communities, such
as women or people of color. Metrics are needed to evaluate the fairness of a
model. A novel metric for evaluating fairness between groups is Burden, which
uses counterfactuals to approximate the average distance of negatively
classified individuals in a group to the decision boundary of the model. The
goal of this study is to compare Burden to statistical parity, a well-known
fairness metric, and discover Burden's advantages and disadvantages. We do this
by calculating the Burden and statistical parity of a sensitive attribute in
three datasets: two synthetic datasets are created to display differences
between the two metrics, and one real-world dataset is used. We show that
Burden can show unfairness where statistical parity can not, and that the two
metrics can even disagree on which group is treated unfairly. We conclude that
Burden is a valuable metric, but does not replace statistical parity: it rather
is valuable to use both.Comment: 11 pages, 3 figures, conference: BNAIC/BeNeLearn
(https://bnaic2022.uantwerpen.be/accepted-submissions/
Opening the black box: a primer for anti-discrimination
The pervasive adoption of Artificial Intelligence (AI) models in the modern
information society, requires counterbalancing the growing decision power
demanded to AI models with risk assessment methodologies. In this paper, we
consider the risk of discriminatory decisions and review approaches for
discovering discrimination and for designing fair AI models. We highlight the
tight relations between discrimination discovery and explainable AI, with the
latter being a more general approach for understanding the behavior of black
boxes
- …