10 research outputs found
Debiasing Community Detection: The Importance of Lowly-Connected Nodes
Community detection is an important task in social network analysis, allowing
us to identify and understand the communities within the social structures.
However, many community detection approaches either fail to assign low degree
(or lowly-connected) users to communities, or assign them to trivially small
communities that prevent them from being included in analysis. In this work, we
investigate how excluding these users can bias analysis results. We then
introduce an approach that is more inclusive for lowly-connected users by
incorporating them into larger groups. Experiments show that our approach
outperforms the existing state-of-the-art in terms of F1 and Jaccard similarity
scores while reducing the bias towards low-degree users
Exacerbating Algorithmic Bias through Fairness Attacks
Algorithmic fairness has attracted significant attention in recent years,
with many quantitative measures suggested for characterizing the fairness of
different machine learning algorithms. Despite this interest, the robustness of
those fairness measures with respect to an intentional adversarial attack has
not been properly addressed. Indeed, most adversarial machine learning has
focused on the impact of malicious attacks on the accuracy of the system,
without any regard to the system's fairness. We propose new types of data
poisoning attacks where an adversary intentionally targets the fairness of a
system. Specifically, we propose two families of attacks that target fairness
measures. In the anchoring attack, we skew the decision boundary by placing
poisoned points near specific target points to bias the outcome. In the
influence attack on fairness, we aim to maximize the covariance between the
sensitive attributes and the decision outcome and affect the fairness of the
model. We conduct extensive experiments that indicate the effectiveness of our
proposed attacks
FLIRT: Feedback Loop In-context Red Teaming
Warning: this paper contains content that may be inappropriate or offensive.
As generative models become available for public use in various applications,
testing and analyzing vulnerabilities of these models has become a priority.
Here we propose an automatic red teaming framework that evaluates a given model
and exposes its vulnerabilities against unsafe and inappropriate content
generation. Our framework uses in-context learning in a feedback loop to red
team models and trigger them into unsafe content generation. We propose
different in-context attack strategies to automatically learn effective and
diverse adversarial prompts for text-to-image models. Our experiments
demonstrate that compared to baseline approaches, our proposed strategy is
significantly more effective in exposing vulnerabilities in Stable Diffusion
(SD) model, even when the latter is enhanced with safety features. Furthermore,
we demonstrate that the proposed framework is effective for red teaming
text-to-text models, resulting in significantly higher toxic response
generation rate compared to previously reported numbers
Is the Elephant Flying? Resolving Ambiguities in Text-to-Image Generative Models
Natural language often contains ambiguities that can lead to
misinterpretation and miscommunication. While humans can handle ambiguities
effectively by asking clarifying questions and/or relying on contextual cues
and common-sense knowledge, resolving ambiguities can be notoriously hard for
machines. In this work, we study ambiguities that arise in text-to-image
generative models. We curate a benchmark dataset covering different types of
ambiguities that occur in these systems. We then propose a framework to
mitigate ambiguities in the prompts given to the systems by soliciting
clarifications from the user. Through automatic and human evaluations, we show
the effectiveness of our framework in generating more faithful images aligned
with human intention in the presence of ambiguities