4 research outputs found
Fairness through Aleatoric Uncertainty
We propose a simple yet effective solution to tackle the often-competing
goals of fairness and utility in classification tasks. While fairness ensures
that the model's predictions are unbiased and do not discriminate against any
particular group or individual, utility focuses on maximizing the model's
predictive performance. This work introduces the idea of leveraging aleatoric
uncertainty (e.g., data ambiguity) to improve the fairness-utility trade-off.
Our central hypothesis is that aleatoric uncertainty is a key factor for
algorithmic fairness and samples with low aleatoric uncertainty are modeled
more accurately and fairly than those with high aleatoric uncertainty. We then
propose a principled model to improve fairness when aleatoric uncertainty is
high and improve utility elsewhere. Our approach first intervenes in the data
distribution to better decouple aleatoric uncertainty and epistemic
uncertainty. It then introduces a fairness-utility bi-objective loss defined
based on the estimated aleatoric uncertainty. Our approach is theoretically
guaranteed to improve the fairness-utility trade-off. Experimental results on
both tabular and image datasets show that the proposed approach outperforms
state-of-the-art methods w.r.t. the fairness-utility trade-off and w.r.t. both
group and individual fairness metrics. This work presents a fresh perspective
on the trade-off between utility and algorithmic fairness and opens a key
avenue for the potential of using prediction uncertainty in fair machine
learning
Exploring Musical, Lyrical, and Network Dimensions of Music Sharing Among Depression Individuals
Depression has emerged as a significant mental health concern due to a
variety of factors, reflecting broader societal and individual challenges.
Within the digital era, social media has become an important platform for
individuals navigating through depression, enabling them to express their
emotional and mental states through various mediums, notably music.
Specifically, their music preferences, manifested through sharing practices,
inadvertently offer a glimpse into their psychological and emotional
landscapes. This work seeks to study the differences in music preferences
between individuals diagnosed with depression and non-diagnosed individuals,
exploring numerous facets of music, including musical features, lyrics, and
musical networks. The music preferences of individuals with depression through
music sharing on social media, reveal notable differences in musical features
and topics and language use of lyrics compared to non-depressed individuals. We
find the network information enhances understanding of the link between music
listening patterns. The result highlights a potential echo-chamber effect,
where depression individual's musical choices may inadvertently perpetuate
depressive moods and emotions. In sum, this study underscores the significance
of examining music's various aspects to grasp its relationship with mental
health, offering insights for personalized music interventions and
recommendation algorithms that could benefit individuals with depression.Comment: arXiv admin note: text overlap with arXiv:2007.03137,
arXiv:2205.03459 by other author
Distributional Shift Adaptation using Domain-Specific Features
Machine learning algorithms typically assume that the training and test
samples come from the same distributions, i.e., in-distribution. However, in
open-world scenarios, streaming big data can be Out-Of-Distribution (OOD),
rendering these algorithms ineffective. Prior solutions to the OOD challenge
seek to identify invariant features across different training domains. The
underlying assumption is that these invariant features should also work
reasonably well in the unlabeled target domain. By contrast, this work is
interested in the domain-specific features that include both invariant features
and features unique to the target domain. We propose a simple yet effective
approach that relies on correlations in general regardless of whether the
features are invariant or not. Our approach uses the most confidently predicted
samples identified by an OOD base model (teacher model) to train a new model
(student model) that effectively adapts to the target domain. Empirical
evaluations on benchmark datasets show that the performance is improved over
the SOTA by ~10-20
Exploring Platform Migration Patterns between Twitter and Mastodon: A User Behavior Study
A recent surge of users migrating from Twitter to alternative platforms, such
as Mastodon, raised questions regarding what migration patterns are, how
different platforms impact user behaviors, and how migrated users settle in the
migration process. In this study, we elaborate how we investigate these
questions by collecting data over 10,000 users who migrated from Twitter to
Mastodon within the first ten weeks following Elon Musk's acquisition of
Twitter. Our research is structured in three primary steps. First, we develop
algorithms to extract and analyze migration patters. Second, by leveraging
behavioral analysis, we examine the distinct architectures of Twitter and
Mastodon to learn how different platforms shape user behaviors on each
platform. Last, we determine how particular behavioral factors influence users
to stay on Mastodon. We share our findings of user migration, insights, and
lessons learned from the user behavior study