15 research outputs found
Mitigating Algorithmic Bias with Limited Annotations
Existing work on fairness modeling commonly assumes that sensitive attributes
for all instances are fully available, which may not be true in many real-world
applications due to the high cost of acquiring sensitive information. When
sensitive attributes are not disclosed or available, it is needed to manually
annotate a small part of the training data to mitigate bias. However, the
skewed distribution across different sensitive groups preserves the skewness of
the original dataset in the annotated subset, which leads to non-optimal bias
mitigation. To tackle this challenge, we propose Active Penalization Of
Discrimination (APOD), an interactive framework to guide the limited
annotations towards maximally eliminating the effect of algorithmic bias. The
proposed APOD integrates discrimination penalization with active instance
selection to efficiently utilize the limited annotation budget, and it is
theoretically proved to be capable of bounding the algorithmic bias. According
to the evaluation on five benchmark datasets, APOD outperforms the
state-of-the-arts baseline methods under the limited annotation budget, and
shows comparable performance to fully annotated bias mitigation, which
demonstrates that APOD could benefit real-world applications when sensitive
information is limited
Improving Fairness and Privacy in Selection Problems
Supervised learning models have been increasingly used for making decisions
about individuals in applications such as hiring, lending, and college
admission. These models may inherit pre-existing biases from training datasets
and discriminate against protected attributes (e.g., race or gender). In
addition to unfairness, privacy concerns also arise when the use of models
reveals sensitive personal information. Among various privacy notions,
differential privacy has become popular in recent years. In this work, we study
the possibility of using a differentially private exponential mechanism as a
post-processing step to improve both fairness and privacy of supervised
learning models. Unlike many existing works, we consider a scenario where a
supervised model is used to select a limited number of applicants as the number
of available positions is limited. This assumption is well-suited for various
scenarios, such as job application and college admission. We use ``equal
opportunity'' as the fairness notion and show that the exponential mechanisms
can make the decision-making process perfectly fair. Moreover, the experiments
on real-world datasets show that the exponential mechanism can improve both
privacy and fairness, with a slight decrease in accuracy compared to the model
without post-processing.Comment: This paper has been accepted for publication in the 35th AAAI
Conference on Artificial Intelligenc
On the Fairness ROAD: Robust Optimization for Adversarial Debiasing
In the field of algorithmic fairness, significant attention has been put on
group fairness criteria, such as Demographic Parity and Equalized Odds.
Nevertheless, these objectives, measured as global averages, have raised
concerns about persistent local disparities between sensitive groups. In this
work, we address the problem of local fairness, which ensures that the
predictor is unbiased not only in terms of expectations over the whole
population, but also within any subregion of the feature space, unknown at
training time. To enforce this objective, we introduce ROAD, a novel approach
that leverages the Distributionally Robust Optimization (DRO) framework within
a fair adversarial learning objective, where an adversary tries to infer the
sensitive attribute from the predictions. Using an instance-level re-weighting
strategy, ROAD is designed to prioritize inputs that are likely to be locally
unfair, i.e. where the adversary faces the least difficulty in reconstructing
the sensitive attribute. Numerical experiments demonstrate the effectiveness of
our method: it achieves Pareto dominance with respect to local fairness and
accuracy for a given global fairness level across three standard datasets, and
also enhances fairness generalization under distribution shift.Comment: 23 pages, 10 figure
FLEA: Provably Fair Multisource Learning from Unreliable Training Data
Fairness-aware learning aims at constructing classifiers that not only make
accurate predictions, but do not discriminate against specific groups. It is a
fast-growing area of machine learning with far-reaching societal impact.
However, existing fair learning methods are vulnerable to accidental or
malicious artifacts in the training data, which can cause them to unknowingly
produce unfair classifiers. In this work we address the problem of fair
learning from unreliable training data in the robust multisource setting, where
the available training data comes from multiple sources, a fraction of which
might be not representative of the true data distribution. We introduce FLEA, a
filtering-based algorithm that allows the learning system to identify and
suppress those data sources that would have a negative impact on fairness or
accuracy if they were used for training. We show the effectiveness of our
approach by a diverse range of experiments on multiple datasets. Additionally
we prove formally that, given enough data, FLEA protects the learner against
unreliable data as long as the fraction of affected data sources is less than
half