101 research outputs found
FERMI: Fair Empirical Risk Minimization via Exponential R\'enyi Mutual Information
Despite the success of large-scale empirical risk minimization (ERM) at
achieving high accuracy across a variety of machine learning tasks, fair ERM is
hindered by the incompatibility of fairness constraints with stochastic
optimization. In this paper, we propose the fair empirical risk minimization
via exponential R\'enyi mutual information (FERMI) framework. FERMI is built on
a stochastic estimator for exponential R\'enyi mutual information (ERMI), an
information divergence measuring the degree of the dependence of predictions on
sensitive attributes. Theoretically, we show that ERMI upper bounds existing
popular fairness violation metrics, thus controlling ERMI provides guarantees
on other commonly used violations, such as . We derive an unbiased
estimator for ERMI, which we use to derive the FERMI algorithm. We prove that
FERMI converges for demographic parity, equalized odds, and equal opportunity
notions of fairness in stochastic optimization. Empirically, we show that FERMI
is amenable to large-scale problems with multiple (non-binary) sensitive
attributes and non-binary targets. Extensive experiments show that FERMI
achieves the most favorable tradeoffs between fairness violation and test
accuracy across all tested setups compared with state-of-the-art baselines for
demographic parity, equalized odds, equal opportunity. These benefits are
especially significant for non-binary classification with large sensitive sets
and small batch sizes, showcasing the effectiveness of the FERMI objective and
the developed stochastic algorithm for solving it.Comment: 29 page
A benchmark study on methods to ensure fair algorithmic decisions for credit scoring
The utility of machine learning in evaluating the creditworthiness of loan
applicants has been proofed since decades ago. However, automatic decisions may
lead to different treatments over groups or individuals, potentially causing
discrimination. This paper benchmarks 12 top bias mitigation methods discussing
their performance based on 5 different fairness metrics, accuracy achieved and
potential profits for the financial institutions. Our findings show the
difficulties in achieving fairness while preserving accuracy and profits.
Additionally, it highlights some of the best and worst performers and helps
bridging the gap between experimental machine learning and its industrial
application
Fair Supervised Learning with A Simple Random Sampler of Sensitive Attributes
As the data-driven decision process becomes dominating for industrial
applications, fairness-aware machine learning arouses great attention in
various areas. This work proposes fairness penalties learned by neural networks
with a simple random sampler of sensitive attributes for non-discriminatory
supervised learning. In contrast to many existing works that critically rely on
the discreteness of sensitive attributes and response variables, the proposed
penalty is able to handle versatile formats of the sensitive attributes, so it
is more extensively applicable in practice than many existing algorithms. This
penalty enables us to build a computationally efficient group-level
in-processing fairness-aware training framework. Empirical evidence shows that
our framework enjoys better utility and fairness measures on popular benchmark
data sets than competing methods. We also theoretically characterize estimation
errors and loss of utility of the proposed neural-penalized risk minimization
problem
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
Learning Optimal Fair Scoring Systems for Multi-Class Classification
Machine Learning models are increasingly used for decision making, in
particular in high-stakes applications such as credit scoring, medicine or
recidivism prediction. However, there are growing concerns about these models
with respect to their lack of interpretability and the undesirable biases they
can generate or reproduce. While the concepts of interpretability and fairness
have been extensively studied by the scientific community in recent years, few
works have tackled the general multi-class classification problem under
fairness constraints, and none of them proposes to generate fair and
interpretable models for multi-class classification. In this paper, we use
Mixed-Integer Linear Programming (MILP) techniques to produce inherently
interpretable scoring systems under sparsity and fairness constraints, for the
general multi-class classification setup. Our work generalizes the SLIM
(Supersparse Linear Integer Models) framework that was proposed by Rudin and
Ustun to learn optimal scoring systems for binary classification. The use of
MILP techniques allows for an easy integration of diverse operational
constraints (such as, but not restricted to, fairness or sparsity), but also
for the building of certifiably optimal models (or sub-optimal models with
bounded optimality gap)
Fast Model Debias with Machine Unlearning
Recent discoveries have revealed that deep neural networks might behave in a
biased manner in many real-world scenarios. For instance, deep networks trained
on a large-scale face recognition dataset CelebA tend to predict blonde hair
for females and black hair for males. Such biases not only jeopardize the
robustness of models but also perpetuate and amplify social biases, which is
especially concerning for automated decision-making processes in healthcare,
recruitment, etc., as they could exacerbate unfair economic and social
inequalities among different groups. Existing debiasing methods suffer from
high costs in bias labeling or model re-training, while also exhibiting a
deficiency in terms of elucidating the origins of biases within the model. To
this respect, we propose a fast model debiasing framework (FMD) which offers an
efficient approach to identify, evaluate and remove biases inherent in trained
models. The FMD identifies biased attributes through an explicit counterfactual
concept and quantifies the influence of data samples with influence functions.
Moreover, we design a machine unlearning-based strategy to efficiently and
effectively remove the bias in a trained model with a small counterfactual
dataset. Experiments on the Colored MNIST, CelebA, and Adult Income datasets
along with experiments with large language models demonstrate that our method
achieves superior or competing accuracies compared with state-of-the-art
methods while attaining significantly fewer biases and requiring much less
debiasing cost. Notably, our method requires only a small external dataset and
updating a minimal amount of model parameters, without the requirement of
access to training data that may be too large or unavailable in practice
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