1,161 research outputs found
Mitigating Discrimination in Insurance with Wasserstein Barycenters
The insurance industry is heavily reliant on predictions of risks based on
characteristics of potential customers. Although the use of said models is
common, researchers have long pointed out that such practices perpetuate
discrimination based on sensitive features such as gender or race. Given that
such discrimination can often be attributed to historical data biases, an
elimination or at least mitigation is desirable. With the shift from more
traditional models to machine-learning based predictions, calls for greater
mitigation have grown anew, as simply excluding sensitive variables in the
pricing process can be shown to be ineffective. In this article, we first
investigate why predictions are a necessity within the industry and why
correcting biases is not as straightforward as simply identifying a sensitive
variable. We then propose to ease the biases through the use of Wasserstein
barycenters instead of simple scaling. To demonstrate the effects and
effectiveness of the approach we employ it on real data and discuss its
implications
Fairness in Multi-Task Learning via Wasserstein Barycenters
Algorithmic Fairness is an established field in machine learning that aims to
reduce biases in data. Recent advances have proposed various methods to ensure
fairness in a univariate environment, where the goal is to de-bias a single
task. However, extending fairness to a multi-task setting, where more than one
objective is optimised using a shared representation, remains underexplored. To
bridge this gap, we develop a method that extends the definition of
\textit{Strong Demographic Parity} to multi-task learning using multi-marginal
Wasserstein barycenters. Our approach provides a closed form solution for the
optimal fair multi-task predictor including both regression and binary
classification tasks. We develop a data-driven estimation procedure for the
solution and run numerical experiments on both synthetic and real datasets. The
empirical results highlight the practical value of our post-processing
methodology in promoting fair decision-making
A Sequentially Fair Mechanism for Multiple Sensitive Attributes
In the standard use case of Algorithmic Fairness, the goal is to eliminate
the relationship between a sensitive variable and a corresponding score.
Throughout recent years, the scientific community has developed a host of
definitions and tools to solve this task, which work well in many practical
applications. However, the applicability and effectivity of these tools and
definitions becomes less straightfoward in the case of multiple sensitive
attributes. To tackle this issue, we propose a sequential framework, which
allows to progressively achieve fairness across a set of sensitive features. We
accomplish this by leveraging multi-marginal Wasserstein barycenters, which
extends the standard notion of Strong Demographic Parity to the case with
multiple sensitive characteristics. This method also provides a closed-form
solution for the optimal, sequentially fair predictor, permitting a clear
interpretation of inter-sensitive feature correlations. Our approach seamlessly
extends to approximate fairness, enveloping a framework accommodating the
trade-off between risk and unfairness. This extension permits a targeted
prioritization of fairness improvements for a specific attribute within a set
of sensitive attributes, allowing for a case specific adaptation. A data-driven
estimation procedure for the derived solution is developed, and comprehensive
numerical experiments are conducted on both synthetic and real datasets. Our
empirical findings decisively underscore the practical efficacy of our
post-processing approach in fostering fair decision-making
Addressing Fairness and Explainability in Image Classification Using Optimal Transport
Algorithmic Fairness and the explainability of potentially unfair outcomes
are crucial for establishing trust and accountability of Artificial
Intelligence systems in domains such as healthcare and policing. Though
significant advances have been made in each of the fields separately, achieving
explainability in fairness applications remains challenging, particularly so in
domains where deep neural networks are used. At the same time, ethical
data-mining has become ever more relevant, as it has been shown countless times
that fairness-unaware algorithms result in biased outcomes. Current approaches
focus on mitigating biases in the outcomes of the model, but few attempts have
been made to try to explain \emph{why} a model is biased. To bridge this gap,
we propose a comprehensive approach that leverages optimal transport theory to
uncover the causes and implications of biased regions in images, which easily
extends to tabular data as well. Through the use of Wasserstein barycenters, we
obtain scores that are independent of a sensitive variable but keep their
marginal orderings. This step ensures predictive accuracy but also helps us to
recover the regions most associated with the generation of the biases. Our
findings hold significant implications for the development of trustworthy and
unbiased AI systems, fostering transparency, accountability, and fairness in
critical decision-making scenarios across diverse domains
Parametric Fairness with Statistical Guarantees
Algorithmic fairness has gained prominence due to societal and regulatory
concerns about biases in Machine Learning models. Common group fairness metrics
like Equalized Odds for classification or Demographic Parity for both
classification and regression are widely used and a host of computationally
advantageous post-processing methods have been developed around them. However,
these metrics often limit users from incorporating domain knowledge. Despite
meeting traditional fairness criteria, they can obscure issues related to
intersectional fairness and even replicate unwanted intra-group biases in the
resulting fair solution. To avoid this narrow perspective, we extend the
concept of Demographic Parity to incorporate distributional properties in the
predictions, allowing expert knowledge to be used in the fair solution. We
illustrate the use of this new metric through a practical example of wages, and
develop a parametric method that efficiently addresses practical challenges
like limited training data and constraints on total spending, offering a robust
solution for real-life applications
Runtime Hardware Reconfiguration in Wireless Sensor Networks for Condition Monitoring
The integration of miniaturized heterogeneous electronic components has enabled the deployment of tiny sensing platforms empowered by wireless connectivity known as wireless sensor networks. Thanks to
an optimized duty-cycled activity, the energy consumption of these battery-powered devices can be reduced to a level where several years of operation is possible. However, the processing capability of currently available wireless sensor nodes does not scale well with the observation of phenomena requiring a high sampling resolution. The large amount of data generated by the sensors cannot be handled efficiently by low-power wireless communication protocols without a preliminary filtering of the information relevant for the application. For this purpose, energy-efficient, flexible, fast and accurate processing units are required to extract important features from the sensor data and relieve the operating system from computationally demanding tasks. Reconfigurable hardware is identified as a suitable technology to fulfill these requirements, balancing implementation
flexibility with performance and energy-efficiency.
While both static and dynamic power consumption of field programmable gate arrays has often been pointed out as prohibitive for very-low-power applications, recent programmable logic chips based on non-volatile memory appear as a potential solution overcoming this constraint. This thesis first verifies this assumption with the help of a modular sensor node built around a field programmable gate array based on Flash technology. Short and autonomous duty-cycled operation combined with hardware acceleration efficiently drop the energy consumption of the device in the considered context.
However, Flash-based devices suffer from restrictions such as long configuration times and limited resources, which reduce their suitability for complex processing tasks. A template of a dynamically
reconfigurable architecture built around coarse-grained reconfigurable function units is proposed in a second part of this work to overcome these issues. The module is conceived as an overlay of the sensor node FPGA increasing the implementation flexibility and introducing a standardized programming model. Mechanisms for virtual reconfiguration tailored for resource-constrained systems are introduced to minimize the overhead induced by this genericity.
The definition of this template architecture leaves room for design space exploration and application- specific customization. Nevertheless, this aspect must be supported by appropriate design tools which facilitate and automate the generation of low-level design files. For this purpose, a software tool is introduced to graphically configure the architecture and operation of the hardware accelerator. A middleware service is further integrated into the wireless sensor
network operating system to bridge the gap between the hardware and the design tools, enabling remote reprogramming and scheduling of the hardware functionality at runtime.
At last, this hardware and software toolchain is applied to real-world wireless sensor network deployments in the domain of condition monitoring. This category of applications often require the complex analysis of signals in the considered range of sampling frequencies such as vibrations or electrical currents, making the proposed system ideally suited for the implementation. The flexibility of the approach is demonstrated by taking examples with heterogeneous algorithmic
specifications. Different data processing tasks executed by the sensor node hardware accelerator are modified at runtime according to application requests
Bayesian Numerical Integration with Neural Networks
Bayesian probabilistic numerical methods for numerical integration offer
significant advantages over their non-Bayesian counterparts: they can encode
prior information about the integrand, and can quantify uncertainty over
estimates of an integral. However, the most popular algorithm in this class,
Bayesian quadrature, is based on Gaussian process models and is therefore
associated with a high computational cost. To improve scalability, we propose
an alternative approach based on Bayesian neural networks which we call
Bayesian Stein networks. The key ingredients are a neural network architecture
based on Stein operators, and an approximation of the Bayesian posterior based
on the Laplace approximation. We show that this leads to orders of magnitude
speed-ups on the popular Genz functions benchmark, and on challenging problems
arising in the Bayesian analysis of dynamical systems, and the prediction of
energy production for a large-scale wind farm
Comb-based WDM transmission at 10 Tbit/s using a DC-driven quantum-dash mode-locked laser diode
Chip-scale frequency comb generators have the potential to become key
building blocks of compact wavelength-division multiplexing (WDM) transceivers
in future metropolitan or campus-area networks. Among the various comb
generator concepts, quantum-dash (QD) mode-locked laser diodes (MLLD) stand out
as a particularly promising option, combining small footprint with simple
operation by a DC current and offering flat broadband comb spectra. However,
the data transmission performance achieved with QD-MLLD was so far limited by
strong phase noise of the individual comb tones, restricting experiments to
rather simple modulation formats such as quadrature phase shift keying (QPSK)
or requiring hard-ware-based compensation schemes. Here we demonstrate that
these limitations can be over-come by digital symbol-wise phase tracking
algorithms, avoiding any hardware-based phase-noise compensation. We
demonstrate 16QAM dual-polarization WDM transmission on 38 channels at an
aggregate net data rate of 10.68 Tbit/s over 75 km of standard single-mode
fiber. To the best of our knowledge, this corresponds to the highest data rate
achieved through a DC-driven chip-scale comb generator without any
hardware-based phase-noise reduction schemes
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