A crucial but often neglected aspect of algorithmic fairness is the question of how we justify enforcing a certain fairness metric from a moral perspective. When fairness metrics are proposed, they are typically argued for by highlighting their mathematical properties. Rarely are the moral assumptions beneath the metric explained. Our aim in this paper is to consider the moral aspects associated with the statistical fairness criterion of independence (statistical parity). To this end, we consider previous work, which discusses the two worldviews "What You See Is What You Get" (WYSIWYG) and "We're All Equal" (WAE) and by doing so provides some guidance for clarifying the possible assumptions in the design of algorithms. We present an extension of this work, which centers on morality. The most natural moral extension is that independence needs to be fulfilled if and only if differences in predictive features (e.g. high school grades and standardized test scores are predictive of performance at university) between socio-demographic groups are caused by unjust social disparities or measurement errors. Through two counterexamples, we demonstrate that this extension is not universally true. This means that the question of whether independence should be used or not cannot be satisfactorily answered by only considering the justness of differences in the predictive features. CCS CONCEPTS• Applied computing → Law, social and behavioral sciences;• Computing methodologies → Machine learning; • Social and professional topics → Socio-technical systems.
A general framework for the physical description of partial discharge (PD) processes is presented that holds for different types of PD causing defects. A PD process is treated as a stochastic process consisting of short duration discharges (point-like in time) and charge carrier drift/recombination intervals between these discharges. It is determined by few basic physical parameters and, in a stochastic process framework, can be described in a closed form by a master equation. Since usually only the fast discharges can be measured as PD signals, a restricted possibility of observing a PD process results. The link between the stochastic process and observable quantities is derived. A specific type of measurements is reported, the so-called phase-resolved partial discharge (PRPD) patterns. Here the total charge transferred during a discharge and the time or alternating current phase at which the discharge occurs are measured. Thus each discharge event is described by the two quantities, charge and phase angle. The modelling of the observation process is explicitly derived for this case. However, the used method can easily be generalized to other types of PD measurements. The proposed approach yields new possibilities for the interpretation and analysis of PD patterns. Features of PD patterns can be derived analytically from the process parameters. Conversely, quantitative information about the discharge physics can be gained from measured patterns. Some limiting cases of model parameter values leading to typical pattern features are discussed explicitly. Examples are presented that demonstrate the applicability of the model for three different discharge types (internal discharge in a gas-filled void, surface discharge in oil, corona in air).
A method is presented for the determination of physical discharge parameters for partial discharges (PDs) of voids in solid insulation. Based on a recently developed stochastic theory of PD processes, a statistical analysis of a measured phase-resolved partial discharge (PRPD) pattern allows the determination of the relevant physical parameters like first electron availability or decay time constants for deployed charge carriers. These parameters can be estimated directly from the measured patterns without the need of performing simulations. Furthermore, error bounds for the parameter values can be given.The parameter estimation algorithm is based on the analysis of a contiguous region of the PRPD pattern where this region can be chosen nearly arbitrarily. Thus, even patterns with several active PD defects or patterns which are corrupted by noise can be analysed.The method is applied to a sequence of patterns of a void in epoxy resin. The change in first electron availability in the course of a day can be determined quantitatively from the data while the other physical parameters remain constant.
Abstract-This paper introduces a new approach for rational macromodeling of multiport devices that ensures high accuracy with arbitrary terminal conditions. This is achieved by reformulating the vector fitting (VF) technique to focus on eigenpairs rather than matrix elements. By choosing the least squares (LS) weighting equal to the inverse of the eigenvalue magnitude, the modal components are fitted with a relative accuracy criterion. The resulting modal vector fitting (MVF) method is shown to give a major improvement in accuracy for cases with a high ratio between the largest and smallest eigenvalue, although it is computationally more costly than VF. It is also shown how to utilize the impedance characteristics of the adjacent network in the fitting process. The application of MVF is demonstrated for a two-conductor stripline, a coaxial cable, and a transformer measurement. We also show a simplified procedure which achieves similar results as MVF if the admittance matrix can be diagonalized by a constant transformation matrix. The extracted model is finally subjected to passivity enforcement by the modal perturbation method, which makes use of a similar LS formulation as MVF for the constrained optimization problem.
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