251 research outputs found

    Locating disparities in machine learning

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    Machine learning can provide predictions with disparate outcomes, in which subgroups of the population (e.g., defined by age, gender, or other sensitive attributes) are systematically disadvantaged. In order to comply with upcoming legislation, practitioners need to locate such disparate outcomes. However, previous literature typically detects disparities through statistical procedures for when the sensitive attribute is specified a priori. This limits applicability in real-world settings where datasets are high dimensional and, on top of that, sensitive attributes may be unknown. As a remedy, we propose a data-driven framework called Automatic Location of Disparities (ALD) which aims at locating disparities in machine learning. ALD meets several demands from industry: ALD (1) is applicable to arbitrary machine learning classifiers; (2) operates on different definitions of disparities (e.g., statistical parity or equalized odds); and (3) deals with both categorical and continuous predictors even if disparities arise from complex and multi-way interactions known as intersectionality (e. g., age above 60 and female). ALD produces interpretable audit reports as output. We demonstrate the effectiveness of ALD based on both synthetic and real-world datasets. As a result, we empower practitioners to effectively locate and mitigate disparities in machine learning algorithms, conduct algorithmic audits, and protect individuals from discrimination

    Quantum Instability of the Cauchy Horizon in Reissner-Nordstr\"om-deSitter Spacetime

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    In classical General Relativity, the values of fields on spacetime are uniquely determined by their values at an initial time within the domain of dependence of this initial data surface. However, it may occur that the spacetime under consideration extends beyond this domain of dependence, and fields, therefore, are not entirely determined by their initial data. This occurs, for example, in the well-known (maximally) extended Reissner-Nordstr\"om or Reissner-Nordstr\"om-deSitter (RNdS) spacetimes. The boundary of the region determined by the initial data is called the "Cauchy horizon." It is located inside the black hole in these spacetimes. The strong cosmic censorship conjecture asserts that the Cauchy horizon does not, in fact, exist in practice because the slightest perturbation (of the metric itself or the matter fields) will become singular there in a sufficiently catastrophic way that solutions cannot be extended beyond the Cauchy horizon. Thus, if strong cosmic censorship holds, the Cauchy horizon will be converted into a "final singularity," and determinism will hold. Recently, however, it has been found that, classically this is not the case in RNdS spacetimes in a certain range of mass, charge, and cosmological constant. In this paper, we consider a quantum scalar field in RNdS spacetime and show that quantum theory comes to the rescue of strong cosmic censorship. We find that for any state that is nonsingular (i.e., Hadamard) within the domain of dependence, the expected stress-tensor blows up with affine parameter, VV, along a radial null geodesic transverse to the Cauchy horizon as TVV∌C/V2T_{VV} \sim C/V^2 with CC independent of the state and C≠0C \neq 0 generically in RNdS spacetimes. This divergence is stronger than in the classical theory and should be sufficient to convert the Cauchy horizon into a strong curvature singularity.Comment: 50 pages, abstract truncated due to arXiv length restriction. v2: minor correction

    Quantum instability of the Cauchy horizon in Reissner–Nordström–deSitter spacetime

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    In classical general relativity, the values of elds on spacetime are uniquely determined by their values at an initial time within the domain of dependence of this initial data surface. However, it may occur that the spacetime under consideration extends beyond this domain of dependence, and elds, therefore, are not entirely determined by their initial data. This occurs, for example, in the well-known (maximally) extended Reissner–Nordström or Reissner–Nordström–deSitter (RNdS) spacetimes. The boundary of the region determined by the initial data is called the ‘Cauchy horizon.’ It is located inside the black hole in these spacetimes. The strong cosmic censorship conjecture asserts that the Cauchy horizon does not, in fact, exist in practice because the slightest perturbation (of the metric itself or the matter elds) will become singular there in a sufciently catastrophic way that solutions cannot be extended beyond the Cauchy horizon. Thus, if strong cosmic censorship holds, the Cauchy horizon will be converted into a ‘nal singularity,’ and determinism will hold. Recently, however, it has been found that, classically this is not the case in RNdS spacetimes in a certain range of mass, charge, and cosmological constant. In this paper, we consider a quantum scalar eld in RNdS spacetime and show that quantum theory comes to the rescue of strong cosmic censorship. We nd that for any state that is nonsingular (i.e., Hadamard) within the domain of dependence, the expected stress-tensor blows up with afne parameter, V, along a radial null geodesic transverse to the Cauchy horizon as TVV ∌ C/V 2 with C independent of the state and C 6= 0 generically in RNdS spacetimes. This divergence is stronger than in the classical theory and should be sufcient to convert the Cauchy horizon into a singularity through which the spacetime cannot be extended as a (weak) solution of the semiclassical Einstein equation. This behavior is expected to be quite general, although it is possible to have C = 0 in certain special cases, such as the BTZ black hol

    The Cost of Fairness in AI: Evidence from E-Commerce

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    Contemporary information systems make widespread use of artificial intelligence (AI). While AI offers various benefits, it can also be subject to systematic errors, whereby people from certain groups (defined by gender, age, or other sensitive attributes) experience disparate outcomes. In many AI applications, disparate outcomes confront businesses and organizations with legal and reputational risks. To address these, technologies for so-called “AI fairness” have been developed, by which AI is adapted such that mathematical constraints for fairness are fulfilled. However, the financial costs of AI fairness are unclear. Therefore, the authors develop AI fairness for a real-world use case from e-commerce, where coupons are allocated according to clickstream sessions. In their setting, the authors find that AI fairness successfully manages to adhere to fairness requirements, while reducing the overall prediction performance only slightly. However, they find that AI fairness also results in an increase in financial cost. Thus, in this way the paper’s findings contribute to designing information systems on the basis of AI fairness

    Datenmanagement im Rahmen eines Transregios an der UniversitĂ€t Leipzig, der TU Chemnitz und dem Leibniz Institut fĂŒr OberflĂ€chenmodifizierung e.V. (IOM)

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    Posterbeitrag, welcher das Datenmanagement im Rahmen eines geplanten Transregios zwischen der UniversitĂ€t Leipzig, der TU Chemnitz und des Leibniz Instituts fĂŒr OberflĂ€chenmodifizierung skizziert

    Theory of real space imaging of Fermi surfaces

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    A scanning tunneling microscope can be used to visualize in real space Fermi surfaces with buried impurities far below substrates acting as local probes. A theory describing this feature is developed based on the stationary phase approximation. It is demonstrated how a Fermi surface of a material acts as a mirror focusing electrons that scatter at hidden impurities.Comment: 10 pages, 4 figure

    Imaging the Cosmic Matter Distribution using Gravitational Lensing of Pregalactic HI

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    21-cm emission from neutral hydrogen during and before the epoch of cosmic reionisation is gravitationally lensed by material at all lower redshifts. Low-frequency radio observations of this emission can be used to reconstruct the projected mass distribution of foreground material, both light and dark. We compare the potential imaging capabilities of such 21-cm lensing with those of future galaxy lensing surveys. We use the Millennium Simulation to simulate large-area maps of the lensing convergence with the noise, resolution and redshift-weighting achievable with a variety of idealised observation programmes. We find that the signal-to-noise of 21-cm lens maps can far exceed that of any map made using galaxy lensing. If the irreducible noise limit can be reached with a sufficiently large radio telescope, the projected convergence map provides a high-fidelity image of the true matter distribution, allowing the dark matter halos of individual galaxies to be viewed directly, and giving a wealth of statistical and morphological information about the relative distributions of mass and light. For instrumental designs like that planned for the Square Kilometer Array (SKA), high-fidelity mass imaging may be possible near the resolution limit of the core array of the telescope.Comment: version accepted for publication in MNRAS (reduced-resolution figures
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