The invisible power of fairness. How machine learning shapes democracy

Many machine learning systems make extensive use of large amounts of data regarding human behaviors. Several researchers have found various discriminatory practices related to the use of human-related machine learning systems, for example in the field of criminal justice, credit scoring and advertising. Fair machine learning is therefore emerging as a new field of study to mitigate biases that are inadvertently incorporated into algorithms. Data scientists and computer engineers are making various efforts to provide definitions of fairness. In this paper, we provide an overview of the most widespread definitions of fairness in the field of machine learning, arguing that the ideas highlighting each formalization are closely related to different ideas of justice and to different interpretations of democracy embedded in our culture. This work intends to analyze the definitions of fairness that have been proposed to date to interpret the underlying criteria and to relate them to different ideas of democracy.Comment: 12 pages, 1 figure, preprint version, submitted to The 32nd Canadian Conference on Artificial Intelligence that will take place in Kingston, Ontario, May 28 to May 31, 201

Academic freedom in Europe: reviewing UNESCO’s recommendation

This paper examines the compliance of universities in the European Union with the UNESCO Recommendation concerning the Status of Higher–Education Teaching Personnel, which deals primarily with protection for academic freedom. The paper briefly surveys the European genesis of the modern research university and academic freedom, before evaluating compliance with the UNESCO recommendation on institutional autonomy, academic freedom, university governance and tenure. Following from this, the paper examines the reasons for the generally low level of compliance with the UNESCO Recommendation within the EU states, and considers how such compliance could be improved

A threshold phenomenon for embeddings of H0mH^m_0 into Orlicz spaces

We consider a sequence of positive smooth critical points of the Adams-Moser-Trudinger embedding of $H^m_0$ into Orlicz spaces. We study its concentration-compactness behavior and show that if the sequence is not precompact, then the liminf of the $H^m_0$-norms of the functions is greater than or equal to a positive geometric constant.Comment: 14 Page

Implementation of Multidimensional Databases with Document-Oriented NoSQL

International audienceNoSQL (Not Only SQL) systems are becoming popular due to known advantages such as horizontal scalability and elasticity. In this paper, we study the implementation of data warehouses with document-oriented NoSQL systems. We propose mapping rules that transform the multidimensional data model to logical document-oriented models. We consider three different logical models and we use them to instantiate data warehouses. We focus on data loading, model-to-model conversion and OLAP cuboid computation

Spitzer Observations of Low Luminosity Isolated and Low Surface Brightness Galaxies

We examine the infrared properties of five low surface brightness galaxies (LSBGs) and compare them with related but higher surface brightness galaxies, using Spitzer Space Telescope images and spectra. All the LSBGs are detected in the 3.6 and 4.5um bands, representing the stellar population. All but one are detected at 5.8 and 8.0um, revealing emission from hot dust and aromatic molecules, though many are faint or point-like at these wavelengths. Detections of LSBGs at the far-infrared wavelengths, 24, 70, and 160um, are varied in morphology and brightness, with only two detections at 160um, resulting in highly varied spectral energy distributions. Consistent with previous expectations for these galaxies, we find that detectable dust components exist for only some LSBGs, with the strength of dust emission dependent on the existence of bright star forming regions. However, the far-infrared emission may be relatively weak compared with normal star-forming galaxies.Comment: 20 pages, 8 figures, accepted to Ap

Do low surface brightness galaxies have dense disks?

The disk masses of four low surface brightness galaxies (LSB) were estimated using marginal gravitational stability criterion and the stellar velocity dispersion data which were taken from Pizzella et al., 2008 [1]. The constructed mass models appear to be close to the models of maximal disk. The results show that the disks of LSB galaxies may be significantly more massive than it is usually accepted from their brightnesses. In this case their surface densities and masses appear to be rather typical for normal spirals. Otherwise, unlike the disks of many spiral galaxies, the LSB disks are dynamically overheated.Comment: 14 pages, 10 figures, submitted to Astronomy Report

Long-time discrete particle effects versus kinetic theory in the self-consistent single-wave model

The influence of the finite number N of particles coupled to a monochromatic wave in a collisionless plasma is investigated. For growth as well as damping of the wave, discrete particle numerical simulations show an N-dependent long time behavior resulting from the dynamics of individual particles. This behavior differs from the one due to the numerical errors incurred by Vlasov approaches. Trapping oscillations are crucial to long time dynamics, as the wave oscillations are controlled by the particle distribution inhomogeneities and the pulsating separatrix crossings drive the relaxation towards thermal equilibrium.Comment: 11 pages incl. 13 figs. Phys. Rev. E, in pres

The Invisible Power of Fairness. How Machine Learning Shapes Democracy

Many machine learning systems make extensive use of large amounts of data regarding human behaviors. Several researchers have found various discriminatory practices related to the use of human-related machine learning systems, for example in the field of criminal justice, credit scoring and advertising. Fair machine learning is therefore emerging as a new field of study to mitigate biases that are inadvertently incorporated into algorithms. Data scientists and computer engineers are making various efforts to provide definitions of fairness. In this paper, we provide an overview of the most widespread definitions of fairness in the field of machine learning, arguing that the ideas highlighting each formalization are closely related to different ideas of justice and to different interpretations of democracy embedded in our culture. This work intends to analyze the definitions of fairness that have been proposed to date to interpret the underlying criteria and to relate them to different ideas of democracy

Four Lessons in Versatility or How Query Languages Adapt to the Web

Exposing not only human-centered information, but machine-processable data on the Web is one of the commonalities of recent Web trends. It has enabled a new kind of applications and businesses where the data is used in ways not foreseen by the data providers. Yet this exposition has fractured the Web into islands of data, each in different Web formats: Some providers choose XML, others RDF, again others JSON or OWL, for their data, even in similar domains. This fracturing stifles innovation as application builders have to cope not only with one Web stack (e.g., XML technology) but with several ones, each of considerable complexity. With Xcerpt we have developed a rule- and pattern based query language that aims to give shield application builders from much of this complexity: In a single query language XML and RDF data can be accessed, processed, combined, and re-published. Though the need for combined access to XML and RDF data has been recognized in previous work (including the W3C’s GRDDL), our approach differs in four main aspects: (1) We provide a single language (rather than two separate or embedded languages), thus minimizing the conceptual overhead of dealing with disparate data formats. (2) Both the declarative (logic-based) and the operational semantics are unified in that they apply for querying XML and RDF in the same way. (3) We show that the resulting query language can be implemented reusing traditional database technology, if desirable. Nevertheless, we also give a unified evaluation approach based on interval labelings of graphs that is at least as fast as existing approaches for tree-shaped XML data, yet provides linear time and space querying also for many RDF graphs. We believe that Web query languages are the right tool for declarative data access in Web applications and that Xcerpt is a significant step towards a more convenient, yet highly efficient data access in a “Web of Data”