Full UPF3B function is critical for neuronal differentiation of neural stem cells

Acknowledgments We thank Fred H Gage (Salk Institute, La Jolla, CA, USA) for HCN-A94 cells and Niels Gehring (University of Cologne, Germany) for constructs. We gratefully acknowledge Tenovus Scotland (Project Grant G11-06), Moonlight Prowl (FS) and the Saudi Arabian Ministry of Higher Education via King Abdullah Program for Scholarships for support (TA). JA is supported by a PhD studentship from Medical Research Scotland (PhD-654-2012) and Dundee Cell Products.Peer reviewedPublisher PD

Algorithmic fairness through group parities? The case of COMPAS-SAPMOC

Machine learning classifiers are increasingly used to inform, or even make, decisions significantly affecting human lives. Fairness concerns have spawned a number of contributions aimed at both identifying and addressing unfairness in algorithmic decision-making. This paper critically discusses the adoption of group-parity criteria (e.g., demographic parity, equality of opportunity, treatment equality) as fairness standards. To this end, we evaluate the use of machine learning methods relative to different steps of the decision-making process: assigning a predictive score, linking a classification to the score, and adopting decisions based on the classification. Throughout our inquiry we use the COMPAS system, complemented by a radical simplification of it (our SAPMOC I and SAPMOC II models), as our running examples. Through these examples, we show how a system that is equally accurate for different groups may fail to comply with group-parity standards, owing to different base rates in the population. We discuss the general properties of the statistics determining the satisfaction of group-parity criteria and levels of accuracy. Using the distinction between scoring, classifying, and deciding, we argue that equalisation of classifications/decisions between groups can be achieved thorough group-dependent thresholding. We discuss contexts in which this approach may be meaningful and useful in pursuing policy objectives. We claim that the implementation of group-parity standards should be left to competent human decision-makers, under appropriate scrutiny, since it involves discretionary value-based political choices. Accordingly, predictive systems should be designed in such a way that relevant policy goals can be transparently implemented. Our paper presents three main contributions: (1) it addresses a complex predictive system through the lens of simplified toy models; (2) it argues for selective policy interventions on the different steps of automated decision-making; (3) it points to the limited significance of statistical notions of fairness to achieve social goals

Consent to Targeted Advertising

Targeted advertising in digital markets involves multiple actors collecting, exchanging, and processing personal data for the purpose of capturing users’ attention in online environments. This ecosystem has given rise to considerable adverse effects on individuals and society, resulting from mass surveillance, the manipulation of choices and opinions, and the spread of addictive or fake messages. Against this background, this article critically discusses the regulation of consent in online targeted advertising. To this end, we review EU laws and proposals and consider the extent to which a requirement of informed consent may provide effective consumer protection. On the basis of such an analysis, we make suggestions for possible avenues that may be pursued

Argumentation and Defeasible Reasoning in the Law

Different formalisms for defeasible reasoning have been used to represent knowledge and reason in the legal field. In this work, we provide an overview of the following logic-based approaches to defeasible reasoning: defeasible logic, Answer Set Programming, ABA+, ASPIC+, and DeLP. We compare features of these approaches under three perspectives: the logical model (knowledge representation), the method (computational mechanisms), and the technology (available software resources). On top of that, two real examples in the legal domain are designed and implemented in ASPIC+ to showcase the benefit of an argumentation approach in real-world domains. The CrossJustice and Interlex projects are taken as a testbed, and experiments are conducted with the Arg2P technology

Defeasible Systems in Legal Reasoning: A Comparative Assessment

Different formalisms for defeasible reasoning have been used to represent legal knowledge and to reason with it. In this work, we provide an overview of the following logic-based approaches to defeasible reasoning: Defeasible Logic, Answer Set Programming, ABA+, ASPIC+, and DeLP. We compare features of these approaches from three perspectives: the logical model (knowledge representation), the method (computational mechanisms), and the technology (available software). On this basis, we identify and apply criteria for assessing their suitability for legal applications. We discuss the different approaches through a legal running example

Unsupervised Factor Extraction from Pretrial Detention Decisions by Italian and Brazilian Supreme Courts

Pretrial detention is a debated and controversial measure since it is an exception to the principle of the presumption of innocence. To determine whether and to what extent legal systems make exces- sive use of pretrial detention, an empirical analysis of judicial practice is needed. The paper presents some preliminary results of experimental re- search aimed at identifying the relevant factors on the basis of which Ital- ian and Brazilian Supreme Courts impose the measure. To analyze and extract the relevant predictive-features, we rely on unsupervised learn- ing approaches, in particular association and clustering methods. As a result, we found common factors between the two legal systems in terms of crime, location, grounds for appeal, and judge’s reasoning

Combining WordNet and Word Embeddings in Data Augmentation for Legal Texts

Creating balanced labeled textual corpora for complex tasks, like legal analysis, is a challenging and expensive process that often requires the collaboration of domain experts. To address this problem, we propose a data augmentation method based on the combination of GloVe word embeddings and the WordNet ontology. We present an example of application in the legal domain, specifically on decisions of the Court of Justice of the European Union. Our evaluation with human experts confirms that our method is more robust than the alternatives

Deep Learning for Detecting and Explaining Unfairness in Consumer Contracts

Consumer contracts often contain unfair clauses, in apparent violation of the rel- evant legislation. In this paper we present a new methodology for evaluating such clauses in online Terms of Services. We expand a set of tagged documents (terms of service), with a structured corpus where unfair clauses are liked to a knowledge base of rationales for unfairness, and experiment with machine learning methods on this expanded training set. Our experimental study is based on deep neural net- works that aim to combine learning and reasoning tasks, one major example being Memory Networks. Preliminary results show that this approach may not only pro- vide reasons and explanations to the user, but also enhance the automated detection of unfair clauses