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In a legendary technical report, the Google founders sketched a wisdom-of-crowds justification for PageRank arguing that the algorithm, by aggregating incoming links to webpages in a sophisticated way, tracks importance (quality, relevance, etc.) on the web. On this reading of the report, webpages that have a high impact as measured by PageRank are supposed to be important webpages in a sense of importance that is not reducible to mere impact or popularity. In this paper, we look at the state of the art regarding the more precise statement of the thesis that PageRank and other similar in-link-based ranking algorithms can be justified by reference to the wisdom of crowds. We argue that neither the influential preferential attachment models due to Barabási and Albert in (Science 286:509-512, 1999 (See CR3)) nor the recent model introduced by Masterton et al. in (Scientometrics 106:945-966, 2016 (See CR10)) allows for a satisfactory wisdom-of-crowds justification of PageRank. As a remedy, we suggest that future work should explore 'dual models' of linking on the web, i.e., models that combine the two previous approaches. Dual models view links as being attracted to both popularity and importance., Author(s): George Masterton [sup.1] , Erik J. Olsson [sup.1] Author Affiliations: (Aff1) 0000 0001 0930 2361, grid.4514.4, Department of Philosophy, Lund University, , Lund, Sweden Introduction In a legendary technical [...]

The paper focuses on concerns and legal challenges brought on by the use of algorithms. A particular class of algorithms that augment or replace analysis and decision-making by humans, i.e. data analytics and machine learning, is under scrutiny. Taking into account Balkin's work on 'the laws of an algorithmic society', attention is drawn to obligations of transparency, matters of due process, and accountability. This US-centric analysis on drawbacks and loopholes of current legal systems is complemented with the analysis of norms and principles of the EU data protection law, or 'GDPR'. The aim is twofold. On the one hand, the intent is to shed light on some crucial differences between the US and EU law on the regulation of algorithmic operators, both public and private. Whereas, in the USA, scholars debate whether and to what extent new duties and responsibilities of algorithmic operators, e.g. information fiduciaries, have to amend the current framework of self-regulation and light government-as shown by the White House's Office of Science and Technology Policy report from November 2016-in EU law much of the new duties and responsibilities of algorithmic operators have been passed upon them as data controllers. Whether such approaches will successfully tackle the normative challenges of the algorithmic society is, on the other hand, an open issue that will likely represent the main topic of debate over the next years. Disagreement may concern: (i) the terms framing the legal question in e.g. statistical purposes of the data processing; (ii) how such terms are related to each other in legal reasoning (e.g. a right to explanation as valid law in the EU); and (iii) legal hard cases that will increasingly have to do with the principles that are at stake also but not only in data protection (e.g. informational self-determination). By entrusting such legal hard cases to algorithms, or some sort of smart artificial agent, humans still bear full responsibility for the judgment of what is socially, ethically, and legally 'plain' and 'hard' in social affairs. The balance between delegation of decisions to algorithms and non-delegation will be the leitmotiv of the algorithmic society. Since the devil is in the detail, the current paper is devoted to some of them., Author(s): Ugo Pagallo [sup.1] Author Affiliations: (Aff1) 0000 0001 2336 6580, grid.7605.4, Law School, University of Torino, , Lungo Dora Siena 100 A, 10153, Torino, Italy Introduction There is a [...]

The combination of increased availability of large amounts of fine-grained human behavioral data and advances in machine learning is presiding over a growing reliance on algorithms to address complex societal problems. Algorithmic decision-making processes might lead to more objective and thus potentially fairer decisions than those made by humans who may be influenced by greed, prejudice, fatigue, or hunger. However, algorithmic decision-making has been criticized for its potential to enhance discrimination, information and power asymmetry, and opacity. In this paper, we provide an overview of available technical solutions to enhance fairness, accountability, and transparency in algorithmic decision-making. We also highlight the criticality and urgency to engage multi-disciplinary teams of researchers, practitioners, policy-makers, and citizens to co-develop, deploy, and evaluate in the real-world algorithmic decision-making processes designed to maximize fairness and transparency. In doing so, we describe the Open Algortihms (OPAL) project as a step towards realizing the vision of a world where data and algorithms are used as lenses and levers in support of democracy and development., Author(s): Bruno Lepri [sup.1] , Nuria Oliver [sup.2] [sup.3] , Emmanuel Letouzé [sup.3] [sup.4] , Alex Pentland [sup.3] [sup.4] , Patrick Vinck [sup.3] [sup.5] Author Affiliations: (Aff1) 0000 0000 9780 [...]

Decision-making assisted by algorithms developed by machine learning is increasingly determining our lives. Unfortunately, full opacity about the process is the norm. Would transparency contribute to restoring accountability for such systems as is often maintained? Several objections to full transparency are examined: the loss of privacy when datasets become public, the perverse effects of disclosure of the very algorithms themselves ('gaming the system' in particular), the potential loss of companies' competitive edge, and the limited gains in answerability to be expected since sophisticated algorithms usually are inherently opaque. It is concluded that, at least presently, full transparency for oversight bodies alone is the only feasible option; extending it to the public at large is normally not advisable. Moreover, it is argued that algorithmic decisions preferably should become more understandable; to that effect, the models of machine learning to be employed should either be interpreted ex post or be interpretable by design ex ante., Author(s): Paul B. de Laat [sup.1] Author Affiliations: (Aff1) 0000 0004 0407 1981, grid.4830.f, University of Groningen, , Groningen, Netherlands Introduction Two decades ago, Helen Nissenbaum sounded the alert about [...]

IMPROVING MATERIAL PROPERTY PREDICTIONS By combining large quantities of low-fidelity data with smaller amounts of high-fidelity data, nanoengineers at the University of California San Diego developed a new machine learning [...]

Recently, bias-variance decomposition of error has been used as a tool to study the behavior of learning algorithms and to develop new ensemble methods well suited to the bias-variance characteristics of base learners. We propose methods and procedures, based on Domingo's unified bias-variance theory, to evaluate and quantitatively measure the bias-variance decomposition of error in ensembles of learning machines. We apply these methods to study and compare the bias-variance characteristics of single support vector machines (SVMs) and ensembles of SVMs based on resampling techniques, and their relationships with the cardinality of the training samples. In particular, we present an experimental bias-variance analysis of bagged and random aggregated ensembles of SVMs in order to verify their theoretical variance reduction properties. The experimental bias-variance analysis quantitatively characterizes the relationships between bagging and random aggregating, and explains the reasons why ensembles built on small subsamples of the data work with large databases. Our analysis also suggests new directions for research to improve on classical bagging. Index Terms--Bagging, bias-variance analysis, ensemble of learning machines, support vector machines.