AI ethics in November 2017

A smarter transport system that caters for social, economic and environmental sustainability is arguably one of the most critical prerequisites for creating pathways to more livable urban futures. This paper aims to provide a state-of-the-art analysis of a selection of mobility initiatives that may dictate the future of urban transportation and make cities smarter. These are mechanisms either recently introduced with encouraging uptake so far and much greater potential to contribute in a shift t

Peer review of research articles is a core part of our scholarly communication system. In spite of its importance, the status and purpose of peer review is often contested. What is its role in our modern digital research and communications infrastructure? Does it perform to the high standards with which it is generally regarded? Studies of peer review have shown that it is prone to bias and abuse in numerous dimensions, frequently unreliable, and can fail to detect even fraudulent research. With

OBJECTIVE: Update breastfeeding indicators trend in Brazil for the last three decades, incorporating more up-to-date information from the National Health Survey. METHODS: We used secondary data from national surveys with information on breastfeeding (1986, 1996, 2006, and 2013) to construct the time series of prevalence for the following indicators: exclusive breastfeeding in children under six months of age (EBF6m), breastfeeding in toddlers under 2 years of age (BF), continued breastfeeding at

BACKGROUND: Data measuring airborne pollutants, public health and environmental factors are increasingly being stored and merged. These big datasets offer great potential, but also challenge traditional epidemiological methods. This has motivated the exploration of alternative methods to make predictions, find patterns and extract information. To this end, data mining and machine learning algorithms are increasingly being applied to air pollution epidemiology. METHODS: We conducted a systematic

Abstract I argue that virtual reality is a sort of genuine reality. In particular, I argue for virtual digitalism, on which virtual objects are real digital objects, and against virtual fictionalism, on which virtual objects are fictional objects. I also argue that perception in virtual reality need not be illusory, and that life in virtual worlds can have roughly the same sort of value as life in non-virtual worlds.

This paper explores the phenomena of the emergence of the use of artificial intelligence in teaching and learning in higher education. It investigates educational implications of emerging technologies on the way students learn and how institutions teach and evolve. Recent technological advancements and the increasing speed of adopting new technologies in higher education are explored in order to predict the future nature of higher education in a world where artificial intelligence is part of the

BACKGROUND: Even today the reliable diagnosis of the prodromal stages of Alzheimer's disease (AD) remains a great challenge. Our research focuses on the earliest detectable indicators of cognitive decline in mild cognitive impairment (MCI). Since the presence of language impairment has been reported even in the mild stage of AD, the aim of this study is to develop a sensitive neuropsychological screening method which is based on the analysis of spontaneous speech production during performing a m

Self-driving cars, a quintessentially 'smart' technology, are not born smart. The algorithms that control their movements are learning as the technology emerges. Self-driving cars represent a high-stakes test of the powers of machine learning, as well as a test case for social learning in technology governance. Society is learning about the technology while the technology learns about society. Understanding and governing the politics of this technology means asking 'Who is learning, what are the

Massive Open Online Courses (MOOCs) represent a form of large-scale learning that is changing the landscape of higher education. In this paper, we offer a perspective on how advances in artificial intelligence (AI) may enhance learning and research on MOOCs. We focus on emerging AI techniques including how knowledge representation tools can enable students to adjust the sequence of learning to fit their own needs; how optimization techniques can efficiently match community teaching assistants to

Cite as Lilian Edwards and Michael Veale, 'Slave to the Algorithm? Why a 'right to an explanation' is probably not the remedy you are looking for' (2017) 16 Duke Law and Technology Review 18–84. (First posted on SSRN 24 May 2017)Algorithms, particularly machine learning (ML) algorithms, are increasingly important to individuals’ lives, but have caused a range of concerns revolving mainly around unfairness, discrimination and opacity. Transparency in the form of a “right to an explanation” has em

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 com

While new generations of implantable brain computer interface (BCI) devices are being developed, evidence in the literature about their impact on the patient experience is lagging. In this article, we address this knowledge gap by analysing data from the first-in-human clinical trial to study patients with implanted BCI advisory devices. We explored perceptions of self-change across six patients who volunteered to be implanted with artificially intelligent BCI devices. We used qualitative method

This article updates our understanding of the field of job insecurity (JI) by incorporating studies across the globe since 2003, analyzes what we know, and offers ideas on how to move forward. We begin by reviewing the conceptualization and operationalization of job insecurity. We then review empirical studies of the antecedents, consequences, and moderators of JI effects, as well as the various theoretical perspectives used to explain the relationship of JI to various outcomes. Our analyses als

While exploratory factor analysis (EFA) provides a more realistic presentation of the data with the allowance of item cross-loadings, confirmatory factor analysis (CFA) includes many methodological advances that the former does not. To create a synergy of the two, exploratory structural equation modeling (ESEM) was proposed as an alternative solution, incorporating the advantages of EFA and CFA. The present investigation is thus an illustrative demonstration of the applicability and flexibility

Breastfeeding is a powerful health-promoting behavior. A 2016 Lancet global collaboration to review the health implications of breastfeeding was among the first to consider oral health outcomes. While a role was suggested for breastfeeding in preventing malocclusion, caries was the only included disease condition unfavorably associated with breastfeeding. The present critical review examines the evidence connecting breastfeeding practices to these outcomes and discusses the methodological challe

Health and wellbeing are determined by a number of complex, interrelated factors. The application of design thinking to questions around health may prove valuable and complement existing approaches. A number of public health projects utilizing human centered design (HCD), or design thinking, have recently emerged, but no synthesis of the literature around these exists. The results of a scoping review of current research on human centered design for health outcomes are presented. The review aimed

BACKGROUND: Gene expression is a key intermediate level that genotypes lead to a particular trait. Gene expression is affected by various factors including genotypes of genetic variants. With an aim of delineating the genetic impact on gene expression, we build a deep auto-encoder model to assess how good genetic variants will contribute to gene expression changes. This new deep learning model is a regression-based predictive model based on the MultiLayer Perceptron and Stacked Denoising Auto-en