Transparency

BACKGROUND: Hyperspectral imaging is emerging as a promising approach for plant disease identification. The large and possibly redundant information contained in hyperspectral data cubes makes deep learning based identification of plant diseases a natural fit. Here, we deploy a novel 3D deep convolutional neural network (DCNN) that directly assimilates the hyperspectral data. Furthermore, we interrogate the learnt model to produce physiologically meaningful explanations. We focus on an economica

Abstract Reproducible science requires transparent reporting. The ARRIVE guidelines were originally developed in 2010 to improve the reporting of animal research. They consist of a checklist of information to include in publications describing in vivo experiments to enable others to scrutinise the work adequately, evaluate its methodological rigour, and reproduce the methods and results. Despite considerable levels of endorsement by funders and journals over the years, adherence to the guideline

Explainable artificial intelligence (AI) is attracting much interest in medicine. Technically, the problem of explainability is as old as AI itself and classic AI represented comprehensible retraceable approaches. However, their weakness was in dealing with uncertainties of the real world. Through the introduction of probabilistic learning, applications became increasingly successful, but increasingly opaque. Explainable AI deals with the implementation of transparency and traceability of statis

Evolutionary fuzzy systems are one of the greatest advances within the area of computational intelligence. They consist of evolutionary algorithms applied to the design of fuzzy systems. Thanks to this hybridization, superb abilities are provided to fuzzy modeling in many different data science scenarios. This contribution is intended to comprise a position paper developing a comprehensive analysis of the evolutionary fuzzy systems research field. To this end, the "4 W" questions are posed and a

There has recently been a surge of work in explanatory artificial intelligence (XAI). This research area tackles the important problem that complex machines and algorithms often cannot provide insights into their behavior and thought processes. XAI allows users and parts of the internal system to be more transparent, providing explanations of their decisions in some level of detail. These explanations are important to ensure algorithmic fairness, identify potential bias/problems in the training

Big data and data science transform organizational decision-making. We increasingly defer decisions to algorithms because machines have earned a reputation of outperforming us. As algorithms become embedded within organizations, they become more influential and increasingly opaque. Those who create algorithms may make arbitrary decisions in all stages of the ‘data value chain’, yet these subjectivities are obscured from view. Algorithms come to reflect the biases of their creators, can reinforce

Algorithms silently structure our lives. Algorithms can determine whether someone is hired, promoted, offered a loan, or provided housing as well as determine which political ads and news articles consumers see. Yet, the responsibility for algorithms in these important decisions is not clear. This article identifies whether developers have a responsibility for their algorithms later in use, what those firms are responsible for, and the normative grounding for that responsibility. I conceptualize

Reviewing recent research on poverty in the United States, we derive a conceptual framework with three main characteristics. First, poverty is multidimensional, compounding material hardship with human frailty, generational trauma, family and neighborhood violence, and broken institutions. Second, poverty is relational, produced through connections between the truly advantaged and the truly disadvantaged. Third, a component of this conceptual framework is transparently normative, applying empiri

This paper focuses on the ethical challenges presented by direct-to-consumer (DTC) digital psychotherapy services that do not involve oversight by a professional mental health provider. DTC digital psychotherapy services can potentially assist in improving access to mental health care for the many people who would otherwise not have the resources or ability to connect with a therapist. However, the lack of adequate regulation in this area exacerbates concerns over how safety, privacy, accountabi

Calls for heightened consideration of fairness and accountability in algorithmically-informed public decisions-like taxation, justice, and child protection-are now commonplace. How might designers support such human values? We interviewed 27 public sector machine learning practitioners across 5 OECD countries regarding challenges understanding and imbuing public values into their work. The results suggest a disconnect between organisational and institutional realities, constraints and needs, and

Artificial intelligence (AI) generally and machine learning (ML) specifically demonstrate impressive practical success in many different application domains, e.g. in autonomous driving, speech recognition, or recommender systems. Deep learning approaches, trained on extremely large data sets or using reinforcement learning methods have even exceeded human performance in visual tasks, particularly on playing games such as Atari, or mastering the game of Go. Even in the medical domain there are re

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

The EPSRC's Principles of Robotics advises the implementation of transparency in robotic systems, however research related to AI transparency is in its infancy. This paper introduces the reader of the importance of having transparent inspection of intelligent agents and provides guidance for good practice when developing such agents. By considering and expanding upon other prominent definitions found in literature, we provide a robust definition of transparency as a mechanism to expose the decis

The ever-increasing application of algorithms to decision-making in a range of social contexts has prompted demands for algorithmic accountability. Accountable decision-makers must provide their decision-subjects with justifications for their automated system's outputs, but what kinds of broader principles should we expect such justifications to appeal to? Drawing from political philosophy, I present an account of algorithmic accountability in terms of the democratic ideal of 'public reason'. I

In recent months, researchers,1 government bodies,2 and the media3 have claimed that a ‘right to explanation’ of decisions made by automated and artificially intelligent algorithmic systems is legally mandated by the forthcoming European Union General Data Protection Regulation4 2016/679 (GDPR). The right to explanation is viewed as a promising mechanism in the broader pursuit by government and industry for accountability and transparency in algorithms, artificial intelligence, robotics, and oth

This article is based on the assumption of musical power to change the listener's mood. The paper studies the outcome of two experiments on the regulation of emotional states in a series of participants who listen to different auditions. The present research focuses on note value, an important musical cue related to rhythm. The influence of two concepts linked to note value is analyzed separately and discussed together. The two musical cues under investigation are tempo and rhythmic unit. The pa

We investigated the relationship between semantic knowledge and word reading. A sample of 27 6-year-old children read words both in isolation and in context. Lexical knowledge was assessed using general and item-specific tasks. General semantic knowledge was measured using standardized tasks in which children defined words and made judgments about the relationships between words. Item-specific knowledge of to-be-read words was assessed using auditory lexical decision (lexical phonology) and defi

Current uses of robots in classrooms are reviewed and used to characterise four scenarios: (s1) Robot as Classroom Teacher; (s2) Robot as Companion and Peer; (s3) Robot as Care-eliciting Companion; and (s4) Telepresence Robot Teacher. The main ethical concerns associated with robot teachers are identified as: privacy; attachment, deception, and loss of human contact; and control and accountability. These are discussed in terms of the four identified scenarios. It is argued that classroom robots

There is a growing literature arguing that certain entities embodied by groups of agents—certain “collectives”—themselves qualify as agents; even, some say, as moral agents.1 These are ambitious claims. To be agents, such entities must be capable of acting on the basis of their own beliefs, desires, and intentions. To be moral agents, subject to moral obligations and accountable for their actions, they should presumably also be capable of acting freely in some relevant sense, and of recognizing

Scholars in the humanities and social sciences are experimenting with novel ways of engaging with worlds around us. Passionate immersion in the lives of fungi, microorganisms, animals, and plants is opening up new understandings, relationships, and accountabilities. This introduction to the special issue offers an overview of the emerging field of multispecies studies. Unsettling given notions of species, it explores a broad terrain of possible modes of classifying, categorizing, and paying atte

Children with specific language impairments (SLIs) show impaired perception and production of language, and also show impairments in perceiving auditory cues to rhythm [amplitude rise time (ART) and sound duration] and in tapping to a rhythmic beat. Here we explore potential links between language development and rhythm perception in 45 children with SLI and 50 age-matched controls. We administered three rhythmic tasks, a musical beat detection task, a tapping-to-music task, and a novel music/sp

Trust and transparency influence consumer information exchanges, yet the understanding of how they shape marketing and public policy relating to privacy and security issues is not current with the digital and informational age. People face increasing complexity in online exchanges of information and lack the time, attention, and wherewithal to understand how to protect themselves. Society's reliance on technology results in individuals engaging in continuous partial attention and behaving as cog

Listeners with hearing loss commonly report having difficulty understanding speech, particularly in noisy environments. Their difficulties could be due to auditory and cognitive processing problems. Performance on speech-in-noise tests has been correlated with reading working memory span (RWMS), a measure often chosen to avoid the effects of hearing loss. If the goal is to assess the cognitive consequences of listeners' auditory processing abilities, however, then listening working memory span (

This study investigated peoples' opinion on auditory interfaces in contemporary cars and their willingness to be exposed to auditory feedback in automated driving. We used an Internet-based survey to collect 1,205 responses from 91 countries. The respondents stated their attitudes towards two existing auditory driver assistance systems, a parking assistant (PA) and a forward collision warning system (FCWS), as well as towards a futuristic augmented sound system (FS) proposed for fully automated

Major theories on the neural basis of schizophrenic core symptoms highlight aberrant salience network activity (insula and anterior cingulate cortex), prefrontal hypoactivation, sensory processing deficits as well as an impaired connectivity between temporal and prefrontal cortices. The mismatch negativity is a potential biomarker of schizophrenia and its reduction might be a consequence of each of these mechanisms. In contrast to the previous electroencephalographic studies, functional magnetic

Bangladesh has recently been enjoying significant economic growth mainly arising from an export led development strategy. However, in that process its natural environment has been degraded and become more vulnerable in geophysical terms (e.g. environmental pollution). Much of the Bangladeshi population are also vulnerable in socio-economic terms due primarily to widespread poverty. In this context we ask, albeit sceptically, whether there is any chance of holding corporations to account for thei