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The increasing implementation of and reliance on machine-learning (ML) algorithms to perform tasks, deliver services and make decisions in health and healthcare have made the need for fairness in ML, and more specifically in healthcare ML algorithms (HMLA), a very important and urgent task. However, while the debate on fairness in the ethics of artificial intelligence (AI) and in HMLA has grown significantly over the last decade, the very concept of fairness as an ethical value has not yet been sufficiently explored. Our paper aims to fill this gap and address the AI ethics principle of fairness from a conceptual standpoint, drawing insights from accounts of fairness elaborated in moral philosophy and using them to conceptualise fairness as an ethical value and to redefine fairness in HMLA accordingly. To achieve our goal, following a first section aimed at clarifying the background, methodology and structure of the paper, in the second section, we provide an overview of the discussion of the AI ethics principle of fairness in HMLA and show that the concept of fairness underlying this debate is framed in purely distributive terms and overlaps with non-discrimination, which is defined in turn as the absence of biases. After showing that this framing is inadequate, in the third section, we pursue an ethical inquiry into the concept of fairness and argue that fairness ought to be conceived of as an ethical value. Following a clarification of the relationship between fairness and non-discrimination, we show that the two do not overlap and that fairness requires much more than just non-discrimination. Moreover, we highlight that fairness not only has a distributive but also a socio-relational dimension. Finally, we pinpoint the constitutive components of fairness. In doing so, we base our arguments on a renewed reflection on the concept of respect, which goes beyond the idea of equal respect to include respect for individual persons. In the fourth section, we analyse the implications of our conceptual redefinition of fairness as an ethical value in the discussion of fairness in HMLA. Here, we claim that fairness requires more than non-discrimination and the absence of biases as well as more than just distribution; it needs to ensure that HMLA respects persons both as persons and as particular individuals. Finally, in the fifth section, we sketch some broader implications and show how our inquiry can contribute to making HMLA and, more generally, AI promote the social good and a fairer society. [ABSTRACT FROM AUTHOR]

In popular discussions, the nuances of AI are often abridged as "the algorithm", as the specific arrangements of machine learning (ML), deep learning (DL) and automated decision-making on social media platforms are typically shrouded in proprietary secrecy punctuated by press releases and transparency initiatives. What is clear, however, is that AI embedded on social media functions to recommend content, personalize ads, aggregate news stories, and moderate problematic material. It is also increasingly apparent that individuals are concerned with the uses, implications, and fairness of algorithmic systems. Perhaps in response to concerns about "the algorithm" by individuals and governments, social media platforms utilize transparency initiatives and official statements, in part, to deflect official regulation. In the following paper, I draw from transparency initiatives and statements from representatives of Facebook and TikTok as case studies of how AI is embedded in these platforms, with attention to the promotion of AI content moderation as a solution to the circulation of problematic material and misinformation. This examination considers the complexity of embedded AI as a material-discursive apparatus, predicated on discursive techniques—what is seeable, sayable, knowable in a given time period—as well as the material arrangements—algorithms, datasets, users, platforms, infrastructures, moderators, etc. As such, the use of AI as part of the immensely popular platforms Facebook and TikTok demonstrates that AI does not exist in isolation, instead functioning as human–machine ensemble reliant on strategies of acceptance via discursive techniques and the changing material arrangements of everyday embeddedness. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence, big data classification technology provides the advantageous help for the medicine auxiliary diagnosis research. While due to the different conditions in the different sample collection, the medical big data is often imbalanced. The class-imbalance problem has been reported as a serious obstacle to the classification performance of many standard learning algorithms. SMOTE algorithm could be used to generate sample points randomly to improve imbalance rate, but its application is affected by the marginalization generation and blindness of parameter selection. Focusing on this problem, an improved SMOTE algorithm based on Normal distribution is proposed in this paper, so that the new sample points are distributed closer to the center of the minority sample with a higher probability to avoid the marginalization of the expanded data. Experiments show that the classification effect is better when use proposed algorithm to expand the imbalanced dataset of Pima, WDBC, WPBC, Ionosphere and Breast-cancer-wisconsin than the original SMOTE algorithm. In addition, the parameter selection of the proposed algorithm is analyzed and it is found that the classification effect is the best when the distribution characteristics of the original data was maintained best by selecting appropriate parameters in our designed experiments. [ABSTRACT FROM AUTHOR]

During 2018, as part of a research project funded by the Deviant Practice Grant, artist Bruno Moreschi and digital media researcher Gabriel Pereira worked with the Van Abbemuseum collection (Eindhoven, NL), reading their artworks through commercial image-recognition (computer vision) artificial intelligences from leading tech companies. The main takeaways were: somewhat as expected, AI is constructed through a capitalist and product-focused reading of the world (values that are embedded in this sociotechnical system); and that this process of using AI is an innovative way for doing institutional critique, as AI offers an untrained eye that reveals the inner workings of the art system through its glitches. This paper aims to regard these glitches as potentially revealing of the art system, and even poetic at times. We also look at them as a way of revealing the inherent fallibility of the commercial use of AI and machine learning to catalogue the world: it cannot comprehend other ways of knowing about the world, outside the logic of the algorithm. But, at the same time, due to their "glitchy" capacity to level and reimagine, these faulty readings can also serve as a new way of reading art; a new way for thinking critically about the art image in a moment when visual culture has changed form to hybrids of human–machine cognition and "machine-to-machine seeing". [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is one of the main drivers of what has been described as the "Fourth Industrial Revolution", as well as the most innovative technology developed to date. It is a pervasive transformative innovation, which needs a new approach. In 2017, the European Parliament introduced the notion of the "electronic person", which sparked huge debates in philosophical, legal, technological, and other academic settings. The issues related to AI should be examined from an interdisciplinary perspective. In this paper, we examine this legal innovation—that has been proposed by the European Parliament—from not only legal but also technological points of view. In the first section, we define AI and analyse its main characteristics. We argue that, from a technical perspective, it appears premature and probably inappropriate to introduce AI personhood now. In the second section, justifications for the European Parliament's proposals are explored in contrast with the opposing arguments that have been presented. As the existing mechanisms of liability could be insufficient in scenarios where AI systems cause harm, especially when algorithms of AI learn and evolve on their own, there is a need to depart from traditional liability theories. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) and machine learning (ML) tools play a significant role in the recent evolution of smart systems. AI solutions are pushing towards a significant shift in many fields such as healthcare, autonomous airplanes and vehicles, security, marketing customer profiling and other diverse areas. One of the main challenges hindering the AI potential is the demand for high-performance computation resources. Recently, hardware accelerators are developed in order to provide the needed computational power for the AI and ML tools. In the literature, hardware accelerators are built using FPGAs, GPUs and ASICs to accelerate computationally intensive tasks. These accelerators provide high-performance hardware while preserving the required accuracy. In this work, we present a systematic literature review that focuses on exploring the available hardware accelerators for the AI and ML tools. More than 169 different research papers published between the years 2009 and 2019 are studied and analysed. [ABSTRACT FROM AUTHOR]

Artificial intelligence algorithms are booming in medicine, and the question of biases induced or perpetuated by these tools is a very important topic. There is a greater risk of these biases in radiology, which is now the primary diagnostic tool in modern treatment. Some authors have recently proposed an analysis framework for social inequalities and the biases at risk of being introduced into future algorithms. In our paper, we comment on the different strategies for resolving these biases. We warn that there is an even greater risk in mixing the notion of equity, the definition of which is socio-political, into the design stages of these algorithms. We believe that rather than being beneficial, this could in fact harm the main purpose of these artificial intelligence tools, which is the care of the patient. [ABSTRACT FROM AUTHOR]

Since the emergence of the innovative field of artificial intelligence (AI) in the 1960s, the late Hubert Dreyfus insisted on the ontological distinction between man and machine, human and artificial intelligence. In the different editions of his classic and influential book What computers can't do (1972), he posits that an algorithmic machine can never fully simulate the complex functioning of the human mind—not now, nor in the future. Dreyfus' categorical distinctions between man and machine are still relevant today, but their relation has become more complex in our increasingly data-driven society. We, humans, are continuously immersed within a technological universe, while at the same time ubiquitous computing, in the words of computer scientist Mark Weiser, "forces computers to live out here in the world with people" (De Souza e Silva in Interfaces of hybrid spaces. In: Kavoori AP, Arceneaux N (eds) The cell phone reader. Peter Lang Publishing, New York, 2006, p 20). Dreyfus' ideas are therefore challenged by thinkers such as Weiser, Kevin Kelly, Bruno Latour, Philip Agre, and Peter Paul Verbeek, who all argue that humans are much more intrinsically linked to machines than the original dichotomy suggests—they have evolved in concert. Through a discussion of the classical concepts of individuum and 'authenticity' within Western civilization, this paper argues that within the ever-expanding data-sphere of the twenty-first century, a new concept of man as 'aggregate of data' has emerged, which further erodes and undermines the categorical distinction between man and machine. This raises political and ethical questions beyond the confines of technology and artificial intelligence. Moreover, this seemingly never-ending debate on what computers should (or should not) do provokes the philosophical necessity to once again define the concept of what it is to be 'human.' [ABSTRACT FROM AUTHOR]

The aim of this paper is to present some philosophical considerations about the supposed AI emergence in the future. However, the predicted timeline of this process is uncertain. To avoid any kind of speculations on the proposed analysis from a scientific point of view, a metaphysical approach is undertaken as a modal context of the discussion. I argue that modal claim about possible AI emergence at a certain point of time in the future is justified from a temporal perspective. Therefore, worldwide society must be prepared for possible AI emergence and the expected profound impact of such an event on the existential status of humanity. [ABSTRACT FROM AUTHOR]

In Homo Deus, Yuval Noah Harari argues that technological advances of the twenty-first century will usher in a significant shift in how humans make important life decisions. Instead of turning to the Bible or the Quran, to the heart or to our therapists, parents, and mentors, people will turn to Big Data recommendation algorithms to make these choices for them. Much as we rely on Spotify to recommend music to us, we will soon rely on algorithms to decide our careers, spouses, and commitments. Harari also predicts that next, the state will take away individuals’ rights to make their own choices about their lives. If Google knows where your children would flourish best in school, why should the state allow a fallible human parent to decide? Liberalism—which, as Harari uses this term, refers to a state of society in which human freedom to choose is respected and championed—will collapse. In this paper, I argue that Harari’s conception of the future implications of recommendation algorithms is deeply flawed, for two reasons. First, users will not rely on algorithms to make decisions for them because they have no reason to trust algorithms, which are developed by companies with their own incentives, such as profit. Second, for most of our life decisions, algorithms will not be able to be developed, because the factors relevant to the decisions we face are unique to our situation. I present an alternative depiction of the future: instead of relying on algorithms to make decisions for us, humans will use algorithms to enhance our decision-making by helping us consider the most relevant choices first and notice information we might not otherwise. Finally, I will also argue that even if computers could make many of our decisions for us, liberalism as a political system would emerge unscathed. [ABSTRACT FROM AUTHOR]