scholarly journals Big Data and the Threat to Moral Responsibility in Healthcare

2021 ◽  
pp. 11-25
Author(s):  
Daniel W. Tigard
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Moral Responsibility ◽  
Intelligent Systems ◽  
Healthcare Systems ◽  
Ethical Challenges ◽  
Trade Offs ◽  
The Social ◽  
Healthcare Technologies

AbstractTechnological innovations in healthcare, perhaps now more than ever, are posing decisive opportunities for improvements in diagnostics, treatment, and overall quality of life. The use of artificial intelligence and big data processing, in particular, stands to revolutionize healthcare systems as we once knew them. But what effect do these technologies have on human agency and moral responsibility in healthcare? How can patients, practitioners, and the general public best respond to potential obscurities in responsibility? In this paper, I investigate the social and ethical challenges arising with newfound medical technologies, specifically the ways in which artificially intelligent systems may be threatening moral responsibility in the delivery of healthcare. I argue that if our ability to locate responsibility becomes threatened, we are left with a difficult choice of trade-offs. In short, it might seem that we should exercise extreme caution or even restraint in our use of state-of-the-art systems, but thereby lose out on such benefits as improved quality of care. Alternatively, we could embrace novel healthcare technologies but in doing so we might need to loosen our commitment to locating moral responsibility when patients come to harm; for even if harms are fewer – say, as a result of data-driven diagnostics – it may be unclear who or what is responsible when things go wrong. What is clear, at least, is that the shift toward artificial intelligence and big data calls for significant revisions in expectations on how, if at all, we might locate notions of responsibility in emerging models of healthcare.

scholarly journals Group Agency and Artificial Intelligence

2021 ◽  
Author(s):  
Christian List
Keyword(s):  
Artificial Intelligence ◽  
Moral Status ◽  
Intelligent Systems ◽  
Group Agency ◽  
High Stakes ◽  
Moral Agents ◽  
Legal Personhood ◽  
The Social ◽  
Intentional Agents ◽  
Regulatory Challenges

AbstractThe aim of this exploratory paper is to review an under-appreciated parallel between group agency and artificial intelligence. As both phenomena involve non-human goal-directed agents that can make a difference to the social world, they raise some similar moral and regulatory challenges, which require us to rethink some of our anthropocentric moral assumptions. Are humans always responsible for those entities’ actions, or could the entities bear responsibility themselves? Could the entities engage in normative reasoning? Could they even have rights and a moral status? I will tentatively defend the (increasingly widely held) view that, under certain conditions, artificial intelligent systems, like corporate entities, might qualify as responsible moral agents and as holders of limited rights and legal personhood. I will further suggest that regulators should permit the use of autonomous artificial systems in high-stakes settings only if they are engineered to function as moral (not just intentional) agents and/or there is some liability-transfer arrangement in place. I will finally raise the possibility that if artificial systems ever became phenomenally conscious, there might be a case for extending a stronger moral status to them, but argue that, as of now, this remains very hypothetical.

scholarly journals Neurosurgeon, nota bene! … brain!

2020 ◽  
Vol 29 (3) ◽  
pp. 80-87
Author(s):  
M.E. Polishchuk
Keyword(s):  
Artificial Intelligence ◽  
Brain Tumor ◽  
Social Factor ◽  
Surgical Interventions ◽  
The Core ◽  
The Social ◽  
The Brain ◽  
Modern Technologies ◽  
Functional Defects

Advances in radiology, and introduction of modern neuroimaging technologies into practice, make it possible to identify pathological zones in various parts of the brain, that measure in millimeters. Modern tractography reveals the influence of various lesion on the conductors of the brain. Applications of the modern neurophysiology technology – electroencephalography, evoked potentials, etc., reveal the functions of various parts of the brain. Utilization of neuronavigation, microsurgery, endoscopy, provide access to the deepest structures of the brain, including the brain stem regions, which were previously inaccessible, and the localization of the process in this area was a serious taboo for neurosurgery. Disputable is the functional acceptability of surgical interventions in order to minimize disorders affecting the quality of patients life. It is necessary to take into account the social factor when before planing the operation with possible functional defects. Neurosurgery has gone from a hammer, a chisel, and removal of brain tumor with a «smart» finger in microsurgery, endoscopy, and endovascular surgery. As the most technologically equipped, she approached the introduction of artificial intelligence both in scientific research and in practical activities, more than other sciences. The usage of modern technologies for predicting neurosurgical interventions should be based in the core of indications and contraindications for surgery.

scholarly journals Causal Learning for Socially Responsible AI

2021 ◽  
Author(s):  
Lu Cheng ◽  
Ahmadreza Mosallanezhad ◽  
Paras Sheth ◽  
Huan Liu
Keyword(s):  
Artificial Intelligence ◽  
Social Responsibility ◽  
State Of The Art ◽  
Causal Learning ◽  
Ethical Challenges ◽  
Socially Responsible ◽  
The Social ◽  
Art Methods

There have been increasing concerns about Artificial Intelligence (AI) due to its unfathomable potential power. To make AI address ethical challenges and shun undesirable outcomes, researchers proposed to develop socially responsible AI (SRAI). One of these approaches is causal learning (CL). We survey state-of-the-art methods of CL for SRAI. We begin by examining the seven CL tools to enhance the social responsibility of AI, then review how existing works have succeeded using these tools to tackle issues in developing SRAI such as fairness. The goal of this survey is to bring forefront the potentials and promises of CL for SRAI.

Towards a Better and Smarter Healthcare and IoT

2019 ◽  
pp. 191-228
Author(s):  
Navin Kumar
Keyword(s):  
Big Data ◽  
Quality Of Care ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Systems ◽  
Healthcare Organizations ◽  
Healthcare Data ◽  
Value Based Care ◽  
Rich Data

The amount of healthcare data continues to exponentially grow everyday. The complexity of this data further limits the analytical capabilities of traditional healthcare systems. With value-based care, it is far more imminent for healthcare organizations to control the costs and to improve the quality of care in order to sustain their business. The purpose of the chapter is to gain insights into complexities and challenges that exist in current healthcare systems and how big data analytics and IoT can play a pivotal role to positively influence the quality of care and patient outcomes. The chapter also provides solutions and strategies for building cloud-based data asset that can deliver rich data analytics to both the healthcare systems and the patients.

Social Implications of AI

2021 ◽  
pp. 197-224
Keyword(s):  
Artificial Intelligence ◽  
Technological Process ◽  
Intelligent Systems ◽  
Social Issues ◽  
Tipping Point ◽  
Housing Finance ◽  
Social Implications ◽  
Field Of Study ◽  
The Social ◽  
Important Field

‘Social implications' generally refers to anything that affects an individual, a community, and wider society. The social implications of artificial intelligence (AI) is an immensely important field of study since AI technology will steadily continue to permeate other technologies and, inevitably, our society as a whole. Many of the social implications of this technological process are non-obvious and surprising. We should ask ourselves, What type of society do we want and what role will AI play to influence and shape lives? Will people simply become consumers served by intelligent systems that respond to our every whim? Are we reaching a tipping point between convenience and dependency? How will AI affect social issues relating to housing, finance, privacy, poverty, and so on? Do we want a society where machines are supplementing (or augmenting) humans or perhaps even substituting humans? It is important to be as clear as possible about likely social implications of AI if it truly helps benefit individuals and society.

Artificial Agents in Natural Moral Communities: A Brief Clarification

2021 ◽  
Vol 30 (3) ◽  
pp. 455-458
Author(s):  
Daniel W. Tigard
Keyword(s):  
Artificial Intelligence ◽  
Moral Responsibility ◽  
Social Ontology ◽  
Artificial Agents ◽  
Human Beings ◽  
Context Sensitive ◽  
Attitudes And Practices ◽  
Moral Communities ◽  
The Social ◽  
Moral Attitudes

AbstractWhat exactly is it that makes one morally responsible? Is it a set of facts which can be objectively discerned, or is it something more subjective, a reaction to the agent or context-sensitive interaction? This debate gets raised anew when we encounter newfound examples of potentially marginal agency. Accordingly, the emergence of artificial intelligence (AI) and the idea of “novel beings” represent exciting opportunities to revisit inquiries into the nature of moral responsibility. This paper expands upon my article “Artificial Moral Responsibility: How We Can and Cannot Hold Machines Responsible” and clarifies my reliance upon two competing views of responsibility. Although AI and novel beings are not close enough to us in kind to be considered candidates for the same sorts of responsibility we ascribe to our fellow human beings, contemporary theories show us the priority and adaptability of our moral attitudes and practices. This allows us to take seriously the social ontology of relationships that tie us together. In other words, moral responsibility is to be found primarily in the natural moral community, even if we admit that those communities now contain artificial agents.

scholarly journals Influential Usage of Big Data and Artificial Intelligence in Healthcare

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yan Cheng Yang ◽  
Saad Ul Islam ◽  
Asra Noor ◽  
Sadia Khan ◽  
Waseem Afsar ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Tumor Biology ◽  
Health Sector ◽  
Clinical Work ◽  
Information And Communications Technology ◽  
Added Value ◽  
Medical Health ◽  
Electronic Health

Artificial intelligence (AI) is making computer systems capable of executing human brain tasks in many fields in all aspects of daily life. The enhancement in information and communications technology (ICT) has indisputably improved the quality of people’s lives around the globe. Especially, ICT has led to a very needy and tremendous improvement in the health sector which is commonly known as electronic health (eHealth) and medical health (mHealth). Deep machine learning and AI approaches are commonly presented in many applications using big data, which consists of all relevant data about the medical health and diseases which a model can access at the time of execution or diagnosis of diseases. For example, cardiovascular imaging has now accurate imaging combined with big data from the eHealth record and pathology to better characterize the disease and personalized therapy. In clinical work and imaging, cancer care is getting improved by knowing the tumor biology and helping in the implementation of precision medicine. The Markov model is used to extract new approaches for leveraging cancer. In this paper, we have reviewed existing research relevant to eHealth and mHealth where various models are discussed which uses big data for the diagnosis and healthcare system. This paper summarizes the recent promising applications of AI and big data in medical health and electronic health, which have potentially added value to diagnosis and patient care.

scholarly journals Big data in the healthcare system: a synergy with artificial intelligence and blockchain technology

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Reyes-González Juan Pablo ◽  
Díaz-Peregrino Roberto ◽  
Soto-Ulloa Victor ◽  
Galvan-Remigio Isabel ◽  
Castillo Paul ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Healthcare System ◽  
Medical Doctors ◽  
Blockchain Technology ◽  
System A ◽  
Healthcare Professions ◽  
State Of Art ◽  
Digital Anatomy

Abstract In the last decades big data has facilitating and improving our daily duties in the medical research and clinical fields; the strategy to get to this point is understanding how to organize and analyze the data in order to accomplish the final goal that is improving healthcare system, in terms of cost and benefits, quality of life and outcome patient. The main objective of this review is to illustrate the state-of-art of big data in healthcare, its features and architecture. We also would like to demonstrate the different application and principal mechanisms of big data in the latest technologies known as blockchain and artificial intelligence, recognizing their benefits and limitations. Perhaps, medical education and digital anatomy are unexplored fields that might be profitable to investigate as we are proposing. The healthcare system can be revolutionized using these different technologies. Thus, we are explaining the basis of these systems focused to the medical arena in order to encourage medical doctors, nurses, biotechnologies and other healthcare professions to be involved and create a more efficient and efficacy system.

scholarly journals The social turn of artificial intelligence

AI & Society ◽  
2021 ◽  
Author(s):  
Nello Cristianini ◽  
Teresa Scantamburlo ◽  
James Ladyman
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Systems ◽  
Single System ◽  
New Paradigm ◽  
Digital Platforms ◽  
Online Platforms ◽  
Large Numbers ◽  
The Social ◽  
Social Machines ◽  
New Phase

AbstractSocial machines are systems formed by material and human elements interacting in a structured way. The use of digital platforms as mediators allows large numbers of humans to participate in such machines, which have interconnected AI and human components operating as a single system capable of highly sophisticated behaviour. Under certain conditions, such systems can be understood as autonomous goal-driven agents. Many popular online platforms can be regarded as instances of this class of agent. We argue that autonomous social machines provide a new paradigm for the design of intelligent systems, marking a new phase in AI. After describing the characteristics of goal-driven social machines, we discuss the consequences of their adoption, for the practice of artificial intelligence as well as for its regulation.

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