AI ethics in March 2021

Purpose This study aims to conduct a comprehensive review and network-based analysis by exploring future research directions in the nexus of circular economy (CE) and sustainable business performance (SBP) in the context of digitalization. Design/methodology/approach A systematic literature review methodology was adopted to present the review in the field of CE and SBP in the era of digitalization. WOS and SCOPUS databases were considered in the study to identify and select the articles. The bib

Data privacy mechanisms are essential for rapidly scaling medical training databases to capture the heterogeneity of patient data distributions toward robust and generalizable machine learning systems. In the current COVID-19 pandemic, a major focus of artificial intelligence (AI) is interpreting chest CT, which can be readily used in the assessment and management of the disease. This paper demonstrates the feasibility of a federated learning method for detecting COVID-19 related CT abnormalitie

AI is one of the most debated subjects of today and there seems little common understanding concerning the differences and similarities of human intelligence and artificial intelligence. Discussions on many relevant topics, such as trustworthiness, explainability, and ethics are characterized by implicit anthropocentric and anthropomorphistic conceptions and, for instance, the pursuit of human-like intelligence as the golden standard for Artificial Intelligence. In order to provide more agreemen

BACKGROUND: Strategies for integrating artificial intelligence (AI) into thyroid nodule management require additional development and testing. We developed a deep-learning AI model (ThyNet) to differentiate between malignant tumours and benign thyroid nodules and aimed to investigate how ThyNet could help radiologists improve diagnostic performance and avoid unnecessary fine needle aspiration. METHODS: ThyNet was developed and trained on 18 049 images of 8339 patients (training set) from two hos

The use of black box algorithms in medicine has raised scholarly concerns due to their opaqueness and lack of trustworthiness. Concerns about potential bias, accountability and responsibility, patient autonomy and compromised trust transpire with black box algorithms. These worries connect epistemic concerns with normative issues. In this paper, we outline that black box algorithms are less problematic for epistemic reasons than many scholars seem to believe. By outlining that more transparency

The increasing population across the globe makes it essential to link smart and sustainable city planning with the logistics of transporting people and goods, which will significantly contribute to how societies will face mobility in the coming years. The concept of smart mobility emerged with the popularity of smart cities and is aligned with the sustainable development goals defined by the United Nations. A reduction in traffic congestion and new route optimizations with reduced ecological foo

Abstract This paper addresses quantitative meta-analyses on corporate governance-related determinants and firms’ (non) financial consequences of Corporate Social Responsibility (CSR). Legitimacy theory as our theoretical framework assumes that, through a social contract, a company must fulfil the respective society’s values and expectations and gain legitimacy. We also rely on the business case argument, assuming a positive relationship between CSR and financial outcomes of the firm. This analys

Deep learning as represented by the artificial deep neural networks (DNNs) has achieved great success recently in many important areas that deal with text, images, videos, graphs, and so on. However, the black-box nature of DNNs has become one of the primary obstacles for their wide adoption in mission-critical applications such as medical diagnosis and therapy. Because of the huge potentials of deep learning, increasing the interpretability of deep neural networks has recently attracted much re

The rapid growth, demand, and production of batteries to meet various emerging applications, such as electric vehicles and energy storage systems, will result in waste and disposal problems in the next few years as these batteries reach end-of-life. Battery reuse and recycling are becoming urgent worldwide priorities to protect the environment and address the increasing need for critical metals. As a review article, this paper reveals the current global battery market and global battery waste st

BACKGROUND: Artificial intelligence (AI) applications are growing at an unprecedented pace in health care, including disease diagnosis, triage or screening, risk analysis, surgical operations, and so forth. Despite a great deal of research in the development and validation of health care AI, only few applications have been actually implemented at the frontlines of clinical practice. OBJECTIVE: The objective of this study was to systematically review AI applications that have been implemented in

Data-driven digital health technologies have the power to transform health care. If these tools could be sustainably delivered at scale, they might have the potential to provide everyone, everywhere, with equitable access to expert-level care, narrowing the global health and wellbeing gap. Conversely, it is highly possible that these transformative technologies could exacerbate existing health-care inequalities instead. In this Viewpoint, we describe the problem of health data poverty: the inabi

The most successful Machine Learning (ML) systems remain complex black boxes to end-users, and even experts are often unable to understand the rationale behind their decisions. The lack of transparency of such systems can have severe consequences or poor uses of limited valuable resources in medical diagnosis, financial decision-making, and in other high-stake domains. Therefore, the issue of ML explanation has experienced a surge in interest from the research community to application domains. W

Artificial intelligence technology has advanced rapidly in recent years and has the potential to improve healthcare outcomes. However, technology uptake will be largely driven by clinicians, and there is a paucity of data regarding the attitude that clinicians have to this new technology. In June-August 2019 we conducted an online survey of fellows and trainees of three specialty colleges (ophthalmology, radiology/radiation oncology, dermatology) in Australia and New Zealand on artificial intell

In this experience report, we describe an AI summer workshop designed to prepare middle school students to become informed citizens and critical consumers of AI technology and to develop their foundational knowledge and skills to support future endeavors as AI-empowered workers. The workshop featured the 30-hour "Developing AI Literacy" or DAILy curriculum that is grounded in literature on child development, ethics education, and career development. The participants in the workshop were students

The 5G wireless communication network is currently faced with the challenge of limited data speed exacerbated by the proliferation of billions of data-intensive applications. To address this problem, researchers are developing cutting-edge technologies for the envisioned 6G wireless communication standards to satisfy the escalating wireless services demands. Though some of the candidate technologies in the 5G standards will apply to 6G wireless networks, key disruptive technologies that will gua

PURPOSE: Artificial intelligence (AI) models are playing an increasing role in biomedical research and healthcare services. This review focuses on challenges points to be clarified about how to develop AI applications as clinical decision support systems in the real-world context. METHODS: A narrative review has been performed including a critical assessment of articles published between 1989 and 2021 that guided challenging sections. RESULTS: We first illustrate the architectural characteristic

Bias is the evaluation of something or someone that can be positive or negative, and implicit or unconscious bias is when the person is unaware of their evaluation. This is particularly relevant to policymaking during the coronavirus pandemic and racial inequality highlighted during the support for the Black Lives Matter movement. A literature review was performed to define bias, identify the impact of bias on clinical practice and research as well as clinical decision making (cognitive bias). B

THE ETHICAL ALGORITHM: The Science of Socially Aware Algorithm Design by Michael Kearns and Aaron Roth. New York: Oxford University Press, 2019. 232 pages. Hardcover; $24.95. ISBN: 9780190948207. *Can an algorithm be ethical? That question appears to be similar to asking if a hammer can be ethical. Isn't the ethics solely related to how the hammer is used? Using it to build a house seems ethical; using it to harm another person would be immoral. *That line of thinking would be appropriate if the