Ethical Harm Research Articles

A classification of ethical harm

This paper discusses a classification of types of harm linked to actions concerning (in)justice. Although the starting point is Aristotelian practical philosophy, in this research this perspective is extended and criticised. Once a first approach to the object of the study (the harm that results for others from the agent's action or omission) has been defined, a classification of the types of harm will be established. In doing so, it will be shown where the Stagirite's proposition needs to be expanded, qualified, and even corrected. In particular, a commonly overlooked element will be highlighted: that the most perverse effect of an injustice is to make the one who commits it unjust.

Ethical implications of artificial intelligence in skin cancer diagnostics: use-case analyses.

Skin cancer is the most common cancer worldwide. Early diagnosis is crucial for improving patient survival and morbidity. Artificial intelligence (AI)-assisted smartphone applications (apps) for skin cancer potentially offer accessible, early risk assessment of suspicious skin lesions. However, the integration of novel technologies into dermatology pathways raises ethical concerns. Although ethical principles for AI governance are well known, how these principles should be applied to real-life AI apps readily available for public use is less well understood. We conducted an ethical use-case analysis of commercially available skin cancer apps to better understand the ethical issues arising from their development and use in a real-world context. Established methods for ethical analysis of clinical AI applications were applied to two popular skin cancer apps in the UK: SkinVision and Scanoma. Systematic searches of published literature, regulatory documents, and websites were conducted to review the evidence regarding app development, effectiveness, and use. Screening for inclusion was undertaken by two researchers independently. Ethical concerns were identified with reference to previously described ethical concerns and principles for AI-assisted healthcare. By conceptualising ethical principles within the use-context of skin cancer apps, we identified specific ethical issues arising throughout the AI lifecycle of both apps. One company provided extensive detail regarding algorithm development and decision-making, this information was insufficiently reported for the other app. Other concerns identified related to number, quality, and consistency of studies assessing algorithm efficacy. Limited efforts to address potential skin tone biases and exclusion of individuals with darker skin tones as target users by one app risks perpetuating existing inequalities. Inadequate regulatory oversight was identified. Findings from our ethical use-case analysis of two patient-facing AI-assisted skin cancer apps suggest inadequate incorporation of bioethical norms such as justice, responsibility and transparency into the development and deployment of both apps. Improved regulation should increase accountability. Ensuring ethics by design through integration between technology developers, dermatologists, ethicists, and the public is urgently needed to prevent the potential benefits of AI-assisted skin cancer apps being overshadowed by potential ethical harms.

The ethical problems of ‘intelligence–AI’

Abstract Interest in artificial intelligence (AI) has grown rapidly alongside the ability to examine information in far vaster quantities and from more diverse sources, and to provide previously unattainable forms of evaluation. For the intelligence community, AI offers an important solution to their data collection bottleneck, allowing the data to be processed and analysed at speeds and in ways not previously possible. While AI has received some general criticism, when it is combined with the reach, secrecy, and coercive power of the intelligence community it creates unique ethical problems. Intelligence–AI exacerbates the biases AI creates, undermines proposed transparency solutions, and creates new ethical dilemmas and harms. This article examines intelligence–AI across its collection, processing, and analysis phases. It argues that open-source does not necessarily mean ethical, as the AI collection en masse of social media data violates citizens' privacy, consent and autonomy. The article also argues that AI-aided categorization is overly reductive and perpetuates harmful social binaries, while also revealing new private information beyond what was initially shared. Finally, it argues that the secretive intelligence environment prevents critical interrogation while promoting practices that, through the coercive power of the state, cause unequal harms across society.

A Conceptual Framework for Ethical Evaluation of Machine Learning Systems

Research in Responsible AI has developed a range of principles and practices to ensure that machine learning systems are used in a manner that is ethical and aligned with human values. However, a critical yet often neglected aspect of ethical ML is the ethical implications that appear when designing evaluations of ML systems. For instance, teams may have to balance a trade-off between highly informative tests to ensure downstream product safety, with potential fairness harms inherent to the implemented testing procedures. We conceptualize ethics-related concerns in standard ML evaluation techniques. Specifically, we present a utility framework, characterizing the key trade-off in ethical evaluation as balancing information gain against potential ethical harms. The framework is then a tool for characterizing challenges teams face, and systematically disentangling competing considerations that teams seek to balance. Differentiating between different types of issues encountered in evaluation allows us to highlight best practices from analogous domains, such as clinical trials and automotive crash testing, which navigate these issues in ways that can offer inspiration to improve evaluation processes in ML. Our analysis underscores the critical need for development teams to deliberately assess and manage ethical complexities that arise during the evaluation of ML systems, and for the industry to move towards designing institutional policies to support ethical evaluations.

Ethical and practical considerations of pediatric refusal in clinical anesthesia: An educational review.

Children commonly refuse induction of anesthesia. Anesthesia providers must then decide whether to honor the child's dissent or to proceed over objection. In some circumstances, a forced induction involves restraining the child, incurring both practical and ethical harms to the patient-provider encounter. This educational review explores the practical dilemma encountered when a child dissents to induction of anesthesia. In the course of exploring this dilemma, dissent and associated terms are defined and compared, and the prominent ethical underpinnings regarding pediatric decision-making are described to clarify dissent as an ethical and practical concept. Important legal and professional standards are summarized, and practice trends are discussed to depict the current state of practice, including novel approaches to honoring pediatric dissent for elective surgeries. This information is then used to invite providers to consider where they ethically situate themselves within a legally and professionally defined space of acceptable practice. Finally, these considerations are synthesized to discuss important nuances regarding pediatric refusal, and some key questions are presented for clinicians to ponder as they consider their practice of choosing whether to honor pediatric dissent at induction.

Tracing Tomorrow: young people’s preferences and values related to use of personal sensing to predict mental health, using a digital game methodology

BackgroundUse of personal sensing to predict mental health risk has sparked interest in adolescent psychiatry, offering a potential tool for targeted early intervention.ObjectivesWe investigated the preferences and values of UK...

Ethical Review on Teachers' Relations with the School Environment

The aim of this study is to examine the relationship of teachers with the school environment from an ethical point of view, according to teachers' opinions. In the research, case study design from the qualitative research method was used. The research data were obtained by face-to-face interviews with 20 teachers working in Sultanbeyli, Pendik and Kartal districts of Istanbul. The data collection tool of the research is the semi-structured interview form developed by the researchers. The data obtained from the interviews were analyzed with the descriptive analysis technique and the participant opinions were gathered under five themes (ethic behaviors of teachers, ethical decision making, increasing ethical sensitivity, unethical behaviors and harms of unethical behaviors). As a result of the research, it was determined that the ethical competencies of the teachers should be increased, that the teachers should be a role model for their followers, and that teachers should have a large number of ethical competencies, and that teachers had some unethical behaviors that harm the environment and students. In order for teachers to make ethical decisions in their relations with the environment, it is considered important that decision makers implement functional policies to increase their ethical awareness level.

Foresight for ethical AI.

There is growing expectation that artificial intelligence (AI) developers foresee and mitigate harms that might result from their creations; however, this is exceptionally difficult given the prevalence of emergent behaviors that occur when integrating AI into complex sociotechnical systems. We argue that Naturalistic Decision Making (NDM) principles, models, and tools are well-suited to tackling this challenge. Already applied in high-consequence domains, NDM tools such as the premortem, and others, have been shown to uncover a reasonable set of risks of underlying factors that would lead to ethical harms. Such NDM tools have already been used to develop AI that is more trustworthy and resilient, and can help avoid unintended consequences of AI built with noble intentions. We present predictive policing algorithms as a use case, highlighting various factors that led to ethical harms and how NDM tools could help foresee and mitigate such harms.

Trustworthy artificial intelligence: A decision‐making taxonomy of potential challenges

AbstractThe significance of artificial intelligence (AI) trustworthiness lies in its potential impacts on society. AI revolutionizes various industries and improves social life, but it also brings ethical harm. However, the challenging factors of AI trustworthiness are still being debated. This research explores the challenging factors and their priorities to be considered in the software process improvement (SPI) manifesto for developing a trustworthy AI system. The multivocal literature review (MLR) and questionnaire‐based survey approaches are used to identify the challenging factors from state‐of‐the‐art literature and industry. Prioritization based taxonomy of the challenges is developed, which reveals that lack of responsible and accountable ethical AI leaders, lack of ethics audits, moral deskilling & debility, lack of inclusivity in AI multistakeholder governance, and lack of scale training programs to sensitize the workforce on ethical issues are the top‐ranked challenging factors to be considered in SPI manifesto. This study's findings suggest revising AI‐based development techniques and strategies, particularly focusing on trustworthiness. In addition, the results of this study encourage further research to support the development and quality assessment of ethics‐aware AI systems.

Ethical harms for migrant 24h caregivers in home care arrangements.

The glaring lack of formal and informal caregivers in Germany has not only become apparent in hospitals and nursing homes but also in home care arrangements. One tension is particularly pertinent in such arrangements: a 'family-oriented' logic of the long-term care insurance and the individual wishes of those in need of care meet the actual possibilities of family carers. This care gap has been compensated for by 24-hour care workers, so-called 'live-ins', from Eastern Europe for some years. This contribution maps the 'live-ins' situation comprehensively from an ethical perspective. Based on different constellations regarding the 'live-ins' status as a professional nurse or non-professional caregiver, which ethical principles and moral norms are affected by whom and potentially conflict with each other in such home care arrangements at a micro and meso level of care are outlined. Special attention is paid to the tension between self-care and care for others, and to questions of the shared responsibility in and social responsibility of those external services that are involved in home care in addition to the 'live-in'.In order to uncover, understand and influence the current ethical problems, an ethical framework that considers both the divergent interests of all individuals involved in the home care arrangement and their mutual dependency and vulnerability is needed.

Auditing Ethics: A Cost–Benefit Framework for Audit Studies

The audit study design offers researchers a powerful tool to measure discrimination. Yet ethical concerns surround the use of this design. In this article, we introduce a cost–benefit framework that researchers should consider when conducting audit studies. One key insight of this framework is that when researchers evaluate audit studies due to ethical concerns, they must also evaluate the relative benefits of studying discrimination through other means. Building on this framework, we provide a set of practical recommendations that researchers can adopt to (1) minimize costs (i.e. ethical harm) and (2) maximize benefits (i.e. quality of their research). We hope that our suggestions contribute to the ongoing, important discussion about ethical issues involving audit studies and generate better work on discrimination.

Structural Justice Ethics in Health Care

Structural Justice Ethics in Health Care

Beyond babies: Implications of human genome editing for women, children, and families

ABSTRACT Scholarship on human germline editing has centered on the risks to the genetically-modified child. However, far less emphasis is focused on women who will become pregnant with a genetically modified embryo as part of human subject research or the families that raise children whose genomes were modified as an embryo. The lack of attention on women and families places these key stakeholders in genomic technologies at significant medical, ethical, and personal harm as research rapidly moves forward to advance the science of genomic modification. Now is the time to address how the interests of women and families should be represented in the ethical and scientific frameworks of human genomic modification, with specific considerations for Institutional Review Boards who review protocols for rigorous human subject protections and scientists who develop scientific methodologies that dictate the potential risks conferred to research participants. In this paper, we examine the implications of genomic modification of human embryo for women, children, and families to explore how to review a first-in-human clinical protocol of human genomic officiation responsibly.

The Ethical Origin and Harm of Chinese Family Corruption

In China, family corruption is generated from the political ethics with vague boundaries between public and private, the clanism ethics with "Ever the dog swaggers when its master wins favor" and the familyism ethics with "The child and the father conceal their crimes from each other". It is very easy to speed up the corruption of public power and induce political crisis. Therefore, it is necessary to suit the remedy to the case and establish a more targeted supervision and early warning system to prevent similar problems in the future.

The Ethics of Facial Recognition Technology

This is a comprehensive presentation of leading ethical issues in debates about facial recognition technology. After defining basic terms (facial detection, facial characterization, facial verification, and facial identification), the following issues are discussed: standards, measures, and disproportionately distributed harms; erosions of trust; ethical harms associated with perfect facial surveillance; alienation, dehumanization, and loss of control; and the slippery slope debate.

Ceteris Paribus: An Epistemological Error with Ethical Consequences

Herein I call into question a common epistemological justification for placebo controls, and thereby problematize the use of placebo in many modern clinical trials. I demonstrate both the ethical harm and epistemic inferiority of placebo controls in certain knowledge contexts, arguing the standard of care should be the more acceptable comparator for novel treatments in such contexts.

Introduction to the Special Issue on Intercultural Digital Ethics

Recent advances in the capability of digital information technologies—particularly due to advances in artificial intelligence (AI)—have invigorated the debate on the ethical issues surrounding their use. However, this debate has often been dominated by ‘Western’ ethical perspectives, values and interests, to the exclusion of broader ethical and socio-cultural perspectives. This imbalance carries the risk that digital technologies produce ethical harms and lack social acceptance, when the ethical norms and values designed into these technologies collide with those of the communities in which they are delivered and deployed. This special issue takes a step towards broadening the approach of digital ethics, by bringing together a range of cultural, social and structural perspectives on the ethical issues relating to digital information technology. Importantly, it refreshes and reignites the field of Intercultural Digital Ethics for the age of AI and ubiquitous computing.

Pregnancy Centers: A Clear Purpose of Medicine with Coherent Ethics.

Pregnancy Centers embrace a traditional "treat and prevent disease" purpose of medicine.This clear and objective purpose logically leads to not advocating for abortion or contraceptives.The authors outline a coherent ethical structure outlining the role values play in regards to this purpose. This is contrasted with the current ill-defined purpose within medicine today that has led to an inconsistent change of the physician-patient relationship to a vendor-customer one, ethical incoherence, and several attendant harms, most notably sex-selective abortion.

Perceived Greenwashing: The Effects of Green Marketing on Environmental and Product Perceptions

Many firms are striving to improve their environmental positions by presenting their environmental efforts to the public. To do so, they are applying green marketing strategies to help gain competitive advantage and appeal to ecologically conscious consumers. However, not all green marketing claims accurately reflect firms’ environmental conduct, and can be viewed as ‘greenwashing’. Greenwashing may not only affect a company’s profitability, but more importantly, result in ethical harm. Therefore, this research extends past greenwashing studies by examining additional influences on and outcomes of perceived greenwashing. To do so, we conducted two studies, an interview study with consumer product and consulting firms, as well as an experiment examining consumers interacting with a company website. For these studies, we used multiple methods, including interviews, questionnaires, and neurophysiological techniques. We found that perceived greenwashing relates not only to environmental and product perceptions, but also to consumers’ happiness while interacting with the website. We also found that website interactivity relates to perceived greenwashing, environmental and product perceptions, and to the amount of interaction with the website. We conclude by discussing managerial and ethical implications for research and practice.

Exploring the Boundaries of Deception in Simulation: A Mixed-Methods Study

BackgroundDeception can be defined as causing someone to accept a falsehood as true. Within simulation, a deception is an aspect of the environment for which there is no clear agreement or knowledge among facilitators and learners about its ground rules, boundaries, or existence. The psychological literature surrounding deception is mixed, and little simulation-specific research exists. MethodsThis mixed-methods survey-based research explored attitudes for and against deception's use and facilitator perceptions of psychological risk and ethical harm. Subjects consisted of a random sample of members from three international simulation societies that included nurses, physicians, standardized patients, and educational specialists. The survey was designed and tested using an iterative process and distributed using SurveyMonkey™. Descriptive statistics and thematic analyses were performed. ResultsEighty-four (11%) of surveys were completed. Thirty-three percent of respondents currently use modification/deception, whereas 61 to 75% of respondents expressed psychological and ethical concerns. Thematic analysis yielded five themes: types of modification/deception, decision-making considerations and guardrails, never events (high risk), potential detriments, and potential benefits. ConclusionsThe use of deception appears relatively prevalent in the simulation community, but significant concerns also exist. Careful consideration of all relevant factors is needed if deception is to be used responsibly.