Hasil untuk "Ethics"

Ching‐Hsun Chang

G. Biesta

L. Shewan, A. Coats

B. Williams

H. Jonas

A. Sen

L. Wittgenstein

R. Rawbone

Jukka Ruohonen, Jani Koskinen, Søren Harnow Klausen et al.

Context: Dark patterns are user interface or other software designs that deceive or manipulate users to do things they would not otherwise do. Even though dark patterns have been under active research for a long time, including particularly in computer science but recently also in other fields such as law, systematic applied ethical assessments have generally received only a little attention. Objective: The present work evaluates ethical concerns in dark patterns and their research in software engineering and closely associated disciplines. The evaluation is extended to cover not only dark patterns themselves but also the research ethics and applied ethics involved in studying, developing, and deploying them. Method: A scenario analysis is used to evaluate six theoretical dark pattern scenarios. The ethical evaluation is carried out by focusing on the three main branches of normative ethics; utilitarianism, deontology, and virtue ethics. In terms of deontology, the evaluation is framed and restricted to the laws enacted in the European Union. Results: The evaluation results indicate that dark patterns are not universally morally bad. That said, numerous ethical issues with practical relevance are demonstrated and elaborated. Some of these may have societal consequences. Conclusion: Dark patterns are ethically problematic but not always. Therefore, ethical assessments are necessary. The two main theoretical concepts behind dark patterns, deception and manipulation, lead to various issues also in research ethics. It can be recommended that dark patterns should be evaluated on case-by-case basis, considering all of the three main branches of normative ethics in an evaluation. Analogous points apply to legal evaluations, especially when considering that the real or perceived harms caused by dark patterns cover both material and non-material harms to natural persons.

Mahamodul Hasan Mahadi, Md. Nasif Safwan, Souhardo Rahman et al.

Developing AI systems capable of nuanced ethical reasoning is critical as they increasingly influence human decisions, yet existing models often rely on superficial correlations rather than principled moral understanding. This paper introduces Ethic-BERT, a BERT-based model for ethical content classification across four domains: Commonsense, Justice, Virtue, and Deontology. Leveraging the ETHICS dataset, our approach integrates robust preprocessing to address vocabulary sparsity and contextual ambiguities, alongside advanced fine-tuning strategies like full model unfreezing, gradient accumulation, and adaptive learning rate scheduling. To evaluate robustness, we employ an adversarially filtered "Hard Test" split, isolating complex ethical dilemmas. Experimental results demonstrate Ethic-BERT's superiority over baseline models, achieving 82.32% average accuracy on the standard test, with notable improvements in Justice and Virtue. In addition, the proposed Ethic-BERT attains 15.28% average accuracy improvement in the HardTest. These findings contribute to performance improvement and reliable decision-making using bias-aware preprocessing and proposed enhanced AI model.

Vincent C. Müller

Artificial intelligence (AI) is a digital technology that will be of major importance for the development of humanity in the near future. AI has raised fundamental questions about what we should do with such systems, what the systems themselves should do, what risks they involve and how we can control these. - After the background to the field (1), this article introduces the main debates (2), first on ethical issues that arise with AI systems as objects, i.e. tools made and used by humans; here, the main sections are privacy (2.1), manipulation (2.2), opacity (2.3), bias (2.4), autonomy & responsibility (2.6) and the singularity (2.7). Then we look at AI systems as subjects, i.e. when ethics is for the AI systems themselves in machine ethics (2.8.) and artificial moral agency (2.9). Finally we look at future developments and the concept of AI (3). For each section within these themes, we provide a general explanation of the ethical issues, we outline existing positions and arguments, then we analyse how this plays out with current technologies and finally what policy consequences may be drawn.

Heather Lent, Erick Galinkin, Yiyi Chen et al.

As NLP models are used by a growing number of end-users, an area of increasing importance is NLP Security (NLPSec): assessing the vulnerability of models to malicious attacks and developing comprehensive countermeasures against them. While work at the intersection of NLP and cybersecurity has the potential to create safer NLP for all, accidental oversights can result in tangible harm (e.g., breaches of privacy or proliferation of malicious models). In this emerging field, however, the research ethics of NLP have not yet faced many of the long-standing conundrums pertinent to cybersecurity, until now. We thus examine contemporary works across NLPSec, and explore their engagement with cybersecurity's ethical norms. We identify trends across the literature, ultimately finding alarming gaps on topics like harm minimization and responsible disclosure. To alleviate these concerns, we provide concrete recommendations to help NLP researchers navigate this space more ethically, bridging the gap between traditional cybersecurity and NLP ethics, which we frame as ``white hat NLP''. The goal of this work is to help cultivate an intentional culture of ethical research for those working in NLP Security.

Viktor Dorfler, Giles Cuthbert

We paraphrase Descartes' famous dictum in the area of AI ethics where the "I doubt and therefore I am" is suggested as a necessary aspect of morality. Therefore AI, which cannot doubt itself, cannot possess moral agency. Of course, this is not the end of the story. We explore various aspects of the human mind that substantially differ from AI, which includes the sensory grounding of our knowing, the act of understanding, and the significance of being able to doubt ourselves. The foundation of our argument is the discipline of ethics, one of the oldest and largest knowledge projects of human history, yet, we seem only to be beginning to get a grasp of it. After a couple of thousand years of studying the ethics of humans, we (humans) arrived at a point where moral psychology suggests that our moral decisions are intuitive, and all the models from ethics become relevant only when we explain ourselves. This recognition has a major impact on what and how we can do regarding AI ethics. We do not offer a solution, we explore some ideas and leave the problem open, but we hope somewhat better understood than before our study.

Junrong LIU

Compared with traditional medical technologies, medical new-quality technologies demonstrate stronger autonomy, such as self-generation, replication, amplification, variation and reproduction, and can interact deeply with the intrinsic mechanisms of life systems, adapt to environmental changes dynamically, and intervene in life processes autonomously at different scales. Its intervention in natural life has led to the blurring of life boundaries and have brought more profound and ethical challenges to biosecurity, life dignity, and personal identity. Interpreting and responding to these challenges through the lens of body theory not only helps to clarify the definition of life and return to the embodied life of human beings, but also facilitates upstream governance. This approach advances ethics as a guiding principle, strengthens ethical awareness, reinforces ethical boundaries, enforces rigorous review mechanisms, and promotes global ethical co-governance.

Ghenwa Chamouni, Filippo Lococo, Carolina Sassorossi et al.

IntroductionArtificial intelligence (AI) is increasingly integrating into the healthcare field, particularly in lung cancer care, including screening, diagnosis, treatment, and prognosis. While these applications offer promising advancements, they also raise complex challenges that must be addressed to ensure responsible implementation in clinical practice. This scoping review explores the ethical and legal aspects of AI applications in lung cancer.MethodsA search was conducted across PubMed, Scopus, Web of Science, Cochrane Library, PROSPERO, OAIster, and CABI. A total of 581 records were initially retrieved, of which 20 met the eligibility criteria and were included in the review. The PRISMA guidelines were followed.ResultsThe most frequently reported ethical concern was data privacy. Other recurrent issues included informed consent, no harm to patients, algorithmic bias and fairness, transparency, equity in AI access and use, and trust. The most frequently raised legal concerns were data protection and privacy, although issues relating to cybersecurity, liability, safety and effectiveness, the lack of appropriate regulation, and intellectual property law were also noted. Solutions proposed ranged from technical approaches to calls for regulatory and policy development. However, many studies lacked comprehensive legal analysis, and most included papers originated from high-income countries. This highlights the need for a broader global perspective.DiscussionThis review found that data privacy and protection are the most prominent ethical and legal concerns in AI applications for lung cancer care. Deep Learning (DL) applications, especially in diagnostic imaging, are closely tied to data privacy, lack of transparency, and algorithmic bias. Hybrid and multimodal AI systems raise additional concerns regarding informed consent and the lack of proper regulations. Ethical issues were more frequently addressed than legal ones, with limited consideration for global applicability, particularly in low- and lower middle-income countries. Although technical and policy solutions have been proposed, these remain largely unvalidated and fragmented, with limited real-world feasibility or scalability.

Judith M. Gault, Nicola Cascella, Nidal Moukaddam et al.

Abstract Introduction Ethical concerns have been raised by both current and historically controversial neurosurgical interventions for treatment-refractory schizophrenia and schizoaffective disorder (TR-SZ). Considering advances in next-generation deep brain stimulation (DBS), initial success in treating a few cases of TR-SZ, and how challenging trial enrollment is, transparency and disseminating knowledge about DBS is important, as is input from involved groups. Here information was presented about DBS as an experimental treatment option for TR-SZ to stakeholders to gauge enthusiasm after consideration of potential risks and benefits. Methods Stakeholders were presented with information about DBS (total n = 629). Opinions about whether DBS should be an option for people with TR-SZ and acceptable response rates considering DBS risks were collected from research participants with SZ, treatment-refractory Parkinson’s disease (TR-PD) approved for DBS, caregivers for either SZ or TR-PD participants, and attendees at medical school presentations. In addition, the attendees were asked to decide whether DBS is appropriate for 4 cases who want DBS, one with PD, 2 with OCD and 1 with SZ. Chi-square, pairwise comparisons, and Duncan Multiple Range Test were performed with significance at p < 0.05. Results Most (83%) research participants and presentation audience members agreed that DBS should be an option for TR-SZ and 40% thought the potential benefits outweigh the risks of DBS with at least a 41–60% response rate. Audience approval of DBS was similar for the PD (30%), SZ (52%) and the OCD case with psychosis (56%), but there was a higher rate of approval (77%) for the OCD case whose compulsions involved self-harm. The majority (73–86%) of the audience thought that they would want to try DBS if they had TR-PD, TR-OCD, or TR-SZ. Conclusions Despite difficulty in recruiting patients for DBS clinical trials for TR-SZ, the consensus among 83% of stakeholders was that DBS should be an option for people with severe TR-SZ. Our approach to disseminate general knowledge then gather opinions among diverse stakeholders was to ensure the development of DBS clinical trials for the new indication TR-SZ is a relevant option despite the known difficulties in enrollment. These findings may help prevent disparities in access to advanced DBS therapeutics.

Irina Spiegel

This article presents a critique of ethics in the context of artificial intelligence (AI). It argues for the need to question established patterns of thought and traditional authorities, including core concepts such as autonomy, morality, and ethics. These concepts are increasingly inadequate to deal with the complexities introduced by emerging AI and autonomous agents. This critique has several key components: clarifying conceptual ambiguities, honestly addressing epistemic issues, and thoroughly exploring fundamental normative problems. The ultimate goal is to reevaluate and possibly redefine some traditional ethical concepts to better address the challenges posed by AI.

Eddie L. Ungless, Nikolas Vitsakis, Zeerak Talat et al.

This whitepaper offers an overview of the ethical considerations surrounding research into or with large language models (LLMs). As LLMs become more integrated into widely used applications, their societal impact increases, bringing important ethical questions to the forefront. With a growing body of work examining the ethical development, deployment, and use of LLMs, this whitepaper provides a comprehensive and practical guide to best practices, designed to help those in research and in industry to uphold the highest ethical standards in their work.

Kaska Porayska-Pomsta, Wayne Holmes, Selena Nemorin

The transition of Artificial Intelligence (AI) from a lab-based science to live human contexts brings into sharp focus many historic, socio-cultural biases, inequalities, and moral dilemmas. Many questions that have been raised regarding the broader ethics of AI are also relevant for AI in Education (AIED). AIED raises further specific challenges related to the impact of its technologies on users, how such technologies might be used to reinforce or alter the way that we learn and teach, and what we, as a society and individuals, value as outcomes of education. This chapter discusses key ethical dimensions of AI and contextualises them within AIED design and engineering practices to draw connections between the AIED systems we build, the questions about human learning and development we ask, the ethics of the pedagogies we use, and the considerations of values that we promote in and through AIED within a wider socio-technical system.

Massimiliano Esposito, Konrad Szocik, Emanuele Capasso et al.

Abstract Background Respect for human rights and bioethical principles in prisons is a crucial aspect of society and is proportional to the well-being of the general population. To date, these ethical principles have been lacking in prisons and prisoners are victims of abuse with strong repercussions on their physical and mental health. Methods A systematic review was performed, through a MESH of the following words (bioethics) AND (prison), (ethics) AND (prison), (bioethics) AND (jail), (ethics) AND (jail), (bioethics) AND (penitentiary), (ethics) AND (penitentiary), (prison) AND (human rights). Inclusion and exclusion criteria were defined and after PRISMA, 17 articles were included in the systematic review. Results Of the 17 articles, most were prevalence studies (n.5) or surveys (n.4), followed by cross-sectional studies (n.3), qualitative studies (n.1), retrospective (n.1) and an explanatory sequential mixed-methods study design (n.1). In most cases, the studies associated bioethics with prisoners’ access to treatment for various pathologies such as vaccinations, tuberculosis, hepatitis, HIV, it was also found that bioethics in prisons was related to the mental health of prisoners, disability, ageing, the condition of women, the risk of suicide or with the request for end-of-life by prisoners. The results showed shortcomings in the system of maintaining bioethical principles and respect for human rights. Conclusions Prisoners, in fact, find it difficult to access care, and have an increased risk of suicide and disability. Furthermore, they are often used as improper organ donors and have constrained autonomy that also compromises their willingness to have end-of-life treatments. In conclusion, prison staff (doctors, nurses, warders, managers) must undergo continuous refresher courses to ensure compliance with ethical principles and human rights in prisons.