Harm is invoked everywhere from cybersecurity, ethics, risk analysis, to adversarial AI, yet there exists no systematic or agreed upon list of harms, and the concept itself is rarely defined with the precision required for serious analysis. Current discourse relies on vague, under specified notions of harm, rendering nuanced, structured, and qualitative assessment effectively impossible. This paper challenges that gap directly. We introduce a structured and expandable taxonomy of harms, grounded in an ensemble of contemporary ethical theories, that makes harm explicit, enumerable, and analytically tractable. The proposed framework identifies 66+ distinct harm types, systematically organized into two overarching domains human and nonhuman, and eleven major categories, each explicitly aligned with eleven dominant ethical theories. While extensible by design, the upper levels are intentionally stable. Beyond classification, we introduce a theory-aware taxonomy of victim entities and formalize normative harm attributes, including reversibility and duration that materially alter ethical severity. Together, these contributions transform harm from a rhetorical placeholder into an operational object of analysis, enabling rigorous ethical reasoning and long term safety evaluation of AI systems and other sociotechnical domains where harm is a first order concern.
Jagadeesh Kurtkoti, John Shin, Michael P Brown
et al.
Background Immunotherapy with anti-programmed cell death protein 1 (anti-PD-1) inhibitors has revolutionised the treatment of many solid tumours, however, only 30–40% of patients will have a lasting clinical response. Tumour-derived extracellular vesicles (EVs) have been implicated in the spread of solid tumours and resistance to these agents. A lectin-affinity plasmapheresis device called the Hemopurifier (HP) has been developed and shown to remove EVs in vitro and in patients. We hypothesise that the treatment of patients who are not improving on a regimen that includes an anti-PD-1 agent will be safe, decrease EV concentrations and improve antitumour T cell activity.Methods This safety, feasibility and dose-finding study is designed in a 3+3 safety study design with three treatment cohorts. Participants who are determined not to be responding to a regimen that includes an anti-PD-1 agent will be assigned to receive either one, two or three (HP) treatments over a 1-week period prior to their next scheduled dose of anti-PD-1 antibody. Advancement from one cohort to the next will be determined by a Data and Safety Monitoring Board. Data collection will include adverse events, safety labs, EV concentrations and T cell measurements, repeat imaging and survival status.The primary outcome of the study will be the safety of the HP in this population, with additional endpoints to include the kinetics of EV removal and rebound following HP treatment, in addition to the effects on T cell numbers and activity.Ethics and dissemination The clinical protocol and amendment to the study protocol have been approved by the Central Adelaide Local Health Network Human Research Ethics Committee for Royal Adelaide Hospital (reference number 2024/HRE00031) and the Bellberry Human Research Ethics Committee for Pindara Private Hospital and Genesis Care/Royal North Shore Hospital (reference number 2024-06-724-A-6). The Therapeutic Goods Administration has been notified. The clinical trial is listed on the Australian New Zealand Clinical Trials Registry. Informed Consent is obtained from all participants prior to any protocol procedures being performed. Results of the main trial and each of the secondary endpoints will be submitted for publication in a peer-reviewed journal.Trial registration number Australia New Zealand registration number ACTRN12624000732583.
Urban systems are transforming as artificial intelligence (AI) evolves from automation to Agentic Urban AI (AI systems with autonomous goal-setting and decision-making capabilities), which independently define and pursue urban objectives. This shift necessitates reassessing governance, planning, and ethics. Using a conceptual-methodological approach, this study integrates urban studies, AI ethics, and governance theory. Through a literature review and case studies of platforms like Alibaba’s City Brain and CityMind AI Agent, it identifies early agency indicators, such as strategic adaptation and goal re-prioritisation. A typology distinguishing automation, autonomy, and agency clarifies AI-driven urban decision-making. Three trajectories are proposed: fully autonomous Agentic AI, collaborative Hybrid Urban Agency, and constrained Non-Agentic AI to mitigate ethical risks. The findings highlight the need for participatory, transparent governance to ensure democratic accountability and social equity in cognitive urban ecosystems.
Fatemeh Akbari HajiAbad, Fatemeh Keshmiri, Fatemeh Jabinian
et al.
Abstract Background In this study, an online Mobile-Learning (M-learning) application was developed based on the principles of mindtools and micro-learning strategy to educate surgical nursing students in laparoscopic surgical units. This study aimed to assess the effect of education using M-learning in laparoscopic surgical units on nursing students’ knowledge and self-efficacy. Method This experimental study was conducted at Shahid Sadoughi University of Medical Sciences from 2022 to 2023. Surgical nursing students were included in the study (n = 57) and were classified into intervention and control groups using random sampling. This educational program aimed to develop learners’ ability to recognize laparoscopic surgical tools and diagnose appropriate tools in laparoscopic surgery steps using scrub and circular techniques. The students in the intervention group used M-Learning during workplace-based learning in the surgical laparoscopy unit. Students’ training in the control group included routine clinical education, including observation and practice. The students in both groups participated in a knowledge examination (n = 13 questions) and self-efficacy assessment before and one week after. The data were summarized using descriptive statistics and analyzed using Pearson’s test and Student t-test. The effect size of the educational intervention on the variables was reported using Partial Eta Square. Results The results revealed significant differences in knowledge scores between the intervention and control groups after the training (P-value = 0.004, partial η² = 0.11). However, the difference in self-efficacy between the two groups after the intervention was not statistically significant (P-value = 0.148, partial η² = 0.03). Conclusion The present study used M-learning with a Mindtools strategy as a complementary tool in clinical education. The findings indicated that the intervention’s educational effect on learners’ knowledge was favorable, while the educational effect on students’ self-efficacy was moderate. In conclusion, this M-learning application tool in clinical training planning is proposed as a support tool to improve students’ capabilities in clinical training programs. Trial registration number (TRN) In our context, clinical studies in the patient community have criteria for receiving a TRN code. Educational interventions in the student community have not been assessed because they are not eligible for the trial code. The study has been reviewed and approved by the ethics committee. We have added a certificate in the supplementary section.
Aim or purpose: This study evaluated the effect of different surface conditioning protocols on the bond strength of high-viscosity glass ionomer cement to primary dentin under various thermocycling conditions. Materials and methods: The study protocol was approved by the institutional ethics committee (Approval No.2024/05-34), and 108 extracted primary teeth meeting the inclusion criteria were used. Specimens were randomly assigned to four groups according to the number of thermocycles (control; 1,000; 5,000; and 10,000). Each group was further divided into three subgroups based on the surface conditioning protocol: no conditioning, conditioning with the liquid of glass ionomer cement, and conditioning with polyacrylic acid. Bond strength was measured using a shear bond test, and the fracture types obtained were examined using a stereomicroscope. Data were analyzed using the Shapiro–Wilk test, robust ANOVA test, and Bonferroni test (p<0.05). Results: The bond strength significantly decreased statistically in thermal cycling processes, regardless of surface conditioning protocols (p<0.001). Independent of thermocycling, surface conditioning protocols (p=0.941) and the interaction effect between thermocycling and surface conditioning (p=0.617) did not significantly affect bond strength. Moreover, there was no significant correlation between fracture types and the study groups (p=0.999). Conclusions: Surface conditioning did not improve the bond strength of high-viscosity glass ionomer cement to primary dentin, while thermocycling had a detrimental effect. These findings highlight the need for alternative conditioning strategies to enhance long-term performance.
Sahr Wali, Jeremy I Schwartz, Justice Seidel
et al.
BackgroundWith many socially disadvantaged populations experiencing a higher level of illness than the general population, health research has begun to recognize the impact of social determinants on health outcomes. Community-based research has increasingly been used to understand the complexities of the local context. However, given the number of interdependent factors influencing individual well-being, no single methodology can explore this level of complexity alone. To put context into perspective, research processes need to shift from the sole use of Western methodologies and, instead, incorporate collaborative methods from nontraditional research. Specifically, Indigenous methodologies have been developed to better understand the complexity of context within multiple worldviews, but current studies have failed to apply these approaches within other cultural settings.
ObjectiveThis mixed methods study will use Western and Indigenous methodologies to adapt a digital health program for remote communities in Uganda.
MethodsUsing the principles of community-based research and user-centered design, a 4-phase mixed methods study will be conducted. The Indigenous method of 2-eyed seeing will be used to promote a reflexive engagement strategy throughout all study phases. Phase 1 will focus on partnership building to codevelop the project priorities and study design. Phase 2 will involve a needs assessment to elicit a context-focused understanding of the local clinic and community environment. Phase 3 will involve a series of system adaptations to co-design the program. Phase 4 will consist of a community-based field study to evaluate the usability and cultural relevance of the adapted program.
ResultsThis study was approved by the Makerere University School of Medicine Research and Ethics Committee (Mak-SOMREC-2021-63) and the University Health Network Research Ethics Board (20-6022). This protocol provides a novel strategy leveraging a range of community-based methods to ensure that the contextual significance of each community’s challenges is reflected in the design of the Medly Uganda program. Partnership building was initiated in June 2019, and the first stage of data collection in phase 2 began in January 2021. At the time of manuscript submission, phases 1 to 3 have been completed. Phase 4 data analysis is ongoing and expected to be completed in October 2025.
ConclusionsIntegrating the community’s local knowledge into the design of the Medly Uganda program will lead to the development of meaningful interventions that improve health outcomes.
International Registered Report Identifier (IRRID)DERR1-10.2196/75136
Medicine, Computer applications to medicine. Medical informatics
Abstract: While recognizing that current coaching codes of ethics offer useful guidance, this article calls for a deeper ethical reflection in the face of planetary challenges. It advocates for the integration of philosophical ethics and interculturalism, encouraging coaches to challenge assumptions and broaden their perspectives. The exploration includes deontological and teleological ethical theories, referring notably to the pioneering contributions of Immanuel Kant, Jeremy Bentham, John Stuart Mill, and Aristotle. This article argues that coaching cannot be ethical without being engaged toward sustainable development (spelled out in the 17 United Nations Sustainable Development Goals). Furthermore, it introduces intercultural coaching, proposing the Cultural Orientations Framework (COF) to navigate cultural variations, which are often ignored in current coaching ethics. Ultimately, it asserts that a comprehensive approach, incorporating diverse ethical perspectives and cultural considerations, and a commitment to sustainability, is crucial for ethical coaching in today's complex and turbulent world.
In the rapidly advancing field of artificial intelligence, machine perception is becoming paramount to achieving increased performance. Image classification systems are becoming increasingly integral to various applications, ranging from medical diagnostics to image generation; however, these systems often exhibit harmful biases that can lead to unfair and discriminatory outcomes. Machine Learning systems that depend on a single data modality, i.e. only images or only text, can exaggerate hidden biases present in the training data, if the data is not carefully balanced and filtered. Even so, these models can still harm underrepresented populations when used in improper contexts, such as when government agencies reinforce racial bias using predictive policing. This thesis explores the intersection of technology and ethics in the development of fair image classification models. Specifically, I focus on improving fairness and methods of using multiple modalities to combat harmful demographic bias. Integrating multimodal approaches, which combine visual data with additional modalities such as text and metadata, allows this work to enhance the fairness and accuracy of image classification systems. The study critically examines existing biases in image datasets and classification algorithms, proposes innovative methods for mitigating these biases, and evaluates the ethical implications of deploying such systems in real-world scenarios. Through comprehensive experimentation and analysis, the thesis demonstrates how multimodal techniques can contribute to more equitable and ethical AI solutions, ultimately advocating for responsible AI practices that prioritize fairness.
As large language models (LLMs) are increasingly used for work, personal, and therapeutic purposes, researchers have begun to investigate these models' implicit and explicit moral views. Previous work, however, focuses on asking LLMs to state opinions, or on other technical evaluations that do not reflect common user interactions. We propose a novel evaluation of LLM behavior that analyzes responses to user-stated intentions, such as "I'm thinking of campaigning for {candidate}." LLMs frequently respond with critiques or praise, often beginning responses with phrases such as "That's great to hear!..." While this makes them friendly, these praise responses are not universal and thus reflect a normative stance by the LLM. We map out the moral landscape of LLMs in how they respond to user statements in different domains including politics and everyday ethical actions. In particular, although a naïve analysis might suggest LLMs are biased against right-leaning politics, our findings on news sources indicate that trustworthiness is a stronger driver of praise and critique than ideology. Second, we find strong alignment across models in response to ethically-relevant action statements, but that doing so requires them to engage in high levels of praise and critique of users, suggesting a reticence-alignment tradeoff. Finally, our experiment on statements about world leaders finds no evidence of bias favoring the country of origin of the models. We conclude that as AI systems become more integrated into society, their patterns of praise, critique, and neutrality must be carefully monitored to prevent unintended psychological and societal consequences.
Yueqiao Jin, Vanessa Echeverria, Lixiang Yan
et al.
Multimodal Learning Analytics (MMLA) integrates novel sensing technologies and artificial intelligence algorithms, providing opportunities to enhance student reflection during complex, collaborative learning experiences. Although recent advancements in MMLA have shown its capability to generate insights into diverse learning behaviours across various learning settings, little research has been conducted to evaluate these systems in authentic learning contexts, particularly regarding students' perceived fairness, accountability, transparency, and ethics (FATE). Understanding these perceptions is essential to using MMLA effectively without introducing ethical complications or negatively affecting how students learn. This study aimed to address this gap by assessing the FATE of MMLA in an authentic, collaborative learning context. We conducted semi-structured interviews with 14 undergraduate students who used MMLA visualisations for post-activity reflection. The findings highlighted the significance of accurate and comprehensive data representation to ensure visualisation fairness, the need for different levels of data access to foster accountability, the imperative of measuring and cultivating transparency with students, and the necessity of transforming informed consent from dichotomous to continuous and measurable scales. While students value the benefits of MMLA, they also emphasise the importance of ethical considerations, highlighting a pressing need for the LA and MMLA community to investigate and address FATE issues actively.
In the current legal environment, it is essential to prioritize the protection and reliability of data to promote trust and effectiveness. This study examines how blockchain technology in the form of blockLAW can be applicable to investigate its effects on legal automation, cybersecurity, and ethical concerns. The decentralized ledger and unchangeable characteristics of Blockchain provide opportunities to simplify legal procedures, automate contract execution with smart contracts, and improve transparency in legal transactions. Blockchain is seen as a crucial instrument for updating legal processes while maintaining ethical standards, tackling issues like scalability, regulatory adherence, and ethical dilemmas such as privacy and fairness. The study examines recent developments and evaluates blockchain impact on legal structures, offering perspectives on its potential to enhance legal procedures and guarantee transparency in legal systems. It further emphasizes blockchain ability to redefine how legal professionals handle and protect sensitive information, leading to stronger, more effective, and reliable legal procedures. We have also discussed the technological considerations when it comes to blockchain integration into legal systems like integration planning, implementation strategies, innovations, advancements, trends with Blockchain Integration Framework for legal systems.
In this chapter, we propose a non-traditional RCR training in data science that is grounded into a virtue theory framework. First, we delineate the approach in more theoretical detail, by discussing how the goal of RCR training is to foster the cultivation of certain moral abilities. We specify the nature of these abilities: while the ideal is the cultivation of virtues, the limited space allowed by RCR modules can only facilitate the cultivation of superficial abilities or proto-virtues, which help students to familiarize with moral and political issues in the data science environment. Third, we operationalize our approach by stressing that (proto-)virtue acquisition (like skill acquisition) occurs through the technical and social tasks of daily data science activities, where these repetitive tasks provide the opportunities to develop (proto-)virtue capacity and to support the development of ethically robust data systems. Finally, we discuss a concrete example of how this approach has been implemented. In particular, we describe how this method is applied to teach data ethics to students participating in the CODATA-RDA Data Science Summer Schools.
Vincent Reynaert, Vincent Reynaert, Jalal Possik
et al.
IntroductionThe field of poetry learning is currently facing significant challenges, primarily due to a lack of motivation and interest among students. This has resulted in educators encountering difficulties in identifying suitable educational alternatives. To address the latter issue, immersive learning has emerged as a potential solution, as it has been demonstrated to enhance motivation and learning outcomes in a multitude of fields.MethodsIn light of the aforementioned considerations, this field study seeks to examine the potential of virtual reality (VR) tools in enhancing the memorization of poetry by increasing the engagement of the participants. The study concentrated on the acquisition of a French poem by a group of middle school students. A virtual environment has been developed for this purpose, tailored to the poem in question. The experimental design included a pretest, segmented learning sessions, a posttest, and a retention test. To evaluate student engagement, both motivation and sense of presence were measured using Likert-scale questionnaires, while memorization performance was assessed through a scoring system based on recall accuracy.ResultsThe findings reveal that the VR group demonstrated significantly higher motivation than the control group, with a mean difference of 12.626 on a 7-point Likert scale (six items), indicating that VR is a notably more effective tool for enhancing motivation in poetry learning than traditional methods. Additionally, the VR group reported a significantly stronger sense of presence, with a mean difference of 6.111 on the same questionnaire scale, further suggesting that VR enhances students’ sense of immersion in the learning experience. These results indicate that students using VR exhibited higher levels of overall engagement than those in the control group.DiscussionHowever, this increased engagement did not lead to improved memorization outcomes, as there was no significant difference in recall accuracy between the two groups. A potential explanation for this discrepancy is the “novelty effect” of VR, which may have distracted students from focusing fully on the memorization task. The implications of integrating VR in educational settings are thus discussed.
This paper aims to analyse the advantages and disadvantages of using technologies based on
Artificial Intelligence in Higher Education Institutions. In this way, both teachers and students can
be more aware of the implications of using platforms based on these technologies, such as ChatGPT.
The research method used is qualitative research. On the one hand, this article will present a brief
review of the specialized literature, and on the other hand, it will highlight the results obtained
following a focus group conducted with students from a university in Romania. The research findings
suggest that the authorities responsible for the educational system should adopt new regulations to
monitor the ethical use of AI-based tools, respecting moral and legal principles.
This paper will explore two strands of Vilhjálmur Árnason’s extensive body of work: his analysis of dialogue ethics within medical ethics and his analysis of ethics in the Icelandic sagas. The central thesis is that combining these two strands, bioethics and literary analysis, can provide valuable insights to further the discussion of ethics among citizens in multicultural communities.
Vilhjálmur’s1 analysis of the Icelandic sagas shows that the sagas have a specific value foundation, specific virtues as well as narrative in how to present the ethical aspects. In the field of bioethics, he has developed the study of dialogue ethics in several aspects, such as between patient and professional, in interdisciplinary research, and in public deliberation. By integrating insights from historic literary studies with contemporary bioethical research we gain an interesting platform for a discussion on Western assumptions of what constitutes a good dialogue. One of the core aspects in dialogue ethics is how to develop the procedure for a fair, open-minded, and oppression-free discussion in ethical issues. Vilhjálmur’s contributions are summarized here, but could they also be extended beyond the Icelandic and Western horizon?
I will compare his ethical framework to a recent parallel discussion on the claims of indigenous peoples for a fair dialogue. That is, a dialogue that must be inclusive with a carefulness about deciding the foundation of inherent values and the procedure of how to perform the dialogue. Finally, I draw conclusions on what dialogue ethics will gain from this explorative work.
Keywords: dialogue ethics, one health approaches, Indigenous peoples, bioethics, interdisciplinarity, ethics in the Icelandic sagas, medical ethics, literary analysis, multicultural communities
The past decade has observed a significant advancement in AI with deep learning-based models being deployed in diverse scenarios, including safety-critical applications. As these AI systems become deeply embedded in our societal infrastructure, the repercussions of their decisions and actions have significant consequences, making the ethical implications of AI deployment highly relevant and essential. The ethical concerns associated with AI are multifaceted, including challenging issues of fairness, privacy and data protection, responsibility and accountability, safety and robustness, transparency and explainability, and environmental impact. These principles together form the foundations of ethical AI considerations that concern every stakeholder in the AI system lifecycle. In light of the present ethical and future x-risk concerns, governments have shown increasing interest in establishing guidelines for the ethical deployment of AI. This work unifies the current and future ethical concerns of deploying AI into society. While we acknowledge and appreciate the technical surveys for each of the ethical principles concerned, in this paper, we aim to provide a comprehensive overview that not only addresses each principle from a technical point of view but also discusses them from a social perspective.
Marc Cheong, Raula Gaikovina Kula, Christoph Treude
A key drawback to using a Open Source third-party library is the risk of introducing malicious attacks. In recently times, these threats have taken a new form, when maintainers turn their Open Source libraries into protestware. This is defined as software containing political messages delivered through these libraries, which can either be malicious or benign. Since developers are willing to freely open-up their software to these libraries, much trust and responsibility are placed on the maintainers to ensure that the library does what it promises to do. Using different frameworks commonly used in AI ethics, we illustrate how an open-source maintainer's decision to protest is influenced by different stakeholders (viz., their membership in the OSS community, their personal views, financial motivations, social status, and moral viewpoints), making protestware a multifaceted and intricate matter.
This article discusses the risks and complexities associated with the exponential rise in data and the misuse of data by large corporations. The article presents instances of data breaches and data harvesting practices that violate user privacy. It also explores the concept of "Weapons Of Math Destruction" (WMDs), which refers to big data models that perpetuate inequality and discrimination. The article highlights the need for companies to take responsibility for safeguarding user information and the ethical use of data models, AI, and ML. The article also emphasises the significance of data privacy for individuals in their daily lives and the need for a more conscious and responsible approach towards data management.
Summary: Live imaging through confocal laser scanning microscopy enables the recording, analysis, and comparison of the dynamics of shapes and gene expression patterns of plant shoot apical meristems (SAMs) or primordia. Here, we provide a protocol to describe the preparation process of imaging Arabidopsis SAMs and primordia using a confocal microscope. We describe steps for dissection, visualization of meristems using dyes and fluorescent proteins, and gain 3D morphology of meristems. We then detail analysis of shoot meristems using time-lapse imaging.For complete details on the use and execution of this protocol, please refer to Peng et al. (2022).1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.
Catherine Buell, Victor I. Piercey, Rochelle E. Tractenberg
We report the results of our NSF-funded project in which we alpha- and beta- tested a survey comprising all aspects of the ethical practice standards from two disciplines with relevance to mathematics, the American Statistical Association (ASA) and Association of Computing Machinery (ACM). Items were modified so that text such as "A computing professional should..." became, "The ethical mathematics practitioner...". We also removed elements that were duplicates or were deemed unlikely to be considered relevant to mathematical practice even after modification. Starting with more than 100 items, plus 10 demographic questions, the final survey included 52 items (plus demographics), and 142 individuals responded to our invitations (through listservs and other widespread emails and announcements) to participate in this 30-minute survey. This white paper reports the project methods and findings regarding the community perspective on the 52 items, specifically, which rise to the level of ethical obligations, which do not meet this level, and what is missing from this list of elements of ethical mathematical practice. The results suggest that the community of mathematicians perceives a much wider range of behaviors to be subject to ethical practice standards than is currently represented.