Regulating AI in Mental Health: Ethics of Care Perspective.

This article contends that the responsible artificial intelligence (AI) approach-which is the dominant ethics approach ruling most regulatory and ethical guidance-falls short because it overlooks the impact of AI on human relationships. Focusing only on responsible AI principles reinforces a narrow concept of accountability and responsibility of companies developing AI. This article proposes that applying the ethics of care approach to AI regulation can offer a more comprehensive regulatory and ethical framework that addresses AI's impact on human relationships. This dual approach is essential for the effective regulation of AI in the domain of mental health care. The article delves into the emergence of the new "therapeutic" area facilitated by AI-based bots, which operate without a therapist. The article highlights the difficulties involved, mainly the absence of a defined duty of care toward users, and shows how implementing ethics of care can establish clear responsibilities for developers. It also sheds light on the potential for emotional manipulation and the risks involved. In conclusion, the article proposes a series of considerations grounded in the ethics of care for the developmental process of AI-powered therapeutic tools.

Perceptions of politics and knowledge sharing: Moderating role of Islamic work ethic in the Islamic banking industry of Pakistan.

Knowledge is the most critical source for sustainable competitive advantage in an organization. Organizations must therefore focus on creating a culture for Knowledge Sharing. However, politics or political work environment is an inevitable outcome where people interact and communicate on a daily basis. Politics discourage Knowledge Sharing and its related efforts in the organization. However, Islamic Work Ethic can remove negativity and strengthen Knowledge Sharing behaviors in the organization. This research focused on the moderating role of Islamic Work Ethic between Perceptions of Politics and Knowledge Sharing behaviors. The data was gathered using a structured questionnaire from the Banking Industry in Karachi, Pakistan using unrestricted non-probability convenience sampling technique. Hypotheses was tested using Structural Equation Modelling in SMART-PLS software. It was found that Perceptions of Politics reduces Knowledge Sharing Behaviors while Islamic Work Ethic Increase Knowledge Sharing Behaviors. Islamic Work Ethic moderates the relationship between Perceptions of Politics and Knowledge Sharing Behaviors. Employees religious orientations affect their work-related outcomes and behaviors. Islamic Banks have been mostly neglected as they have recently emerged as an important part of the economy therefore this study contributes to the literature by collaborating Islamic Work Ethic, Perceptions of Politics and Knowledge Sharing.

Seeing the human behind the research: Strengthening emerging African disability researchers.

Background: A pre-conference workshop that investigated the experiences and needs of PhD candidates and early career researchers in disability studies in Africa was held as part of the proceedings of the African Network for Evidence to Action in Disability (AfriNEAD) 7th Conference in November 2023. Objectives: To determine how the existing structures in AfriINEAD can be leveraged to support emerging African disability researchers. This article documents this event and summarises the key findings from the discussions that took place. Method: The workshop included presentations from leading scholars in health professions education, panel discussions with PhD candidates and early career researchers, and small group discussions on what is needed to support emerging researchers. Results: Disability studies was positioned by participants as not only an academic exercise but also a deeply personal pursuit, requiring introspection and conscientisation, with which they felt they needed support. There are also specific ethical concerns related to doing research work with persons with disabilities, which need to be prioritised in postgraduate education in disability studies. The needs identified by participants are summarised as: (1) mentorship, (2) networking, and (3) funding. Conclusion: We suggest that the development of African disability scholars and scholarship requires an African ethical approach, which prioritises humanity, community and reciprocity. Contribution: African disability studies scholars are well-placed to disrupt ableism in academic, medical and social spheres, as well as hierarchies within academia, which limit development, mutual growth and respect.

Understand nurse's perspectives on communication with Chinese-speaking cardiac patients.

OBJECTIVES: To explore communication experiences, resource accessibility/quality, and communication strategies cardiac nurses use when caring for Chinese-speaking patients. METHODS: In this exploratory qualitative study, nurses were recruited from professional association members and interviewed on communication barriers/facilitators, resource accessibility/quality, and communication strategies used when caring for Chinese-speaking cardiac patients. Transcripts were thematically analysed. RESULTS: Nurses (n = 11) were primarily female (7/11), with 2/11 Chinese-speaking. The themes discussed centred on two areas that created difficulty in communication, including the lack of a common language and uncertainty of the Chinese culture. Dependence on interpreters was highlighted and challenges noted included limited availability and difficulty scheduling, variable quality and approaches, and lack of communication resources leading to a dependence on poor quality materials. Nurses were uncertain about Chinese culture and how to communicate, particularly in relation to family-centred beliefs, mental and sexual health, medication, and diet. CONCLUSIONS: Health communication with Chinese-speaking patients needs to address multiple challenges to be effective. PRACTICE IMPLICATIONS: The findings emphasise the need to optimise interpreting services and provide nurses with cultural competency training and tailored resources to improve their understanding of Chinese immigrants' needs. These recommendations will support nurses to address identified language and cultural uncertainties.

Associations between protestant work ethic and multilevel marketing participation and financial outcomes.

Multilevel marketing (MLM) involvement can adversely affect consumer wellbeing. We examine how individual beliefs about work predict participation and financial losses in MLMs. As MLMs are presented to the marketplace as low-barrier opportunities to start one's own business, we suggest that this may speak directly to people who strongly endorse Protestant work ethic (PWE), making them more inclined toward MLM participation, and financial outcomes associated with that participation. Using a place-based (county level) MLM data set from the Federal Trade Commission (FTC; n = 326,487), and a consumer survey (n = 515), we find evidence that PWE is positively associated with participation in MLMs (studies 1 and 2), and that PWE predicts estimated financial losses among those who lost $1000 or more (study 1) but financial gains in a more general sample of MLM participants (study 2). Implications for research, marketing, and consumer advocacy are discussed.

ChatGPT in Trauma Triage.

How to cite this article: Mungmunpuntipantip R, Wiwanitkit V. ChatGPT in Trauma Triage. Indian J Crit Care Med 2024;28(6):617.

[Time for assessing a clinical research project by one of 39 French ethic committees: A retrospective study between 2019 and 2023]. / Délai d'obtention d'une première décision finale par un Comité de Protection des Personnes pour débuter une étude de recherche clinique en France : Etude rétrospective entre 2019 et 2023.

INTRODUCTION: The study reported the time (from the initial submission to the final decision) to evaluate a clinical research project by one of the 39 French national ethics committees. The times from this final decision to the first participant inclusion and study achievement (first patient inclusion to the end of the last patient's follow-up) were also reported. METHODS: Clinical research projects submitted between January 1st 2019 and June 30th 2023 were analyzed according to their type (research on drugs, clinical investigations, performance studies, research implying human person), and the promotor (industry, university hospital, general hospital, private medical institution, others). The times of assessment of the project by the ethic committee (from the initial submission to the final decision), of the first participant inclusion (from the approval of the project) and of study achievement (first patient inclusion to the end of the last patient's follow-up) were calculated. RESULTS: Among 467 submitted clinical research projects, 424 were approved (90.8 %). The median time [Q1-Q3] to evaluate a project was 73 days [51-98] whatever the types of projects and promotors. In 307 accepted projects, the first patient inclusion occurred after 134 days [61-237] and was being waited for 347 days [306-510] in 39 other ones. In 122 projects, the time for study achievement was 446 days [230-731]. In 185 other projects, the inclusions were still in progress for 699 days [397-1098]. CONCLUSION: In this concerned ethic committee, a final decision was edited after a median assessment time of 73 days (with >90 % approvals), shorter than the times to include the first patient and for achieving the study.

Practice of neurosurgery on Saturn.

BACKGROUND: The practice of neurosurgery on Saturn is almost identical to the one practiced on Earth. Because the art of practice of Medicine was transferred by homo sapiens sapiens "exearthed" 30 years ago from Earth to Saturn. METHODS: In this paper, we present three neurosurgical cases and provided management of them on Saturn. RESULTS: In two cases, the patients underwent surgery; in one case, the surgical indication was not established. CONCLUSION: These cases give us the means of reflection to improve the practice of Neurosurgery.

Human-AI Teaming in Critical Care: A Comparative Analysis of Data Scientists' and Clinicians' Perspectives on AI Augmentation and Automation.

BACKGROUND: Artificial intelligence (AI) holds immense potential for enhancing clinical and administrative health care tasks. However, slow adoption and implementation challenges highlight the need to consider how humans can effectively collaborate with AI within broader socio-technical systems in health care. OBJECTIVE: In the example of intensive care units (ICUs), we compare data scientists' and clinicians' assessments of the optimal utilization of human and AI capabilities by determining suitable levels of human-AI teaming for safely and meaningfully augmenting or automating 6 core tasks. The goal is to provide actionable recommendations for policy makers and health care practitioners regarding AI design and implementation. METHODS: In this multimethod study, we combine a systematic task analysis across 6 ICUs with an international Delphi survey involving 19 health data scientists from the industry and academia and 61 ICU clinicians (25 physicians and 36 nurses) to define and assess optimal levels of human-AI teaming (level 1=no performance benefits; level 2=AI augments human performance; level 3=humans augment AI performance; level 4=AI performs without human input). Stakeholder groups also considered ethical and social implications. RESULTS: Both stakeholder groups chose level 2 and 3 human-AI teaming for 4 out of 6 core tasks in the ICU. For one task (monitoring), level 4 was the preferred design choice. For the task of patient interactions, both data scientists and clinicians agreed that AI should not be used regardless of technological feasibility due to the importance of the physician-patient and nurse-patient relationship and ethical concerns. Human-AI design choices rely on interpretability, predictability, and control over AI systems. If these conditions are not met and AI performs below human-level reliability, a reduction to level 1 or shifting accountability away from human end users is advised. If AI performs at or beyond human-level reliability and these conditions are not met, shifting to level 4 automation should be considered to ensure safe and efficient human-AI teaming. CONCLUSIONS: By considering the sociotechnical system and determining appropriate levels of human-AI teaming, our study showcases the potential for improving the safety and effectiveness of AI usage in ICUs and broader health care settings. Regulatory measures should prioritize interpretability, predictability, and control if clinicians hold full accountability. Ethical and social implications must be carefully evaluated to ensure effective collaboration between humans and AI, particularly considering the most recent advancements in generative AI.

Knowledge and attitudes toward artificial intelligence in nursing among various categories of professionals in China: a cross-sectional study.

Objectives: The application of artificial intelligence (AI) in healthcare is an important public health issue. However, few studies have investigated the perceptions and attitudes of healthcare professionals toward its applications in nursing. This study aimed to explore the knowledge, attitudes, and concerns of healthcare professionals, AI-related professionals, and others in China toward AI in nursing. Methods: We conducted an online cross-sectional study on nursing students, nurses, other healthcare professionals, AI-related professionals, and others in China between March and April 2024. They were invited to complete a questionnaire containing 21 questions with four sections. The survey followed the principle of voluntary participation and was conducted anonymously. The participants could withdraw from the survey at any time during the study. Results: This study obtained 1,243 valid questionnaires. The participants came from 25 provinces and municipalities in seven regions of China. Regarding knowledge of AI in nursing, 57% of the participants knew only a little about AI, 4.7% did not know anything about AI, 64.7% knew only a little about AI in nursing, and 13.4% did not know anything about AI in nursing. For attitudes toward AI in nursing, participants were positive about AI in nursing, with more than 50% agreeing and strongly agreeing with each question on attitudes toward AI in nursing. Differences in the numbers of participants with various categories of professionals regarding knowledge and attitudes toward AI in nursing were statistically significant (p < 0.05). Regarding concerns and ethical issues about AI in nursing, every participant expressed concerns about AI in nursing, and 95.7% of participants believed that it is necessary to strengthen medical ethics toward AI in nursing. Conclusion: Nursing students and healthcare professionals lacked knowledge about AI or its application in nursing, but they had a positive attitude toward AI. It is necessary to strengthen medical ethics toward AI in nursing. The study's findings could help develop new strategies benefiting healthcare.

Making Sense of "Ethics" of War: Just War, Just Peace, and Ethic of Care.

This paper reviews briefly the main approaches in the literature on ethics of war and suggests the need to move beyond an ethic of justice towards an ethic of care. The analysis problematizes dominant understandings of "just war" and "just peace" in the literature and highlights that incorporating elements of an ethic of care, our understanding of ethics of war and peace can be redefined, sharpened, and redeployed through an enlarged ethical lens. The author suggests that scholars and practitioners in different fields of study and domains of social and political life can make important contributions by defining, elucidating, and advocating why both perspectives, those of ethic of justice and care, together allow us to capture the prospects of a broader understanding and the practice of peace.

Does an App a Day Keep the Doctor Away? AI Symptom Checker Applications, Entrenched Bias, and Professional Responsibility.

The growing prominence of artificial intelligence (AI) in mobile health (mHealth) has given rise to a distinct subset of apps that provide users with diagnostic information using their inputted health status and symptom information-AI-powered symptom checker apps (AISympCheck). While these apps may potentially increase access to health care, they raise consequential ethical and legal questions. This paper will highlight notable concerns with AI usage in the health care system, further entrenchment of preexisting biases in the health care system and issues with professional accountability. To provide an in-depth analysis of the issues of bias and complications of professional obligations and liability, we focus on 2 mHealth apps as examples-Babylon and Ada. We selected these 2 apps as they were both widely distributed during the COVID-19 pandemic and make prominent claims about their use of AI for the purpose of assessing user symptoms. First, bias entrenchment often originates from the data used to train AI systems, causing the AI to replicate these inequalities through a "garbage in, garbage out" phenomenon. Users of these apps are also unlikely to be demographically representative of the larger population, leading to distorted results. Second, professional accountability poses a substantial challenge given the vast diversity and lack of regulation surrounding the reliability of AISympCheck apps. It is unclear whether these apps should be subject to safety reviews, who is responsible for app-mediated misdiagnosis, and whether these apps ought to be recommended by physicians. With the rapidly increasing number of apps, there remains little guidance available for health professionals. Professional bodies and advocacy organizations have a particularly important role to play in addressing these ethical and legal gaps. Implementing technical safeguards within these apps could mitigate bias, AIs could be trained with primarily neutral data, and apps could be subject to a system of regulation to allow users to make informed decisions. In our view, it is critical that these legal concerns are considered throughout the design and implementation of these potentially disruptive technologies. Entrenched bias and professional responsibility, while operating in different ways, are ultimately exacerbated by the unregulated nature of mHealth.

Regulatory Issues in Electronic Health Records for Adolescent HIV Research: Strategies and Lessons Learned.

BACKGROUND: Electronic health records (EHRs) are a cost-effective approach to provide the necessary foundations for clinical trial research. The ability to use EHRs in real-world clinical settings allows for pragmatic approaches to intervention studies with the emerging adult HIV population within these settings; however, the regulatory components related to the use of EHR data in multisite clinical trials poses unique challenges that researchers may find themselves unprepared to address, which may result in delays in study implementation and adversely impact study timelines, and risk noncompliance with established guidance. OBJECTIVE: As part of the larger Adolescent Trials Network (ATN) for HIV/AIDS Interventions Protocol 162b (ATN 162b) study that evaluated clinical-level outcomes of an intervention including HIV treatment and pre-exposure prophylaxis services to improve retention within the emerging adult HIV population, the objective of this study is to highlight the regulatory process and challenges in the implementation of a multisite pragmatic trial using EHRs to assist future researchers conducting similar studies in navigating the often time-consuming regulatory process and ensure compliance with adherence to study timelines and compliance with institutional and sponsor guidelines. METHODS: Eight sites were engaged in research activities, with 4 sites selected from participant recruitment venues as part of the ATN, who participated in the intervention and data extraction activities, and an additional 4 sites were engaged in data management and analysis. The ATN 162b protocol team worked with site personnel to establish the necessary regulatory infrastructure to collect EHR data to evaluate retention in care and viral suppression, as well as para-data on the intervention component to assess the feasibility and acceptability of the mobile health intervention. Methods to develop this infrastructure included site-specific training activities and the development of both institutional reliance and data use agreements. RESULTS: Due to variations in site-specific activities, and the associated regulatory implications, the study team used a phased approach with the data extraction sites as phase 1 and intervention sites as phase 2. This phased approach was intended to address the unique regulatory needs of all participating sites to ensure that all sites were properly onboarded and all regulatory components were in place. Across all sites, the regulatory process spanned 6 months for the 4 data extraction and intervention sites, and up to 10 months for the data management and analysis sites. CONCLUSIONS: The process for engaging in multisite clinical trial studies using EHR data is a multistep, collaborative effort that requires proper advanced planning from the proposal stage to adequately implement the necessary training and infrastructure. Planning, training, and understanding the various regulatory aspects, including the necessity of data use agreements, reliance agreements, external institutional review board review, and engagement with clinical sites, are foremost considerations to ensure successful implementation and adherence to pragmatic trial timelines and outcomes.

Physician Induced Demand: The Empirical Evidence of Angiography for Suspected Coronary Artery Disease.

Background: We aimed to investigate the existence of unnecessary demand for angiography and the factors affecting it to provide evidence for decision makers. Methods: This longitudinal panel study was conducted in public hospitals in Tehran, Iran by using 2458 patients' records that were undergoing angiography for suspected coronary artery disease 2013-2015. To modeling the physicians' behavior based on physician-induced demand (PID), the patients were classified as appropriate, uncertain, and inappropriate and then Hierarchical Linear Modeling (HLM) model besides the physician ethic index was developed and finally the existence of PID showed based on three scenarios. Results: Angiographies were performed inappropriately in 23.8% of 2458 patients as well 46.7% were uncertain, and 29.5% were appropriate. According to the HLM model, the physician-to-population ratio (δ0= -0.161) and the interaction variable coefficient are higher than zero and significant (δ1 = 253). The results of the physician ethic index showed that most physicians were at a moderate rate, meaning that their utility was a combination of both pecuniary and non-pecuniary profits (0 < | ɛpδ |<1). Considering the HLM model and the medical ethics index together has almost shown the condition of PID (the necessary condition δ1> 0 and the sufficient condition (1 ≤ |ɛpδ|)) existed for about 26% of all studied physicians who had pure profit maximizer. Conclusion: To reduce induced demand and improve medical ethics adherence in cardiologist, policy makers should develop native guidelines, rules, and instructions besides policies related to education, and increasing patients' awareness.

The Artificial Third: A Broad View of the Effects of Introducing Generative Artificial Intelligence on Psychotherapy.

Unlabelled: This paper explores a significant shift in the field of mental health in general and psychotherapy in particular following generative artificial intelligence's new capabilities in processing and generating humanlike language. Following Freud, this lingo-technological development is conceptualized as the "fourth narcissistic blow" that science inflicts on humanity. We argue that this narcissistic blow has a potentially dramatic influence on perceptions of human society, interrelationships, and the self. We should, accordingly, expect dramatic changes in perceptions of the therapeutic act following the emergence of what we term the artificial third in the field of psychotherapy. The introduction of an artificial third marks a critical juncture, prompting us to ask the following important core questions that address two basic elements of critical thinking, namely, transparency and autonomy: (1) What is this new artificial presence in therapy relationships? (2) How does it reshape our perception of ourselves and our interpersonal dynamics? and (3) What remains of the irreplaceable human elements at the core of therapy? Given the ethical implications that arise from these questions, this paper proposes that the artificial third can be a valuable asset when applied with insight and ethical consideration, enhancing but not replacing the human touch in therapy.

Servant leadership, brand love, and work ethic: important predictors of general health in workers in the education sector.

Background: Building a path aimed at the wellbeing of workers in the education sector is the fundamental basis to encourage quality education. To fill the gap in knowledge and address this aspect by understanding the behavior of the study population, it was proposed as with the objective of determining if servant leadership, brand love and work ethic predict the general health in workers. Methods: A non-probability sampling was applied for convenience. For this purpose, a sample of 509 workers from Peru was submitted to study, who completed a questionnaire consisting of: scale of servant leadership, work ethic, GHQ-12 and brand love. By applying a quantitative method using a structural equation modeling partial least squares approach. Results: The present study demonstrated that the three constructs (servant leadership, brand love, and work ethic) predict the general health of workers in a positive and significant way, in a sample of Peruvian workers in the education sector. Furthermore, the results suggest that these factors can be used to improve the health of employees in educational institutions in Peru and possibly in other contexts as well. Conclusion: Given these results and after knowing the solidity of the predictions, the importance of promoting general health in workers in the education sector.

Alternative consent methods used in the multinational, pragmatic, randomised clinical trial SafeBoosC-III.

BACKGROUND: The process of obtaining prior informed consent for experimental treatment does not fit well into the clinical reality of acute and intensive care. The therapeutic window of interventions is often short, which may reduce the validity of the consent and the rate of enrolled participants, to delay trial completion and reduce the external validity of the results. Deferred consent and 'opt-out' are alternative consent methods. The SafeBoosC-III trial was a randomised clinical trial investigating the benefits and harms of cerebral oximetry monitoring in extremely preterm infants during the first 3 days after birth, starting within the first 6 h after birth. Prior, deferred and opt-out consent were all allowed by protocol. This study aimed to evaluate the use of different consent methods in the SafeBoosC-III trial, Furthermore, we aimed to describe and analyse concerns or complaints that arose during the first 6 months of trial conduct. METHODS: All 70 principal investigators were invited to join this descriptive ancillary study. Each principal investigator received a questionnaire on the use of consent methods in their centre during the SafeBoosC-III trial, including the possibility to describe any concerns related to the consent methods used during the first 6 months of the trial, as raised by the parents or the clinical staff. RESULTS: Data from 61 centres were available. In 43 centres, only prior informed consent was used: in seven, only deferred consent. No centres used the opt-out method only, but five centres used prior and deferred, five used prior, deferred and opt-out (all possibilities) and one used both deferred and opt-out. Six centres applied to use the opt-out method by their local research ethics committee but were denied using it. One centre applied to use deferred consent but was denied. There were only 23 registered concerns during the execution of the trial. CONCLUSIONS: Consent by opt-out was allowed by the protocol in this multinational trial but only a few investigators opted for it and some research ethics boards did not accept its use. It is likely to need promotion by the clinical research community to unfold its potential.

Mapping Ethical Artificial Intelligence Policy Landscape: A Mixed Method Analysis.

As more national governments adopt policies addressing the ethical implications of artificial intelligence, a comparative analysis of policy documents on these topics can provide valuable insights into emerging concerns and areas of shared importance. This study critically examines 57 policy documents pertaining to ethical AI originating from 24 distinct countries, employing a combination of computational text mining methods and qualitative content analysis. The primary objective is to methodically identify common themes throughout these policy documents and perform a comparative analysis of the ways in which various governments give priority to crucial matters. A total of nineteen topics were initially retrieved. Through an iterative coding process, six overarching themes were identified: principles, the protection of personal data, governmental roles and responsibilities, procedural guidelines, governance and monitoring mechanisms, and epistemological considerations. Furthermore, the research revealed 31 ethical dilemmas pertaining to AI that had been overlooked previously but are now emerging. These dilemmas have been referred to in different extents throughout the policy documents. This research makes a scholarly contribution to the expanding field of technology policy formulations at the national level by analyzing similarities and differences among countries. Furthermore, this analysis has practical ramifications for policymakers who are attempting to comprehend prevailing trends and potentially neglected domains that demand focus in the ever-evolving field of artificial intelligence.

Artificial Intelligence in Plastic Surgery: Analysis of Applications, Perspectives, and Psychological Impact.

Artificial intelligence (AI) is emerging as a promising tool in the field of plastic surgery, offering a wide array of applications that enhance surgical outcomes, patient satisfaction, and overall efficiency. This paper explores the utilization of AI, highlighting its various advantages and potential drawbacks. AI-driven technologies such as computer vision, machine learning algorithms, and robotic assistance facilitate preoperative planning, intraoperative guidance, and postoperative monitoring. These advancements enable precise anatomical measurements, personalized treatment plans, and real-time feedback during surgery, leading to improved accuracy and safety. Furthermore, AI-powered image analysis aids in facial recognition, skin texture assessment, and simulation of surgical outcomes, enabling enhanced patient consultations and predictive modeling. However, the integration of AI in plastic surgery also presents challenges, including ethical concerns, data privacy, algorithm biases, and the need for comprehensive training among healthcare professionals. Additionally, the reliance on AI systems may potentially lead to over-reliance or reduced surgeon autonomy, necessitating careful validation and continuous refinement of these technologies. Despite these challenges, the synergistic collaboration between AI and plastic surgery holds great promise in advancing clinical practice, fostering innovation, and ultimately benefiting patients through optimized esthetic and reconstructive outcomes.Level of Evidence V This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors https://www.springer.com/00266 .

Human-centered AI through employee participation.

This article examines the role of employee participation in AI implementation, focusing on a case study from the German telecommunications sector. Theoretical discussions highlight concepts of employee participation and workplace democracy, emphasizing the normative basis for human-centered AI in Europe. The empirical analysis of the case study demonstrates social practices of human-centered AI and the importance of employee representatives and labor policies in sustainable technology. The contribution is structured into two main parts: first, discussing sociological concepts of employee participation and summarizing the role of works councils in shaping digital technology implementation. Second, focusing on a case study of AI regulations at Deutsche Telekom, highlighting the significant effects of employee participation and co-determination by the group works council in promoting socially sustainable AI implementation which is done via qualitative case analysis. The article highlights the significance of participation and negotiations and gives an example for social partnership relations in AI implementations.