Fairness Considerations Research Articles

Machine learning-based infection diagnostic and prognostic models in post-acute care settings: a systematic review.

This study aims to (1) review machine learning (ML)-based models for early infection diagnostic and prognosis prediction in post-acute care (PAC) settings, (2) identify key risk predictors influencing infection-related outcomes, and (3) examine the quality and limitations of these models. PubMed, Web of Science, Scopus, IEEE Xplore, CINAHL, and ACM digital library were searched in February 2024. Eligible studies leveraged PAC data to develop and evaluate ML models for infection-related risks. Data extraction followed the CHARMS checklist. Quality appraisal followed the PROBAST tool. Data synthesis was guided by the socio-ecological conceptual framework. Thirteen studies were included, mainly focusing on respiratory infections and nursing homes. Most used regression models with structured electronic health record data. Since 2020, there has been a shift toward advanced ML algorithms and multimodal data, biosensors, and clinical notes being significant sources of unstructured data. Despite these advances, there is insufficient evidence to support performance improvements over traditional models. Individual-level risk predictors, like impaired cognition, declined function, and tachycardia, were commonly used, while contextual-level predictors were barely utilized, consequently limiting model fairness. Major sources of bias included lack of external validation, inadequate model calibration, and insufficient consideration of data complexity. Despite the growth of advanced modeling approaches in infection-related models in PAC settings, evidence supporting their superiority remains limited. Future research should leverage a socio-ecological lens for predictor selection and model construction, exploring optimal data modalities and ML model usage in PAC, while ensuring rigorous methodologies and fairness considerations.

Testing and Evaluation of Health Care Applications of Large Language Models

Large language models (LLMs) can assist in various health care activities, but current evaluation approaches may not adequately identify the most useful application areas. To summarize existing evaluations of LLMs in health care in terms of 5 components: (1) evaluation data type, (2) health care task, (3) natural language processing (NLP) and natural language understanding (NLU) tasks, (4) dimension of evaluation, and (5) medical specialty. A systematic search of PubMed and Web of Science was performed for studies published between January 1, 2022, and February 19, 2024. Studies evaluating 1 or more LLMs in health care. Three independent reviewers categorized studies via keyword searches based on the data used, the health care tasks, the NLP and NLU tasks, the dimensions of evaluation, and the medical specialty. Of 519 studies reviewed, published between January 1, 2022, and February 19, 2024, only 5% used real patient care data for LLM evaluation. The most common health care tasks were assessing medical knowledge such as answering medical licensing examination questions (44.5%) and making diagnoses (19.5%). Administrative tasks such as assigning billing codes (0.2%) and writing prescriptions (0.2%) were less studied. For NLP and NLU tasks, most studies focused on question answering (84.2%), while tasks such as summarization (8.9%) and conversational dialogue (3.3%) were infrequent. Almost all studies (95.4%) used accuracy as the primary dimension of evaluation; fairness, bias, and toxicity (15.8%), deployment considerations (4.6%), and calibration and uncertainty (1.2%) were infrequently measured. Finally, in terms of medical specialty area, most studies were in generic health care applications (25.6%), internal medicine (16.4%), surgery (11.4%), and ophthalmology (6.9%), with nuclear medicine (0.6%), physical medicine (0.4%), and medical genetics (0.2%) being the least represented. Existing evaluations of LLMs mostly focus on accuracy of question answering for medical examinations, without consideration of real patient care data. Dimensions such as fairness, bias, and toxicity and deployment considerations received limited attention. Future evaluations should adopt standardized applications and metrics, use clinical data, and broaden focus to include a wider range of tasks and specialties.

Interest-aware joint caching, computing, and communication optimization for mobile VR delivery in MEC networks

In the upcoming B5G/6G era, Virtual Reality (VR) over wireless has become a typical application, which is an inevitable trend in the development of video. However, in immersive and interactive VR experiences, VR services typically exhibit high delay, while simultaneously posing challenges for the energy consumption of local devices. To address these issues, this paper aims to improve the performance of VR service in the edge-terminal cooperative system. Specifically, we formulate a joint Caching, Computing, and Communication (3C) VR service policy problem by optimizing the weighted sum of the total VR delivery delay and the energy consumption of local devices. To design the optimal VR service policy, the optimization problem is decoupled into three independent subproblems to be solved separately. To improve the caching efficiency within the network, a Bert-based user interest analysis method is first proposed to accurately characterize the content request behavior. Based on this, a service cost minimum-maximization problem is formulated under the consideration of performance fairness among users. Then, the joint caching and computing scheme is derived for each user with a given allocation of communication resources while a bisection-based communication scheme is acquired with the given information on the joint caching and computing policy. With alternative optimization, an optimal policy for joint 3C based on user interest can be finally obtained. Simulation results are presented to demonstrate the superiority of the proposed user interest-aware caching scheme and the effectiveness of the joint 3C optimization policy while considering user fairness. Our code is available at https://github.com/mrfuqaq1108/Interest-Aware-Joint-3C-Optimization.

Artificial intelligence in applied family research involving families with young children: A scoping review

AbstractObjectiveThis scoping review systematically examined the applied family science literature involving families raising young children to understand how relevant studies have applied artificial intelligence (AI)‐facilitated technologies.BackgroundFamily research is exploring the application of AI. However, there is a critical need for a review study that systematically examines the varied use of AI in applied family science to inform family practitioners and policymakers.MethodComprehensive literature searches were conducted in nine databases. Of the 10,022 studies identified, 21 met inclusion criteria: peer‐reviewed journal article; published between 2014–2024; written in English; involved the use of AI in collecting data, analyzing data, or providing family‐centered services; included families raising young children 0–5 years; and was quantitative in analysis.ResultsMost studies focused on maternal and child health outcomes in low‐ and middle‐income countries. All studies identified were in the AI use domain of data analysis, with 76% of the studies having a focus on identifying the most important predictors. Random forest performed as the best machine learning model. Only one study directly mentioned the ethical use of AI.ConclusionOverall, the applied family science evidence base that employs AI is limited in size and scope, with most studies using AI for data analysis purposes with limited ethical considerations.ImplicationsAI models in applied family science can inform family services and policies aimed at promoting family and child health. However, thoughtful consideration of AI ethics and fairness is needed to prevent the negative social impacts of AI on marginalized groups of families and their young children.

Arab Countries’ Interest in Integration in Eurasia: Tactics or Strategy?

In the situation of transition from a unipolar to a multipolar world, the tasks of developing a dialogue with the Arab world are set out in the Foreign Policy Concept of the Russian Federation for 2023. In contrast to the “senior-junior” concept developed by the West, in which the allegedly “natural” leading role is unfairly assigned to Western powers and which causes growing resistance from the states of the Global South, Russia proposes that its Arab partners form relations based on a fair consideration of each other’s interests. The Arab ruling circles, experts, and the “Arab street” increasingly perceive Russia as a statecivilization that is not only located in Eurasia, but is also a driving force for the integration of the Non-West. Of particular interest is the Shanghai Cooperation Organization as a platform for generating ideas and coordinating between the emerging centers of power represented by India, China, and Russia, especially after Iran, a regional rival of Saudi Arabia, became a member of the SCO in 2023. Is the Arab interest in the Eurasian NonWest strategic or tactical, being in the second case aimed solely at exerting pressure on the West to obtain maximum concessions? This is a difficult, but key question that requires an answer from experts, which explains the relevance of this article. In his study, the author uses situational analysis tools, including content analysis methods, force field analysis and data visualization. The author concludes that the interest of Arab countries in integration in Eurasia, including cooperation with Russia, is predominantly strategic.

The Impact of the Opportunity Zone Program on Residential Real Estate

Problem definition: Opportunity zones (OZs) are designated census tracts in which real estate investments can gain tax benefits. Introduced by the U.S. Tax Cuts and Jobs Act of 2017, the goal of the OZ program is to foster economic development in distressed neighborhoods. In this paper, we investigate and optimize the OZ selection process and examine the impact of OZs by exploiting two data sets: a proprietary real estate data set that includes 36.1 million residential transactions spanning all 50 U.S. states and census-tract demographics data between 2010 and 2019. Methodology/results: We show that census tracts with higher poverty and unemployment rates were more likely to be selected. Counterintuitively, however, tracts with a higher average real estate price were also more likely to be selected. We then apply difference-in-differences, synthetic control, and matching techniques to rigorously assess the impact of the OZ program on two key real estate metrics: price and transaction volume. We find that the OZ program increased real estate prices by 4.03%–6.13% but do not observe a significant effect on the transaction volume. We also find that investors primarily targeted the high-end real estate market, namely, exhibiting a cherry-picking behavior. To better fulfill its intended societal and economic goals, we propose an optimization framework with fairness considerations for OZ assignment decisions. We show that the OZs assigned from our fairness-aware optimization formulation can better serve distressed communities and mitigate investors’ cherry-picking behavior. Managerial implications: Our paper underscores the importance of incorporating fairness in OZ designation to achieve a desirable real estate market reaction. Our large-scale empirical analysis provides a comprehensive assessment of the current government OZ assignment, and our fairness-aware optimization framework provides concrete recommendations for policy makers. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2024.0746 .

Research on Optimizing Low-Saturation Intersection Signals with Consideration for Both Efficiency and Fairness

In response to the fairness issue arising from the unequal delay of vehicles in different phases at intersections and considering the actual situation of small and variable delays for vehicles in low-saturation intersection phases, this paper proposes the concept of “sacrificing efficiency for fairness”. Firstly, the universality of unfair delay phenomena at intersection phases is explained, especially at low-saturation intersections where the fluctuation in phase delays is 1.87 times higher than at other intersections. Then, a fairness evaluation index is constructed using information entropy, and the feasibility of the proposed approach is demonstrated. Subsequently, a signal optimization model that balances efficiency and fairness is proposed. Finally, the proposed model is validated through case studies, showing that it not only simultaneously considers efficiency and fairness but also has minimal impact on efficiency. Moreover, the changes to timing schemes in the efficiency model are much smaller compared to the model that only considers fairness. Sensitivity analysis reveals that the model performs better under low-saturation intersection conditions.

「부론강」에 나타난 장소성 구축과 로컬 정체성 연구

Labor writer Lee In-hwi’s ‘Buron River’ clearly reveals ‘Buron-ness’ through the journey of the space called ‘Buron’ becoming a place from a minority anti-capitalist, other-oriented perspective. In the Anthropocene, where locality is highlighted and reflection on human-centered and human exceptional thinking is required, this novel moves away from people-centered and focuses on communication and response with nature such as trees, rivers, and land, and the meaning of historical figures and relics/remains between humans and humans. It presents an inhumane turn while providing equal and fair consideration and placement. Buron-myeon, Wonju-si, where the writer from Seoul happened to settle, is a place where self-care and wound healing are realized, and it contains topographical and historical significance with the characteristics of motherhood and inclusiveness of the Namhan River, Seom River, Chiaksan Mountain, Baekunsan Mountain, and Mireuksan Mountain. ‘Dream Seekers’, a bar and active community created by people from outside, is a place where self-sufficiency through communal farming, samulnori, caring for others, and local culture take place.Those who want to reflect on the use of land, which was a means of human profit, and escape from capitalism, overcome their wounds and open their hearts in Buron. The setting of a community made up of outsiders and strangers rather than natives or locals allows us to escape from the limitations of regional bias and a sense of home that is overly romanticized and portrayed only as beautiful and positive. In the Buron area, love of place and love of life are expressed through physical harmony with nature, such as thousand-year-old zelkova trees, a river that embraces and allows infinitely, and an abandoned temple site with solitude and mystery. In the literary representation of the Buron region, humans, Nature and history are arranged fairly and balancedly. “Buron River,” in which space is specifically transformed into a place, is evaluated as a very unique and original regional ecology (life) novel that highlights the identity of Buron, Wonju City, clearly and clearly enough to become a model.

The interplay between the proposer's role model and moral behavior modulates proposal processing in the Ultimatum Game: An ERP study

Economic decision-making plays a paramount role in both individual and national interests. Individuals have fairness preferences in economic decision-making, but a proposer's moral-related information may affect fairness considerations. In prior ERP studies, researchers have suggested moral identity influences fairness preferences in the Ultimatum Game (UG), but there are discrepancies in the results. Furthermore, whether role models (individuals whom someone else looks to help decide suitable behaviors), who can modulate people's moral standards, can affect fairness concerns in UG is still understudied. To address the questions, we selected the moral-related statements by eliminating those with illegal information and employed the ERP technique to explore whether the interplay of the proposer's role model and moral-related behavior influenced fairness processing in the modified UG and the corresponding neural mechanisms. We mainly found that the aforementioned interaction effect on proposal considerations in UG could be mirrored in both rejection rates and P300 variations. The results demonstrate that the interaction between the proposer's role model and moral behavior can modulate fairness concerns in UG. Our current work provides new avenues for elucidating the time course of the influencing mechanism of fair distributions in complicated social environments.

Route Planning for Intra-Hospital Patient Transportation Using Metaheuristics and Mixed Integer Linear Programming.

Healthcare processes are complex and involve uncertainties to influence the service quality and health of patients. Patient transportation takes place between the hospitals or between the departments within the hospital (i.e., Inter- or Intra-Hospital Transportation respectively). The focus of our paper is route planning for transporting patients within the hospital. The route planning task is complex due to multiple factors such as regulations, fairness considerations (i.e., balanced workload amongst transporters), and other dynamic factors (i.e., transport delays, wait times). Transporters perform the physical transportation of patients within the hospital. In principle, each job allocation respects the transition time between the subsequent jobs. The primary objective was to determine the feasible number of transporters, and then generate the route plan for all determined transporters by distributing all transport jobs (i.e., from retrospective data) within each shift. Secondary objectives are to minimize the sum of total travel time and sum of total idle time of all transporters and minimize the deviations in total travel time amongst transporters. Our method used multi-staged Local Search Metaheuristics to attain the primary objective. Metaheuristics incorporate Mixed Integer Linear Programming to allocate fairly the transport jobs by formulating optimization constraints with bounds for satisfying the secondary objectives. The obtained results using formulated optimization constraints represent better efficacy in multi-objective route planning of Intra-Hospital Transportation of patients.

Student Performance Analysis: A Systematic Research

In the rapidly evolving landscape of education, understanding and improving student performance is of paramount importance. This research paper explores the application of Machine Learning (ML) technologies within the realm of Big Data Analytics to comprehensively analyze and enhance student performance. Leveraging the vast amount of data generated within educational institutions, this study demonstrates how ML algorithms and techniques can be harnessed to gain insights into student learning patterns, predict academic outcomes, and develop data-driven strategies for educational improvement. The paper begins by highlighting the significance of student performance analysis in modern education and the challenges faced by institutions in managing and interpreting the growing volume of educational data. It then presents a comprehensive review of the state-of-the- art ML algorithms and data processing techniques relevant to student performance analysis. Furthermore, the research outlines a novel framework for student performance analysis, integrating various ML models such as regression, classification, and clustering, alongside advanced data preprocessing techniques. The proposed framework is designed to handle diverse educational datasets, including academic records, attendance records, socio-demographic information, and learning resources utilization[1] . Through a series of experiments and case studies, this paper demonstrates the practical application of ML in predicting student performance accurately, identifying at-risk students, and personalizing educational interventions. It also delves into the ethical considerations and data privacy concerns associated with the use of student data in ML-based educational analytics. The results and insights from this research offer valuable implications for educational institutions, policymakers, and researchers alike. By harnessing the power of Big Data and ML technologies, institutions can make data-driven decisions to enhance teaching methodologies, improve student support systems, and ultimately elevate student success rates. Additionally, this study contributes to the ongoing discourse on the responsible use of data in education, emphasizing the importance of transparency, fairness, and ethical considerations. In conclusion, this research paper presents a robust framework that showcases the potential of Machine Learning technologies within the realm of Big Data Analytics for student performance analysis. It offers a roadmap for educational institutions to harness the power of data to foster better learning outcomes and contributes to the ongoing dialogue on responsible data usage in education.

Fairness, Bias, and Ethics in AI: Exploring the Factors Affecting Student Performance

The use of artificial intelligence (AI) as a data science tool for education has enormous potential for increasing student performance and course outcomes. However, the growing concern about fairness, bias, and ethics in AI systems requires a careful examination of these issues in an educational context. Using AI and predictive modelling tools, this paper explores the aspects influencing student performance and course success. The Open University Learning Analytics Dataset (OULAD) is analysed using several AI techniques (logistic regression and random forest) in this study to reveal insights about fairness, ethics, and potential biases. This dataset has been used by hundreds of studies to explore how educational data mining can provide information on students. However, potential bias or unfairness in that dataset could undermine the results and any conclusions made from them. To gain insights into the dataset's properties, this was analysed using a typical data science methodology, which included data collecting, cleaning, and exploratory data analysis using Python. By applying AI-based predictive models, this study aims to detect potential biases and their impact on student outcomes. Fairness and ethical considerations are central to the analysis as the representation of various demographic groups and any disparities are evaluated in course results. The goal is to provide useful insights on the proper use of AI in education, while also maintaining equitable and transparent decision-making procedures. The findings shed light on the complicated interplay between artificial intelligence, fairness, and ethics in the context of student performance and course success. As artificial intelligence continues to influence the educational landscape, this study will provide useful ideas for encouraging fairness and minimising biases, resulting in a more inclusive and equal learning environment.

Case Article—Three Mountain Communications: Fairness Considerations for Workplace Task Allocation

The case study has two primary objectives: incorporating fairness considerations in managerial decision making and introducing task allocation using an optimization model. The case focuses on a call center that faces the issue of daily task allocation. The current self-selection-based allocation protocol has created friction among employees. To enable employee satisfaction and boost team morale, the call center needs to develop an allocation model that is perceived to be fair by employees. The case tends to elicit enthusiastic participation from students, especially on the theme of workplace fairness and the role of gender and individual constraints on employees’ ability to excel at the workplace. Overall, students believed the case was challenging and that it provided them skills to combine (i) optimization modeling and (ii) qualitative considerations for workplace fairness that they studied in management and leadership courses. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ited.2023.0044ca .

Fairness in Large Language Models: A Taxonomic Survey

Large Language Models (LLMs) have demonstrated remarkable success across various domains. However, despite their promising performance in numerous real-world applications, most of these algorithms lack fairness considerations. Consequently, they may lead to discriminatory outcomes against certain communities, particularly marginalized populations, prompting extensive study in fair LLMs. On the other hand, fairness in LLMs, in contrast to fairness in traditional machine learning, entails exclusive backgrounds, taxonomies, and fulfillment techniques. To this end, this survey presents a comprehensive overview of recent advances in the existing literature concerning fair LLMs. Specifically, a brief introduction to LLMs is provided, followed by an analysis of factors contributing to bias in LLMs. Additionally, the concept of fairness in LLMs is discussed categorically, summarizing metrics for evaluating bias in LLMs and existing algorithms for promoting fairness. Furthermore, resources for evaluating bias in LLMs, including toolkits and datasets, are summarized. Finally, existing research challenges and open questions are discussed.

Optimizing fairness and accuracy: a Pareto optimal approach for decision-making

AbstractIn the era of data-driven decision-making, ensuring fairness and equality in machine learning models has become increasingly crucial. Multiple fairness definitions have been brought forward to evaluate and mitigate unintended fairness-related harms in real-world applications, with little research on addressing their interactions with each other. This paper explores the application of a Minimax Pareto-optimized solution to optimize individual and group fairness at individual and group levels on the Adult Census Income dataset as well as on the German Credit dataset. The objective of training a classification model with a multi-objective loss function is to achieve fair outcomes without compromising utility objectives. We investigate the interplay of different fairness definitions, including definitions of performance consistency and traditional group and individual fairness measures, amongst each other coupled with performance. The results presented in this paper highlight the feasibility of incorporating several fairness considerations into machine learning models, which can be applied to use cases with multiple sensitive features and attributes that characterize real-world applications. This research is a valuable step toward building responsible and transparent machine learning systems that can be incorporated into critical decision-making processes.

Evaluating the near- and long-term role of carbon dioxide removal in meeting global climate objectives

The 6th Assessment Report from the Intergovernmental Panel on Climate Change lacked sufficient land-sector scenario information to estimate total carbon dioxide removal deployment. Here, using a dataset of land-based carbon dioxide removal based on the scenarios assessed by the Intergovernmental Panel on Climate Change, we show that removals via afforestation and reforestation play a critical near-term role in mitigation, accounting for around 10% (median) of the net greenhouse gas emission reductions between 2020 and 2030 in scenarios that limit warming to 1.5 °C with limited overshoot. Novel carbon dioxide removal technologies such as direct air carbon capture and storage scale to multi-gigatonne levels by 2050 and beyond to balance residual emissions and draw down warming. We show that reducing fossil fuel and deforestation emissions (gross emissions) accounts for over 80% of net greenhouse gas reductions until global net zero carbon dioxide (CO2) independent of climate objective stringency. We explore the regional distributions of gross emissions and total carbon dioxide removal in cost-effective mitigation pathways and highlight the importance of incorporating fairness and broader sustainability considerations in future assessments of mitigation pathways with carbon dioxide removal.

Juridical Analysis of Legal Protection On Charges Due To Discretionary Authority For Regional Officials

This article reviews the roles and responsibilities of local officials in the use of discretionary powers and the legal repercussions that may arise from their decisions. Through a juridical approach, it investigates the legal protections available to local officials when they face indictments or lawsuits as a result of their discretionary actions. Taking into account the existing legal framework, this article analyzes various factors that may influence whether local officials will be held legally responsible for their discretionary decisions, including ethical, public policy and fairness considerations. In addition, it explores the legal safeguards that local officials can take, including enforceability of laws, liability insurance, and dispute resolution mechanisms. As such, this article aims to provide a better understanding of the complexities of legal protection for local officials in the context of the use of discretionary authority. The results showed that legal protection for regional officials related to the use of discretionary authority can be seen from various aspects, including the principle of legality, the principle of legal certainty and accountability. The juridical analysis of legal protection against charges due to discretionary authority for regional officials shows the importance of a deep understanding of the limits and responsibilities attached to the use of such authority. By paying attention to aspects of legality, legal certainty, and accountability, it is hoped that legal protection for regional officials can be guaranteed in the context of the use of discretionary authority.

From Intel & Qualcomm to the new Art. 102 TFEU-Guidelines – Streamlining the Form- & Effects Based Approach

Abstract Rapid digital innovation has promoted a broad range of welfare aspects and created a myriad of new business models. While these benefits are uncontentious, the special characteristics of digital markets, such as strong network effects, have led to a substantial concentration of power among a few corporate actors. The increasing complexity of these market dynamics has rendered antitrust enforcement more challenging, particularly with regard to assessing the abuse of market dominance (Art. 102 TFEU). Particularly the European Commission has been confronted with criticism relating to its previously propagated “more economic approach”, which has led to a lack of timely and effective antitrust enforcement. While there is a consensus that a new approach to ascertaining the abuse of dominance is pertinent – which the Commission endorses – the underlying assessment parameters are still contentious. This article argues for a standard of dominance to notably reflect the digital transformation and include a comprehensive framework. While the predominant intervention aim must be based on sufficient innovation, a notion of economic fairness and sustainability considerations gain momentum in antitrust enforcement.

Public and fair consideration of a case by an impartial and independent court in criminal proceedings: European standards and Ukrainian realities

Public and fair consideration of a case by an impartial and independent court in criminal proceedings: European standards and Ukrainian realities

The role of ETSI in the EU’s regulation and governance of artificial intelligence

This paper explores the significant role that standardisation plays in the regulation and governance of artificial intelligence (AI) within the European Union (EU). As AI technologies rapidly advance, they bring about important societal implications involving privacy, fairness, transparency and other relevant ethical considerations. As a result, legislators and policymakers around the world are joined by a common drive to provide legislative solutions and regulatory frameworks that guarantee that the ongoing integration of AI systems into society is consistent with fundamental rights and democratic values. This paper critically examines the EU Regulation on AI (AI Act), which delegates the definition of essential requirements for high-risks AI systems to harmonised standards, underlying the significance of standardisation in ensuring technical feasibility and compliance with EU laws and values. At the forefront of this discussion there is the increasing influence of AI-related standardisation across social, economic, and geopolitical domains, with a particular focus on the crucial role played by Standard Developing Organisations (SDOs) in the regulatory and governance processes. This paper contributes to the legal scholarship by critically analysing the regulatory approach chosen for the EU’s AI Act, contesting the adequacy of the New Legislative Framework (NLF) for AI governance, and arguing that the reliance on harmonised standards risks undermining democratic accountability and fails to sufficiently safeguard fundamental rights without a more inclusive and transparent standard-setting process. The article focuses on the exclusion of the European Telecommunications Standards Institute (ETSI) from the European Commissions standardisation request in support of the AI Act, and asseses its potential impact on EU law-making and regulatory consistency. Ultimately, the analysis aims to contribute to the understanding of standardisation dynamics, offering insights into its profound implications for AI governance and the broader digital sphere.