The article examines the impact of the virtual public sphere on how urban spaces are experienced and conceived in our data-driven society. It places particular emphasis on urban scale digital twins, which are virtual replicas of cities that are used to simulate environments and develop scenarios in response to policy problems. The article also investigates the shift from the technical to the socio-technical perspective within the field of smart cities. Despite the aspirations of urban scale digital twins to (...) enhance the participation of citizens in the decision-making processes relayed to urban planning strategies, the fact that they are based on a limited set of variables and processes makes them problematic. The article aims to shed light on the tension between the real and the ideal at stake during this process of abstracting sets of variables and processes in the case of urban scale digital twins. (shrink)

As the world grapples with the urgent need for sustainable development, the transition towards green energy stands as a critical imperative. Financing this transition poses significant challenges, requiring innovative approaches that align financial objectives with environmental sustainability goals. This review presents a conceptual framework for leveraging data-driven techniques in sustainable finance to facilitate the transition towards green energy. The proposed framework integrates principles of sustainable finance with advanced data analytics to enhance decision-making processes across the (...) financial ecosystem. At its core, the framework emphasizes the importance of harnessing vast datasets related to energy production, consumption, environmental impact, and financial performance. By leveraging machine learning algorithms and predictive modeling techniques, financial stakeholders can gain deeper insights into the risks and opportunities associated with green energy investments. Key components of the framework include data collection and aggregation, risk assessment, impact measurement, and investment optimization. Data sources range from traditional financial indicators to environmental metrics, social impact assessments, and geopolitical factors. Through comprehensive data analysis, financial institutions can assess the long-term viability and sustainability of green energy projects, while also evaluating potential social and environmental impacts. Risk assessment methodologies within the framework consider both financial risks, such as market volatility and regulatory uncertainty, and non-financial risks, such as climate change impacts and community resilience. By integrating these factors into risk models, investors can make more informed decisions that mitigate potential losses and maximize returns. Furthermore, impact measurement tools enable stakeholders to quantify the environmental and social benefits of green energy investments. By tracking metrics such as carbon emissions reduction, energy efficiency improvements, and job creation, investors can assess the contribution of their portfolios towards broader sustainability objectives. Finally, the framework incorporates investment optimization strategies that align financial goals with environmental objectives. Through portfolio diversification, asset allocation, and innovative financial instruments such as green bonds and impact investing funds, financial institutions can allocate capital more efficiently towards green energy projects. The conceptual framework presented herein offers a systematic approach to integrating data-driven methodologies into sustainable finance practices. By leveraging advanced analytics and comprehensive datasets, financial stakeholders can drive the transition towards green energy while simultaneously achieving financial returns and positive environmental outcomes. (shrink)

This review delves into the influence of big data analytics on project management effectiveness and project success rates. By examining applications, accomplishments, hindrances, and emerging developments in the context of big data analytics and project management, this review provides insights into its transformative potential. Results indicate that big data analytics fosters improved project performance, more robust risk management, and heightened adaptability. However, challenges related to data quality, privacy, and project manager training remain to be addressed. This (...) review underscores the value of data-driven decision-making for both practitioners and researchers in the project management field. (shrink)

Since approval of the EU General Data Protection Regulation (GDPR) in 2016, it has been widely and repeatedly claimed that the GDPR will legally mandate a ‘right to explanation’ of all decisions made by automated or artificially intelligent algorithmic systems. This right to explanation is viewed as an ideal mechanism to enhance the accountability and transparency of automated decision-making. However, there are several reasons to doubt both the legal existence and the feasibility of such a right. In (...) contrast to the right to explanation of specific automated decisions claimed elsewhere, the GDPR only mandates that data subjects receive meaningful, but properly limited, information (Articles 13-15) about the logic involved, as well as the significance and the envisaged consequences of automated decision-making systems, what we term a ‘right to be informed’. Further, the ambiguity and limited scope of the ‘right not to be subject to automated decision-making’ contained in Article 22 (from which the alleged ‘right to explanation’ stems) raises questions over the protection actually afforded to data subjects. These problems show that the GDPR lacks precise language as well as explicit and well-defined rights and safeguards against automated decision-making, and therefore runs the risk of being toothless. We propose a number of legislative and policy steps that, if taken, may improve the transparency and accountability of automated decision-making when the GDPR comes into force in 2018. (shrink)

Across two studies the hypotheses were tested that stressful situations affect both leadership ethical acting and leaders' recognition of ethical dilemmas. In the studies, decision makers recruited from 3 sites of a Swedish multinational civil engineering company provided personal data on stressful situations, made ethical decisions, and answered to stress-outcome questions. Stressful situations were observed to have a greater impact on ethical acting than on the recognition of ethical dilemmas. This was particularly true for situations involving punishment and (...) lack of rewards. The results are important for the Corporate Social Responsibility (CSR) of an organization, especially with regard to the analysis of the Stressors influencing managerial work and its implications for ethical behavior. (shrink)

The stakes associated with an algorithmic decision are often said to play a role in determining whether the decision engenders a right to an explanation. More specifically, “high stakes” decisions are often said to engender such a right to explanation whereas “low stakes” or “non-high” stakes decisions do not. While the overall gist of these ideas is clear enough, the details are lacking. In this paper, we aim to provide these details through a detailed investigation of what we (...) will call the “Simple Stakes Thesis”. The Simple Stakes Thesis, as it will turn out, is too simple. For even if the stakes associated with a specific one-off decision are low—and hence does not engender a right to an explanation—such decisions may nevertheless form part of a high stakes pattern or aggregate of decisions. In such cases, we argue, even a low stakes decision may engender a right to explanation. Not only does this show that the right to explanation is more demanding than so far recognized. It also shows that the stakes thesis is significantly harder to apply in practice. (shrink)

In recent years there has been an explosion of interest in Artificial Intelligence (AI) both in health care and academic philosophy. This has been due mainly to the rise of effective machine learning and deep learning algorithms, together with increases in data collection and processing power, which have made rapid progress in many areas. However, use of this technology has brought with it philosophical issues and practical problems, in particular, epistemic and ethical. In this paper the authors, with backgrounds (...) in philosophy, maternity care practice and clinical research, draw upon and extend a recent framework for shared decision-making (SDM) that identified a duty of care to the client's knowledge as a necessary condition for SDM. This duty entails the responsibility to acknowledge and overcome epistemic defeaters. This framework is applied to the use of AI in maternity care, in particular, the use of machine learning and deep learning technology to attempt to enhance electronic fetal monitoring (EFM). In doing so, various sub-kinds of epistemic defeater, namely, transparent, opaque, underdetermined, and inherited defeaters are taxonomized and discussed. The authors argue that, although effective current or future AI-enhanced EFM may impose an epistemic obligation on the part of clinicians to rely on such systems' predictions or diagnoses as input to SDM, such obligations may be overridden by inherited defeaters, caused by a form of algorithmic bias. The existence of inherited defeaters implies that the duty of care to the client's knowledge extends to any situation in which a clinician (or anyone else) is involved in producing training data for a system that will be used in SDM. Any future AI must be capable of assessing women individually, taking into account a wide range of factors including women's preferences, to provide a holistic range of evidence for clinical decision-making. (shrink)

The study aimed to identify the reality of decision-making in the local NGOs in Gaza Strip. In order to achieve the objectives of the study and to test its hypotheses, the analytical descriptive method was used, relying on the questionnaire as a main tool for data collection. The study society was one of the decision makers in the local NGOs in Gaza Strip. The study population reached 78 local NGOs in Gaza Strip. A Census Method of (...) the possible study community was adopted, and the use of the Statistical Package for Social Sciences (SPSS) was mainly based on the analysis and analysis of the data obtained through the survey tool. The study reached a set of results, the most important of which are: NGOs follow a decision-making mechanism. NGOs examine the problems and causes of the organization. Organizations are less concerned with stakeholder participation. Make decision. The main recommendations of the study are: To promote systematic methods of decision-making in NGOs in Gaza Strip: Local NGOs in Gaza Strip should continue to develop their competencies in order to make sound and correct decisions and encourage them to follow the scientific methodologies in the mechanism of taking Decisions. (shrink)

The aim of this paper is to argue that intuitive methods of moral decision making are objective tools on the grounds that they are reasons based. First, I will conduct a preliminary analysis in which I highlight the acceptance of methodological pluralism in the practice of medical ethics. Here, the point is to show the possibility of using intuitive methods given the pluralism framework. Second, I will argue that the best starting point of elaborating such methods is a (...) bottom-up perspective. Third, I will address the worry of subjectivism. Under the influence of certain rationalist positions and recent developments in cognitive science and moral psychology, one might think that intuitive methods of moral decision making are essentially subjective and emotion based. If moral intuitions are the result of emotional reactions and intuitive reasoning is emotionally driven, then there are reasons to believe intuitive methods are subjective and relative to particular psychological constitution. Against this picture, I will argue that intuitive methods of moral decision making are essentially reasons-based. A Wittgensteinian approach will show that intuitive methods of moral decision making are conceptually linked with criteria of morality. (shrink)

Abstract: This study has effectively tackled the critical challenge of accurate calorie prediction in dishes by employing a robust neural network-based model. With an outstanding accuracy rate of 99.32% and a remarkably low average error of 0.009, our model has showcased its proficiency in delivering precise calorie estimations. This achievement equips individuals, healthcare practitioners, and the food industry with a powerful tool to promote healthier dietary choices and elevate awareness of nutrition. Furthermore, our in-depth feature importance analysis has shed light (...) on the indispensable role played by specific nutritional attributes in calorie estimation. This unveiling of crucial factors provides valuable insights for further research endeavors and practical applications. Notably, this research extends its impact beyond the immediate context by making substantial contributions to the domains of nutrition science and dietary planning. It underscores the transformative potential of artificial intelligence, demonstrating how it can revolutionize our approach to food, nutrition, and health. As the world grapples with the challenges of diet-related health issues and environmental concerns, the accuracy and precision achieved in calorie prediction through neural networks represent a significant stride towards more informed and conscientious dietary practices. In this era of data-driven decision-making, our research paves the way for healthier lifestyles, heightened nutritional awareness, and a more health-conscious society. (shrink)

In the end of the course the student will be able to: • Understand the functioning of data mining tools and their contributions to managerial professions • Master the use of dynamic search tools on the open web and on the dark web. • Use the proper tools according to the objectives sought • Master the latest trends and innovations in Business Analytics • Analyze the opportunities offered in terms of data mining by artificial intelligence and IoT.

It is often said that ‘every logical truth is obvious’ (Quine 1970: 82), that the ‘axioms and rules of logic are true in an obvious way’ (Murawski 2014: 87), or that ‘logic is a theory of the obvious’ (Sher 1999: 207). In this chapter, I set out to test empirically how the idea that logic is obvious is reflected in the scholarly work of logicians and philosophers of logic. My approach is data-driven. That is to say, I propose (...) that systematically searching for patterns of usage in databases of scholarly works, such as JSTOR, can provide new insights into the ways in which the idea that logic is obvious is reflected in logical and philosophical practice, i.e., in the arguments that logicians and philosophers of logic actually make in their published work. (shrink)

With the growing focus on prevention in medicine, studies of how to describe risk have become increasing important. Recently, some researchers have argued against giving patients “comparative risk information,” such as data about whether their baseline risk of developing a particular disease is above or below average. The concern is that giving patients this information will interfere with their consideration of more relevant data, such as the specific chance of getting the disease (the “personal risk”), the risk reduction (...) the treatment provides, and any possible side effects. I explore this view and the theories of rationality that ground it, and I argue instead that comparative risk information can play a positive role in decision-making. The criticism of disclosing this sort of information to patients, I conclude, rests on a mistakenly narrow account of the goals of prevention and the nature of rational choice in medicine. (shrink)

The concept of “life” certainly is of some use to distinguish birds and beavers from water and stones. This pragmatic usefulness has led to its construal as a categorical predicate that can sift out living entities from non-living ones depending on their possessing specific properties—reproduction, metabolism, evolvability etc. In this paper, we argue against this binary construal of life. Using text-mining methods across over 30,000 scientific articles, we defend instead a degrees-of-life view and show how these methods can contribute to (...) experimental philosophy of science and concept explication. We apply topic-modeling algorithms to identify which specific properties are attributed to a target set of entities (bacteria, archaea, viruses, prions, plasmids, phages and the molecule of adenine). Eight major clusters of properties were identified together with their relative relevance for each target entity (two that relate to metabolism and catalysis, one to genetics, one to evolvability, one to structure, and—rather unexpectedly—three that concern interactions with the environment broadly construed). While aligning with intuitions—for instance about viruses being less alive than bacteria—these quantitative results also reveal differential degrees of performance that have so far remained elusive or overlooked. Taken together, these analyses provide a conceptual “lifeness space” that makes it possible to move away from a categorical construal of life by empirically assessing the relative lifeness of more-or-less alive entities. (shrink)

The ongoing explosion of interest in artificial intelligence is fueled in part by recently developed techniques in machine learning. Those techniques allow automated systems to process huge amounts of data, utilizing mathematical methods that depart from traditional statistical approaches, and resulting in impressive advancements in our ability to make predictions and uncover correlations across a host of interesting domains. But as is now widely discussed, the way that those systems arrive at their outputs is often opaque, even to the (...) experts who design and deploy them. Is it morally problematic to make use of opaque automated methods when making high-stakes decisions, like whether to issue a loan to an applicant, or whether to approve a parole request? Many scholars answer in the affirmative. However, there is no widely accepted explanation for why transparent systems are morally preferable to opaque systems. We argue that the use of automated decision-making systems sometimes violates duties of consideration that are owed by decision-makers to decision-subjects, duties that are both epistemic and practical in character. Violations of that kind generate a weighty consideration against the use of opaque decision systems. In the course of defending our approach, we show that it is able to address three major challenges sometimes leveled against attempts to defend the moral import of transparency in automated decision-making. (shrink)

Several recent criticisms of the somatic marker hypothesis (SMH) identify multiple ambiguities in the way it has been formulated by its chief proponents. Here we provide evidence that this hypothesis has also been interpreted in various different ways by the scientific community. Our diagnosis of this problem is that SMH lacks an adequate computational-level account of practical decision making. Such an account is necessary for drawing meaningful links between neurological- and psychological-level data. The paper concludes by providing (...) a simple, five-step model of practical decision making. Recasting SMH in terms of this model generates more precise and empirically tractable computational-level hypotheses about the various ways that somatic markers might influence practical decisions. (shrink)

This study aimed to highlight the nature of the relationship between knowledge management and the strategic decision-making process, considering that strategic decisions are formulated and made based on a specific knowledge perspective. The study targeted the university management, deans of faculties, and college directors at Al-Azhar University - Gaza. The study followed a descriptive-analytical approach, and data was collected through a questionnaire designed to cover six dimensions related to knowledge management processes and an axis related to strategic (...) decision-making. The data was analyzed using various statistical methods. The study results showed a statistically significant positive relationship between the role of knowledge management processes and activating the strategic decision-making process at Al-Azhar University - Gaza. The study recommended that the university pay more attention to the processes of knowledge awareness and application as they serve as the link between existing knowledge and the creation of good knowledge. Furthermore, these processes are considered the essential means through which the university improves the effectiveness of strategic decisions and enhances its position. (shrink)

This article examines the complaint that arbitrary algorithmic decisions wrong those whom they affect. It makes three contributions. First, it provides an analysis of what arbitrariness means in this context. Second, it argues that arbitrariness is not of moral concern except when special circumstances apply. However, when the same algorithm or different algorithms based on the same data are used in multiple contexts, a person may be arbitrarily excluded from a broad range of opportunities. The third contribution is to (...) explain why this systemic exclusion is of moral concern and to offer a solution to address it. (shrink)

Goal: To assess public knowledge and attitudes towards the family’s role in deceased organ donation in Europe. -/- Methods: A systematic search was conducted in CINHAL, MEDLINE, PAIS Index, Scopus, PsycINFO, and Web of Science on December 15th, 2017. Eligibility criteria were socio-empirical studies conducted in Europe from 2008 to 2017 addressing either knowledge or attitudes by the public towards the consent system, including the involvement of the family in the decision-making process, for post-mortem organ retrieval. Screening and (...) data collection were performed by two or more independent reviewers for each record. -/- Results: Of the 1482 results, 467 studies were assessed in full-text form, and 33 were included in this synthesis. When the deceased has not expressed any preference, a majority of the public support the family's role as a surrogate decision-maker. When the deceased expressly consented, the respondents' answers depend on whether they see themselves as potential donors or as a deceased's next-of-kin. Answers also depend on the relationship between the deceased and the decision-maker(s) within the family, and on their ethnic or cultural background. -/- Conclusions: Public views on the authority of the family in organ donation decision-making require further research. A common conceptual framework and validated well-designed questionnaires are needed for future studies. The findings should be considered in the development of Government policy and guidance regarding the role of families in deceased organ donation. (shrink)

This thesis contains three studies which provide theoretical analysis and empirical evidence on the decision-making of farm households under shocks and imperfect markets in Vietnam. The first study attempts to investigate the effects of the 2007-08 global food crisis on the investment, saving and consumption decisions of household producers by using the panel data of the Vietnam Household Living Standard Survey (VHLSS), covering 2006 and 2008. The results show that the high food prices had a positive effect (...) on only fixed asset investments in the period of the crisis. When the price shocks are incorporated in the financial conditions, the findings reveal that the effects of household incomes, loans obtained and land sizes matter. The second study uses the Vietnam Access to Resources Household Survey (VARHS) of 2010 to assess the determinants of chemical fertiliser adoption for rice cultivation, and effects on productivity and household welfare. The analysis implements both nonparametric (propensity score matching) and parametric (instrumental variables) approaches. The findings show determinants affecting decision of adoption differ from those affecting decision of adoption intensity. The results show unsurprisingly positive impact on outcomes, but focus on advantage of using parametric approach to estimate these impacts. The third study employs a sub-sample from the 2008 VHLSS that is restricted to rural areas and to children from 10 to 14 years old to explore the relationship between farmland and the employment of children on their family’s farm. The hypothesis is tested in three models (the Tobit, Heckit and double-hurdle models), in which the dependent variables are examined for two stages of decision-making, including the probability of participation and the extent of participation. Empirical evidence supports the hypothesis that child labour increases in land-rich households and decreases in land-poor households. (shrink)

When comparing alternative courses of action, modern military decision makers often must consider both the military effectiveness and the ethical consequences of the available alternatives. The basis, design, calibration, and performance of a principles-based computational model of ethical considerations in military decision making are reported in this article. The relative ethical violation (REV) model comparatively evaluates alternative military actions based upon the degree to which they violate contextually relevant ethical principles. It is based on a set of (...) specific ethical principles deemed by philosophers and ethicists to be relevant to military courses of action. A survey of expert and non-expert human decision makers regarding the relative ethical violation of alternative actions for a set of specially designed calibration scenarios was conducted to collect data that was used to calibrate the REV model. Perhaps unsurprisingly, the survey showed that people, even experts, disagreed greatly amongst themselves regarding the scenarios’ ethical considerations. Despite this disagreement, two significant results emerged. First, after calibration the REV model performed very well in terms of replicating the ethical assessments of human experts for the calibration scenarios. The REV model outperformed an earlier model that was based on tangible consequences rather than ethical principles, that earlier model performed comparably to human experts, the experts outperformed human non-experts, and the non-experts outperformed random selection of actions. All of these performance comparisons were measured quantitatively and confirmed with suitable statistical tests. Second, although humans tended to value some principles over others, none of the ethical principles involved—even the principle of not harming civilians—completely overshadowed all of the other principles. (shrink)

Patient preference predictors aim to solve the moral problem of making treatment decisions on behalf of incapacitated patients. This commentary on a case of an unrepresented patient at the end of life considers 3 related problems of such predictors: the problem of restricting the scope of inputs to the models (the “scope” problem), the problem of weighing inputs against one another (the “weight” problem), and the problem of multiple reasonable solutions to the scope and weight problems (the “multiple reasonable (...) models” problem). Each of these problems poses challenges to reliably implementing patient preference predictors in important, high-stakes health care decision making. This commentary also suggests a way forward. (shrink)

Looks into evaluation of information provision probability from different sources, based on use of linguistic variables. Formation of functions appurtenant for its unclear variables provides for adoption of decisions by the decision maker, in conditions of nonprobabilistic equivocation. The development of market relations in Ukraine increases the independence and responsibility of enterprises in justifying and making management decisions that ensure their effective, competitive activities. As a result of the analysis, it is determined that the condition of economic facilities (...) can be described and determined by the decision-maker, in the presence of the necessary information. The confidence of the decision-maker in the information received is different and the decisions made have a correspondingly different level of information risk. It is important to substantiate the procedure for assessing the numerical extent of information risk in decision-making based on the information obtained in conditions of uncertainty. The use of a linguistic variable in the processing of expert data presented in the form of a matrix of binary relations of values of the membership function, which allowed to move to further processing of knowledge to support decision-making in the management of industrial, commercial, financial and other activities. As a mathematical model for estimating the numerical measure of information risk when making decisions based on the information obtained in conditions of non-stochastic uncertainty, a model has been developed to model natural language uncertainties, which differs from existing ones by formalizing knowledge taking into account uncertainty of input information. Making such a clear decision in a fuzzy environment has appropriate values of effectiveness and risk. The paper proposes all the functions and accessories of indicators of both quantitative nature and qualitative nature to bring their values in the field of definition to one scale. Then the indicator of the effectiveness of decision-making will be a measure of the clarity of the cross-section of fuzzy subsets, which correspond to the introduced indicators of information risk. The condition of economic facilities can be described and determined by the decision-maker, if the necessary information is available. Decision-making on thenumerical measure of information risk must be determined by a set of basic indicators, which can be both quantitative and qualitative in nature. Predictive values of indicators should be determined in conditions of nonstochastic uncertainty. In this case, the indicators of a quantitative nature can be determined by fuzzy triangular numbers, which implement a high level of confidence in the subjective judgments of experts. Indicators of qualitative nature should be presented in linguistic variables. The values of the indicators of qualitative nature that are predicted must be considered for all fuzzy variable terms-sets of linguistic variables introduced into consideration. For any fuzzy variable, the introduction to the consideration of a clear set of values as carriers of the α-level of its membership function allows to reduce to a single interpretation of the predicted values of indicators of quantitative and qualitative nature in terms of non-stochastic uncertainty. (shrink)

Because an increasing number of patients have medical conditions that render them incompetent at making their own medical choices, more and more medical choices are now made by surrogates, often patient family members. However, many studies indicate that surrogates often do not discharge their responsibilities adequately, and in particular, do not choose in accordance with what those patients would have chosen for themselves, especially when it comes to end-of-life medical choices. This chapter argues that a significant part of the (...) explanation of such surrogate failure is that family surrogates are likely to undergo anticipatory grief when making end-of-life decisions. After clarifying both the emotional structure and object of grief, I propose that the pending death of a loved one induces an emotional conflict in surrogates between the care demanded by their responsibility as surrogates and the attachment surrogates feel toward their dying loved one, an attachment surrogates "resolve" in the direction of attachment rather than care. This hypothesis helps to explain both surrogates' general inability to exercise "substituted judgment" on behalf of their loved ones and a wide swath of the particular data regarding this inability (e.g., that surrogates more often err by choosing overtreatment). I conclude by considering possible clinical and philosophical responses to this hypothesis. (shrink)

Abstract In light of the complexity of unfolding disasters, the diversity of rapidly evolving events, the enormous amount of generated information, and the huge pool of casualties, emergency responders (ERs) may be overwhelmed and in consequence poor decisions may be made. In fact, the possibility of transporting the wounded victims to one of several hospitals and the dynamic changes in healthcare resource availability make the decision process more complex. To tackle this problem, we propose a multicriteria decision support (...) service, based on the Analytic Hierarchy Process (AHP) method, that aims to avoid overcrowding and outpacing the capacity of a hospital to effectively provide the best care to victims by finding out the most appropriate hospital that meets the victims’ needs. The proposed approach searches for the most appropriate healthcare institution that can effectively deal with the victims’ needs by considering the availability of the needed resources in the hospital, the victim’s wait time to receive the healthcare, and the transfer time that represents the hospital proximity to the disaster site. The evaluation and validation results showed that the assignment of hospitals was done successfully considering the needs of each victim and without overwhelming any single hospital. (shrink)

Data-driven predictive algorithms are widely used to automate and guide high-stake decision making such as bail and parole recommendation, medical resource distribution, and mortgage allocation. Nevertheless, harmful outcomes biased against vulnerable groups have been reported. The growing research field known as 'algorithmic fairness' aims to mitigate these harmful biases. Its primary methodology consists in proposing mathematical metrics to address the social harms resulting from an algorithm's biased outputs. The metrics are typically motivated by -- or substantively (...) rooted in -- ideals of distributive justice, as formulated by political and legal philosophers. The perspectives of feminist political philosophers on social justice, by contrast, have been largely neglected. Some feminist philosophers have criticized the local scope of the paradigm of distributive justice and have proposed corrective amendments to surmount its limitations. The present paper brings some key insights of feminist political philosophy to algorithmic fairness. The paper has three goals. First, I show that algorithmic fairness does not accommodate structural injustices in its current scope. Second, I defend the relevance of structural injustices -- as pioneered in the contemporary philosophical literature by Iris Marion Young -- to algorithmic fairness. Third, I take some steps in developing the paradigm of 'responsible algorithmic fairness' to correct for errors in the current scope and implementation of algorithmic fairness. I close by some reflections of directions for future research. (shrink)

When it comes to online fashion, this research focused on the interaction between three factors: preferences for online fashion goods, consumer buying choices for online fashion products, and brand image. A descriptive correlational approach was used. A total of 184 sampled active online purchasers of fashion items from a population of 350 online buyers in Thailand participated in the research. The study was carried out with the use of tools that had been adopted. Descriptive data showed that respondents had (...) a high preference for online marketing, a positive attitude towards online fashion goods, a high degree of consumer buying choices towards online fashion products, and a high preference for favorable brand image in the research. There were significant variations in respondents' preferences for online shopping as well as attitudes toward fashion items and consumer purchase decisions based on age, civil status, education, monthly income, and profession, according to the results of a test of difference. A further connection test revealed that respondents' liking for online marketing tends to improve their attitude toward online fashion items and consumer buying choices. In the end, the results of multiple regression analysis revealed that age, civil status and education level, online marketing approach, attitude towards online fashion goods, and brand image and attitude are all predictors of buying choices. The study's practical consequences for an internet business are addressed. The study's practical consequences for an internet business are addressed. (shrink)

Teddy Seidenfeld – CMU An old, wise, and widely held attitude in Statistics is that modest intervention in the design of an experiment followed by simple statistical analysis may yield much more of value than using very sophisticated statistical analysis on a poorly designed existing data set.

This work presents a post-positivist research framework to explain any surprising fact in the evolutionary path of a complex, dynamic and contingent social phenomenon. Primarily, it reconciles the ontological and epistemological assumptions of Critical Realism with the principles of American Pragmatism. Then, the research approach is presented: theoretical propositions about a social structure are translated into a set of grammar rules that acknowledges a pattern of sequences of events of either individual action or social interaction between actors within a real (...) social system. The result is a discrete mathematical model for a concrete category of social process based on these rules. Finally, data-grounded refinement of the theory is possible by the comparison between cases belonging to the same category, but differing about some contingent pattern of sequences of event outcomes. Consequently, their grammars differ in some pairs of context-sensitive rules that can explain this surprising fact, such that the derivation of this alternative historical trajectory of event outcomes becomes an extension to the early category of social process. In this sense, the proposed framework suggests there is a hierarchy of classes of grammars for middle-range explanations based upon the ontological assumption of the generative nature of social reality. (shrink)

Introduction: Online eye-tracking has been used in this study to assess the impacts of different cultural backgrounds on information discernment. An online platform called RealEye allowed participants to engage in the eye-tracking study from their personal computer webcams, allowing for higher ecological validity and a closer replication of social media interaction. -/- Methods: The study consisted of two parts with a total of five visuals of social media posts mimicking news posts on Twitter, Instagram, and Facebook. Participants were asked to (...) view examples of real and fake news taken from a news fact-checking website, Snopes, and their eye movements were recorded during the process. Participants were recruited through Prolific and SONA; the total sample size for study 1.1 was 29 participants, and the total for study 1.2 was 25 participants, after removing poor eye-tracking data. A total of five visual images comprising true and false news were shown to the participant, study 1.1 had three examples and study 1.2 had two examples. There were two main cultural backgrounds in focus: participants born in China or the United Kingdom. -/- Results: Results suggested that participants follow a similar visual pattern of attention to Areas of Interest (AOIs) on the posts, which leads us to believe that due to the global standardisation of popular social media platforms, a bias might have occurred during information discernment. -/- Discussion: It is suggested that regardless of country background, users may have similar eye-tracking results while viewing a social media post because social media platform formats are standardised globally. Further research would recommend looking at language and linguistic traits when seeking differences between country backgrounds during online information discernment. (shrink)

Love is often seen as the most intimate aspect of our lives, but it is increasingly engineered by a few programmers with Artificial Intelligence (AI). Nowadays, numerous dating platforms are deploying so-called smart algorithms to identify a greater number of potential matches for a user. These AI-enabled matchmaking systems, driven by a rich trove of data, can not only predict what a user might prefer but also deeply shape how people choose their partners. This paper draws on Jürgen (...) Habermas’s “colonization of the lifeworld” thesis to critically explore the insidious influence of delegating romantic decision-making to an algorithm. The love lifeworld is colonized inasmuch as online dating algorithms encroach into our romantic relations to the extent that communicative action within romance is replaced by the technocratic rules of an algorithm. (shrink)

Abstract: The COVID-19 pandemic has posed unprecedented challenges to global healthcare systems, necessitating accurate and timely forecasting of cases for effective mitigation strategies. In this research paper, we present a novel approach to predict COVID-19 cases using Artificial Neural Networks (ANNs), harnessing the power of machine learning for epidemiological forecasting. Our ANNs-based forecasting model has demonstrated remarkable efficacy, achieving an impressive accuracy rate of 97.87%. This achievement underscores the potential of ANNs in providing precise and data-driven insights into (...) the dynamics of the pandemic. However, this paper underscores the critical importance of a comprehensive evaluation beyond accuracy, including metrics such as sensitivity, specificity, and the area under the ROC curve (AUC-ROC), to assess the model's performance robustness. The research paper offers detailed insights into the architecture of the ANN model, encompassing critical hyperparameters, data preprocessing techniques, and regularization strategies employed to optimize model accuracy. Ethical considerations surrounding data privacy and potential biases within the COVID-19 dataset are also addressed. While the achieved accuracy is a significant milestone, this study underscores the dynamic and evolving nature of the pandemic, necessitating continuous model refinement and validation. Furthermore, it emphasizes the importance of considering false positives and false negatives in the context of public health decision-making. In conclusion, this research contributes to the arsenal of tools available for pandemic management by showcasing the potential of ANNs in COVID-19 case forecasting. It encourages ongoing exploration and adaptation of predictive models to enhance their applicability in real-world public health scenarios, ultimately contributing to more effective pandemic control and response efforts. (shrink)

Abstract: In, this research embodies the spirit of interdisciplinary collaboration, bringing together data science, healthcare, and public health to address one of the most significant global health challenges in recent history. The achievements of this study underscore the potential of advanced machine learning techniques to enhance our understanding of the pandemic and guide effective decision-making. As we navigate the ongoing battle against COVID-19 and prepare for future health emergencies, the lessons learned from this research serve as a (...) testament to the power of data-driven insights and the unwavering commitment of the global scientific community in the face of adversity. (shrink)

A cognitivist account of decision-making views choice behaviour as a serial process of deliberation and commitment, which is separate from perception and action. By contrast, recent work in embodied decision-making has argued that this account is incompatible with emerging neurophysiological data. We argue that this account has significant overlap with an embodied account of predictive processing, and that both can offer mutual development for the other. However, more importantly, by demonstrating this close connection we uncover (...) an alternative perspective on the nature of decision-making, and the mechanisms that underlie our choice behaviour. This alternative perspective allows us to respond to a challenge for predictive processing, which claims that the satisfaction of distal goal-states is underspecified. Answering this challenge requires the adoption of an embodied perspective. (shrink)

Decision-making by a Data and Safety Monitoring Board (DSMB) regarding clinical trial conduct and termination is intricate and largely limited by cases and rules. Decision-making by legal jury is also intricate and largely constrained by cases and rules. In this paper, I argue by analogy that legal decision-making, which strives for a balance between competing demands of conservatism and innovation, supplies a good basis to the logic behind DSMB decision-making. Using the (...) doctrine of precedents in legal reasoning as my central analog will lead us to a decision-theoretic analogy for much more systematic documentation of decisions in clinical trials. My conclusion is twofold: every DSMB decision should articulate a clear general principle (a ratio decidendi) that gives reason for the decision; and all such decisions should be made public. I use Bartha’s (2010) theory of analogical arguments to both frame and assess my argument by analogy. (shrink)

Recently, it has been argued that the use of Big Data transforms the sciences, making data-driven research possible and studying causality redundant. In this paper, I focus on the claim on causal knowledge by examining the Big Data project EXPOsOMICS, whose research is funded by the European Commission and considered capable of improving our understanding of the relation between exposure and disease. While EXPOsOMICS may seem the perfect exemplification of the data-driven view, I (...) show how causal knowledge is necessary for the project, both as a source for handling complexity and as an output for meeting the project’s goals. Consequently, I argue that data-driven claims about causality are fundamentally flawed and causal knowledge should be considered a necessary aspect of Big Data science. In addition, I present the consequences of this result on other data-driven claims, concerning the role of theoretical considerations. I argue that the importance of causal knowledge and other kinds of theoretical engagement in EXPOsOMICS undermine theory-free accounts and suggest alternative ways of framing science based on Big Data. (shrink)

In 2012, a major traffic safety organization tasked the MIT AgeLab with developing a data-driven system for rating the effectiveness of new technologies intended to improve safety. Such a system was envisioned as having the potential to educate and guide consumers towards more confident and strategic purchasing decisions, ideally encouraging adoption of technologies with demonstrated safety benefit. In addition, an evaluation of the status and extent of existing data was seen as a way of identifying research gaps (...) in the state of knowledge about safety systems. The focus was on technologies as a class, not on a rating review of individual vehicle model implementations. As conceptualized, the system aimed to complement traditional NCAP style ratings as well as to provide consumers with transparent information on early stage and often improving safety technologies. Development of the rating system and identification of data was undertaken in consultation with a range of academic, industrial, consumer, NGO, and governmental experts as well as with representatives of many of the major automotive manufacturers and suppliers. -/- A key observation that emerged was that data on objectively demonstrable real-world benefits were generally sparse and often lower than expectations based on theoretical considerations, simulation studies, or pre-production evaluations. A number of experts and industry representatives expressed some surprise at both the divergence between theoretical and observed benefits and the relative scarcity of data upon which to make objective assessments, while others were quite aware of these issues and the need for the development of objective data under real-world operating conditions. A number of factors that might be relevant to understanding why such differences between expected and observed benefits exist were identified. One outcome of this effort was the founding of the Advanced Vehicle Technology (AVT) consortium to collect and examine objective data under naturalistic driving conditions of how drivers interact with, engage or don’t engage, various production safety and driver assistance systems. This ongoing effort is contributing to insights concerning actual benefits and reasons for benefit gaps. -/- Drawing from our initial work, as well as newer sources of data, we argue that the evaluation and rating of safety and driver assistance technologies for informing the consumer and the public at large should consider both theoretical potential and existing demonstrated benefit of specific technologies. This position is increasingly relevant as the effectiveness of many newer technologies have the potential to actually improve over the lifecycle of a vehicle through software updates. The emphasis on ratings based on observed benefit for actual drivers under real-world conditions is proposed to be complementary, rather than competing with, ratings focused largely on controlled test track evaluations of engineered capability. In addition, a case is made for providing ratings that assesses benefit relative to overall crash, injury, and fatality rates – and in relation to the specific scenario / crash event type that a given technology is intended to address. This approach should aid consumers in considering the extent to which a specific technology is or is not relevant to their particular driving needs. (shrink)

Most medical research and a substantial amount of non-medical research, especially that involving human participants, is governed by some kind of research ethics committee (REC) following the recommendations of the Declaration of Helsinki for the protection of human participants. The role of RECs is usually seen as twofold: firstly, to make some kind of calculation of the risks and benefits of the proposed research, and secondly, to ensure that participants give informed consent. The extent to which the role of the (...) REC includes the former is not uncontroversial. Indeed, the most prevalent debate on the role of RECs sees liberals and strong paternalists arguing over the importance of informed consent given by competent agents versus the significance of making benevolent decisions on behalf of others. On the one hand, liberals argue for the rights of competent adults to decide for themselves the kinds and extents of risks to which they wish to expose themselves. On the other hand, proponents of strong paternalism raise concerns about the likelihood of participants being able to truly understand the complex data involved in research. They support a role for RECs in which they exercise duties of benevolence towards patients and participants by limiting the extent to which they can be exposed to significant, permanent and irreversible harms. In this paper, we will argue that when it comes to decisions about risk it is neither possible nor desirable for RECs to adopt either role. (shrink)

Existing discussions about automated decision-making focus primarily on its inputs and outputs, raising questions about data collection and privacy on one hand and accuracy and fairness on the other. Less attention has been devoted to critically examining the temporality of decision-making processes—the speed at which automated decisions are reached. In this paper, I identify four dimensions of algorithmic speed that merit closer analysis. Duration (how much time it takes to reach a judgment), timing (when automated (...) systems intervene in the activity being evaluated), frequency (how often evaluations are performed), and lived time (the human experience of algorithmic speed) are interrelated, but distinct, features of automated decision-making. Choices about the temporal structure of automated decision-making systems have normative implications, which I describe in terms of "disruption," "displacement," "re-calibration," and "temporal fairness," with values such as accuracy, fairness, accountability, and legitimacy hanging in the balance. As computational tools are increasingly tasked with making judgments about human activities and practices, the designers of decision-making systems will have to reckon, I argue, with when—and how fast—judgments ought to be rendered. Though computers are capable of reaching decisions at incredible speeds, failing to account for the temporality of automated decision-making risks misapprehending the costs and benefits automation promises. (shrink)

This research assesses the factors influencing students' decision-making when choosing a university. The study proposes eight factors, such as parental or guardian influence, high school teacher recommendations, graduate quality, colleague recommendations, location, school fees, learning environment, and university reputation, on students' university choices. A quantitative approach was employed, utilizing both secondary and primary data. A total of 330 students were sampled for this study. The data were analyzed using SPSS, employing descriptive statistics for data summarization (...) and inferential statistics for analysis. Descriptive tools such as frequency analysis were used to examine the distribution of responses, while regression analysis was conducted to test the predictive power of the factors. The findings of this study indicate that factors such as parental or guardian influence, high school teacher recommendations, graduate quality, colleague recommendations, location, school fees, learning environment, and university reputation are all significant considerations in students' decision-making process when choosing a university. These factors play an influential role in shaping students' preferences and choices. The results show that these factors significantly impact students' university choices. The study contributes to the existing literature by providing empirical evidence on the importance of these factors in the decision-making process. Understanding the influence of these factors can assist universities in developing targeted strategies to attract and meet the needs of prospective students. By recognizing the significance of parental involvement, fostering positive relationships with high school teachers, showcasing successful graduates, promoting positive word-of-mouth recommendations, providing a conducive learning environment, and building a solid reputation, universities can enhance their appeal and effectively compete in the higher education market. Further research in this area can explore additional factors and delve deeper into the mechanisms influencing students' decision-making when choosing a university. (shrink)

Ethical decision-making frameworks assist in identifying the issues at stake in a particular setting and thinking through, in a methodical manner, the ethical issues that require consideration as well as the values that need to be considered and promoted. Decisions made about the use, sharing, and re-use of big data are complex and laden with values. This paper sets out an Ethics Framework for Big Data in Health and Research developed by a working group convened by (...) the Science, Health and Policy-relevant Ethics in Singapore Initiative. It presents the aim and rationale for this framework supported by the underlying ethical concerns that relate to all health and research contexts. It also describes a set of substantive and procedural values that can be weighed up in addressing these concerns, and a step-by-step process for identifying, considering, and resolving the ethical issues arising from big data uses in health and research. This Framework is subsequently applied in the papers published in this Special Issue. These papers each address one of six domains where big data is currently employed: openness in big data and data repositories, precision medicine and big data, real-world data to generate evidence about healthcare interventions, AI-assisted decision-making in healthcare, public-private partnerships in healthcare and research, and cross-sectoral big data. (shrink)

Data is very important in any research experiment because it occupies a central place in making decisions based on findings resulting from the analysis of such data. Given its central role, it follows that such an important asset as data, deserve effective management in order to protect the integrity and provide an opportunity for effective problem-solving. The main thrust of this paper was to examine data management practices that should be adopted by scholars in maintaining (...) the quality of their research data. In achieving this, various concepts related to this paper were clarified. Various data management practices were also discussed beginning from data generation to data shredding. Based on the underlying observations from the light of the discussions made in this paper, it was recommended among others that: tertiary institutions in every part of the world should endeavour to establish a data management unit that will be saddled with the primary duty of formulating research data management policies, and the hosting of research data; every Journal should as a matter of compulsion, require the submission of data set corresponding to empirical papers submitted by authors and scholars. (shrink)

“Big Data” and data analytics affect all of us. Data collection, analysis, and use on a large scale is an important and growing part of commerce, governance, communication, law enforcement, security, finance, medicine, and research. And the theme of this symposium, “Individual and Informational Privacy in the Age of Big Data,” is expansive; we could have long and fruitful discussions about practices, laws, and concerns in any of these domains. But a big part of the audience (...) for this symposium is students and faculty in higher education institutions (HEIs), and the subject of this paper is data analytics in our own backyards. Higher education learning analytics (LA) is something that most of us involved in this symposium are familiar with. Students have encountered LA in their courses, in their interactions with their law school or with their undergraduate institutions, instructors use systems that collect information about their students, and administrators use information to help understand and steer their institutions. More importantly, though, data analytics in higher education is something that those of us participating in the symposium can actually control. Students can put pressure on administrators, and faculty often participate in university governance. Moreover, the systems in place in HEIs are more easily comprehensible to many of us because we work with them on a day-to-day basis. Students use systems as part of their course work, in their residences, in their libraries, and elsewhere. Faculty deploy course management systems (CMS) such as Desire2Learn, Moodle, Blackboard, and Canvas to structure their courses, and administrators use information gleaned from analytics systems to make operational decisions. If we (the participants in the symposium) indeed care about Individual and Informational Privacy in the Age of Big Data, the topic of this paper is a pretty good place to hone our thinking and put into practice our ideas. (shrink)

Big Data is a large-sized and complex dataset, which cannot be managed using traditional data processing tools. Mining process of big data is the ability to extract valuable information from these large datasets. Association rule mining is a type of data mining process, which is indented to determine interesting associations between items and to establish a set of association rules whose support is greater than a specific threshold. The classical association rules can only be extracted from (...) binary data where an item exists in a transaction, but it fails to deal effectively with quantitative attributes, through decreasing the quality of generated association rules due to sharp boundary problems. In order to overcome the drawbacks of classical association rule mining, we propose in this research a new neutrosophic association rule algorithm. The algorithm uses a new approach for generating association rules by dealing with membership, indeterminacy, and non-membership functions of items, conducting to an efficient decision-making system by considering all vague association rules. To prove the validity of the method, we compare the fuzzy mining and the neutrosophic mining. The results show that the proposed approach increases the number of generated association rules. (shrink)

We introduce and discuss a knowledge-driven distillation approach to explaining black-box models by means of two kinds of interpretable models. The first is perceptron (or threshold) connectives, which enrich knowledge representation languages such as Description Logics with linear operators that serve as a bridge between statistical learning and logical reasoning. The second is Trepan Reloaded, an ap- proach that builds post-hoc explanations of black-box classifiers in the form of decision trees enhanced by domain knowledge. Our aim is, firstly, (...) to target a model-agnostic distillation approach exemplified with these two frameworks, secondly, to study how these two frameworks interact on a theoretical level, and, thirdly, to investigate use-cases in ML and AI in a comparative manner. Specifically, we envision that user-studies will help determine human understandability of explanations generated using these two frameworks. (shrink)

Traditional medical practices and relationships are changing given the widespread adoption of AI-driven technologies across the various domains of health and healthcare. In many cases, these new technologies are not specific to the field of healthcare. Still, they are existent, ubiquitous, and commercially available systems upskilled to integrate these novel care practices. Given the widespread adoption, coupled with the dramatic changes in practices, new ethical and social issues emerge due to how these systems nudge users into making decisions (...) and changing behaviours. This article discusses how these AI-driven systems pose particular ethical challenges with regards to nudging. To confront these issues, the value sensitive design approach is adopted as a principled methodology that designers can adopt to design these systems to avoid harming and contribute to the social good. The AI for Social Good factors are adopted as the norms constraining maleficence. In contrast, higher-order values specific to AI, such as those from the EU High-Level Expert Group on AI and the United Nations Sustainable Development Goals, are adopted as the values to be promoted as much as possible in design. The use case of Amazon Alexa's Healthcare Skills is used to illustrate this design approach. It provides an exemplar of how designers and engineers can begin to orientate their design programs of these technologies towards the social good. (shrink)

This paper introduces a refined single-valued neutrosophic set (RSVNS) and presents a similarity measure of RSVNSs. Then a multicriteria decision-making method with RSVNS information is developed based on the similarity measure of RSVNSs. By the similarity measure between each alternative and the ideal solution (ideal alternative), all the alternatives can be ranked and the best one can be selected as well. Finally, an actual example on the selecting problems of construction projects demonstrates the application and effectiveness of the (...) proposed method. (shrink)

The aim of the research is to identify decision support systems and their impact on the development of the organizational structure in Palestinian higher education institutions. The research was applied to four Palestinian universities (Al-Aqsa University, the Islamic University, Al-Azhar University, the University of Palestine), and a sample of academics with an administrative position. The analytical descriptive approach was used and secondary data was obtained through a survey list distributed to the research community of academics in an administrative (...) position using the stratified random sample according to the university, and (250) survey lists were distributed and (216) were recovered with a recovery rate of (86.4%). The research concluded with a set of results, the most important of which is that there is a statistically significant relationship at the level of significance (α ≤ 0.05) between the dimensions of decision support systems: the potentials (material, human, technical, organizational) available for the use of decision support systems, the approval of senior management for the use of decision support systems. Training process and organizational structure development in Palestinian higher education institutions. The results also confirmed through the results of multiple linear regression that there is no effect of the two variables (available material capabilities, available human capabilities) in developing the organizational structure in Palestinian higher education institutions, while it was found that there is an effect of the following variables (technical capabilities available for using decision support systems, organizational capabilities). Available for the use of decision support systems, the approval of senior management for the use of decision support systems, the training process) in developing the organizational structure in Palestinian higher education institutions. The results confirmed that there were no statistically significant differences at the level (α ≤ 0.05) between the averages of the respondents' opinions about decision support systems and their impact on the development of the organizational structure in Palestinian higher education institutions due to the variable (scientific degree) and the variable (years of service). The research reached a set of recommendations, the most important of which is the need for the higher management of universities to pay attention to decision support systems, and to make intensive and continuous efforts to support and enhance efforts aimed at enriching and enhancing the use of decision support systems in universities. And that universities actively contribute to increasing interest in providing material and technical capabilities and making them available to all users, while continuing to keep pace with modern technological means and techniques. And increasing the interest in the organizational capabilities available through an independent unit of decision support systems that reports directly to the senior management in Palestinian universities, and allowing the organizational structure to easily flow information between different faculties, departments and departments. Focusing on training as a major component of decision support systems, allocating a special budget for training on the systems to be applied, and linking the training system to the workers’ training needs accurately, in order to improve and develop their skills, which increases the quality and efficiency of their performance. He urged universities to involve their employees when designing the organizational structure, by communicating with all colleges and departments and holding brainstorming sessions, workshops and periodic meetings with all departments. (shrink)

The studies of ethics had long been under-dealt although it is the kind of primary in sustaining a civility. It is hardly deniable that the concept of efficiency and productivity has hailed on the mindedness and interest of academic community. The narrative of ethics or social justice would be ridiculed as the kind of Greek juggle on philosophy or put to be on neglect for its lacking or default on the modern disciplinary frame in the academics. A cure, however, seems (...) not to be from a juggernaut of modern capitalistic civilization and its supportive paradigm of understanding so that alternative way for any more realistic solution can be made exploitative from the ethics or similar tools of motivation. Now we talk about noblesse oblige or benevolent capitalism as one important dose for the new paradigm of social promise or reformed capitalism. The ethics studies have increasingly made its way as focused in the discipline of business and public administration. Typically, it is profiled in combination of leadership studies, and the civility tends to accept this narrative, beyond the liberal competition and fairness school, as one of important element when we consider the concept of social justice. The modern professionals, for example, bar members, medical doctors, and many of licensees for their business operation, also clone to carry their responsibility under a specific ethical code. This would be the kind of safety code from the abusive or madly profit-driven capitalism. More importantly, I believe that a sheer understanding of ethics and social justice would be assets and equipment for the ruling class in the contemporary society. In this context, the paper would survey an ethical theory or decision making model, history of Korea as well as a recent episode involving the chief prosecutor of South Korea. Korean leadership, typically for the powerful cores or inner circle princelings, can be revisited in purview of ethics studies, such as Cooper‟s design and ethical decision making model. (shrink)

Abstract: This study introduces a machine learning-based model for predicting student performance using a comprehensive dataset derived from educational sources, encompassing 15 key features and comprising 62,631 student samples. Our five-layer neural network demonstrated remarkable performance, achieving an accuracy of 89.14% and an average error of 0.000715, underscoring its effectiveness in predicting student outcomes. Crucially, this research identifies pivotal determinants of student success, including factors such as socio-economic background, prior academic history, study habits, and attendance patterns, shedding light on the (...) nuanced dynamics of student performance. The key influential features identified in this study offer valuable insights into the complex factors shaping student achievement. These insights are vital for educators, policymakers, and institutions seeking to enhance educational outcomes and promote equitable access to quality education. This research provides a data-driven foundation for proactive interventions, personalized learning strategies, and support systems, ultimately contributing to improved student performance and academic success. The high accuracy of the predictive model and the feature analysis it provides empower decision-makers in the education sector. This model holds significant potential for applications in student performance monitoring, early intervention, and tailored educational strategies. By adopting a data-driven approach, this work advances the field of educational analytics and contributes to the goal of fostering student success and educational equity. (shrink)