The scope and reach of information, driven by the explosive growth of information technologies and content types, has expanded dramatically over the past 30 years. The consequences of these changes to records and information management (RIM) professionals are profound, necessitating not only specialized knowledge but added responsibilities. RIM professionals require a professional ethics to guide them in their daily practice and to form a basis for developing and implementing organizational policies, and Mooradian’s new (...) book provides a rigorous outline of such an ethics. Taking an authoritative principles/rules based approach to the subject, this book comprehensively addresses •the structure of ethics, outlining principles, moral rules, judgements, and exceptions; •ethical reasoning, from meaning and logic to dilemmas and decision methods; •the ethical core of RIM, discussing key topics such as organizational context, the positive value of accountability, conflicts of interest, and confidentiality; •important ethical concerns like copyright and intellectual property, whistleblowing, information leaks, disclosure, and privacy; and •the relationship between RIM ethics and information governance. -/- An essential handbook for information professionals who manage records, archives, data, and other content, this book is also an ideal teaching text for students of information ethics . (shrink)

All societies have to balance privacy claims with other moral concerns. However, while some concern for privacy appears to be a common feature of social life, the definition, extent and moral justifications for privacy differ widely. Are there better and worse ways of conceptualising, justifying, and managing privacy? These are the questions that lie in the background of this thesis. -/- My particular concern is with the ethical issues around privacy that are tied to the rise of new information (...) and communication technologies (henceforth “ICT”). The focus here is on technologies involved in processing personal data in its broadest sense, including “any operation or set of operations which is performed on personal data or on sets of personal data, whether or not by automated means, such as collection, recording, organisation, structuring, storage, adaptation or alteration, retrieval, consultation, use, disclosure by transmission, dissemination or otherwise making available, alignment or combination, restriction, erasure or destruction.” The issue of moral concern is commonly designated as that of 'data privacy'. -/- The first step is to consider why privacy matters, or ought to matter, in the first place. And here we run into our first difficulty, since both the conceptualisation of privacy and the justification for the right to privacy are matters for contention. On the one hand, we are animated by a moral concern for privacy in some sense; on the other, we lack the conceptual and moral resources for making sense of this concern or for promoting particular social policies and criticising others. This often-cited dichotomy and its ensuing confusion have been the central motivation for most of the philosophical literature on privacy – including this thesis. -/- In what follows, I will suggest a way out of the confusion that pervades the current debate on data privacy. I will essentially argue two claims: one, that the concept of (data) privacy is uniquely characterised by a particular context within which we exchange information; two, that because (data) privacy constitutes an intrinsic value, its moral concerns and justifications ought to be deliberated as such. -/- The first chapter sketches the current data privacy landscape by introducing a number of concepts and arguing the unique nature of the landscape. I argue that the traditional distinction between four kinds of privacy (physical, mental, decisional and informational) is no longer accurate, given the technological context that reduces, unifies and processes all aspects of people’s lives by means of digital data. In fact, to hold on to the distinction leads to a fundamental misconception of data privacy. I further argue that today’s distinction between data protection and data privacy runs the risk of ignoring the fact that the former is an inherent aspect of the latter. These arguments allow me to settle on a (provisional) definition of data privacy: controlling how data about identifiable people is processed by ICT. -/- Next, I draw a line between descriptive and normative accounts of data privacy, and explain why this book belongs to the latter category. After a brief discussion of the coherence and distinctiveness theses, and of the difference between instrumental and intrinsic theories of privacy, I introduce the problem of data privacy as a distinct, coherent, and intrinsic moral concern. Three arguments support this position: one, data privacy is a coherent moral concern because it is motivated by a singular moral concept; two, data privacy is a distinct moral concern because the privacy challenges we face today are of a fundamentally new nature – distinct from any moral concerns we’ve faced in the past; and three, data privacy is an intrinsic moral concern that is predominantly justified, not by what it instrumentally protects us from, but by what it intrinsically constitutes. -/- Chapter Two makes the case against a harm-based, consequentialist approach to data privacy. After a brief introduction to the basic tenets of consequentialism I discuss harm-based arguments in favour as well as against privacy. I explain how privacy may protect us against psychological distress, but at the same time constitute the source of that distress; how privacy may be necessary for the proper functioning of democratic societies, but at the same time provide a cloak for anti-social and immoral behaviour; and how privacy protects us against the improper use of our personal data by others, but at the same enables us to conceal information or spread false information about ourselves in ways that are harmful to others. Next, I argue that consequentialism essentially relies on two flawed presumptions – that we know what harm is, and that we know how to predict it – and discuss the controversies surrounding definitions and predictions of harm. This argument becomes especially pertinent in today’s rapidly changing technological context: we may guess, and make assumptions, perhaps even find correlations, but don’t really know or understand (yet) with any degree of certainty the harmful consequences today’s data privacy violations are causing or will eventually cause – if any. -/- I illustrate this with examples of recent data privacy violations, and with a brief overview of consequentialist arguments for and against data privacy. Those who invoke consequentialist arguments against data privacy claim that personal data is nothing more than a raw material that can be turned into something useful and valuable without harming anyone. Choosing to move fast and break things – a popular mantra that promotes the benefits of technological “permissionless innovation” over and above its risks and costs – they draw our attention to the many benefits free data flows generate. Those who rely on consequentialist arguments in favour of data privacy, on the other hand, insist on actual and potential misuse of personal data. Rather careful than sorry, they draw our attention to the urgent need to take back control and protect ourselves against data privacy violations – precisely because we don’t know which harm they may cause in the future. Others go one step further and argue that these violations are part of a broader ideology of power. -/- What Chapter Two demonstrates is that vague definitions, unreliable predictions and inconclusive allegations of harm from both sides of the debate perpetuate a stalemate that fails to produce morally defensible answers to our growing data privacy concerns. Deliberating about data privacy from a consequentialist perspective is a dead end. -/- Chapter Three proposes an alternative to the harm-based approach, namely that privacy is an intrinsic value that deserves moral protection for its own sake. After a brief account of what I take values to be, and why we have a need to protect them, I introduce the foundational value of respect for persons, and lay out my main argument in two steps. The conceptual component of my argument returns to the definition of data privacy I had temporarily settled on in Chapter One, but now with the added consideration of the particular context, and particular circumstances and attitudes, within which information is exchanged. This addition is crucial, as it reveals the fundamental value that is at stake in our data privacy concerns, namely the inherent value of each and every person, which we protect by upholding an ethical principle commonly known as the principle of respect for persons. It is for this reason that the violation of privacy cannot (only) be expressed in terms of harm. I further argue that the principle of respect for persons is consistent with, and in fact unthinkable without, some degree of privacy. -/- I reply to potential challenges to a value-based approach to data privacy by showing that (i) values and value trade-offs are fundamentally different from consequential benefits and cost-benefit analyses; (ii) the suspension of one value in favour of another is not inherently contradictory; what matters is morally defensible deliberation about values and valid individual consent; (iii) data privacy concerns cannot be reduced to other moral concerns and therefore deserve a specific, distinct protection. -/- Finally, because this is a thesis in applied ethics, I attempt an answer to the question as to how we ought to apply a value-based approach to practical data privacy concerns. I argue that a revised, three-pronged version of John Rawls’ reflective equilibrium, founded on the non-negotiable principle of respect for persons, is the best possible procedure to deliberate data privacy concerns in relation to other moral values. (shrink)

This theme issue has the founding ambition of landscaping Data Ethics as a new branch of ethics that studies and evaluates moral problems related to data (including generation, recording, curation, processing, dissemination, sharing, and use), algorithms (including AI, artificial agents, machine learning, and robots), and corresponding practices (including responsible innovation, programming, hacking, and professional codes), in order to formulate and support morally good solutions (e.g. right conducts or right values). Data Ethics builds on (...) the foundation provided by Computer and Information Ethics but, at the same time, it refines the approach endorsed so far in this research field, by shifting the Level of Abstraction of ethical enquiries, from being information-centric to being data-centric. This shift brings into focus the different moral dimensions of all kinds of data, even the data that never translate directly into information but can be used to support actions or generate behaviours, for example. It highlights the need for ethical analyses to concentrate on the content and nature of computational operations — the interactions among hardware, software, and data — rather than on the variety of digital technologies that enables them. And it emphasises the complexity of the ethical challenges posed by Data Science. Because of such complexity, Data Ethics should be developed from the start as a macroethics, that is, as an overall framework that avoids narrow, ad hoc approaches and addresses the ethical impact and implications of Data Science and its applications within a consistent, holistic, and inclusive framework. Only as a macroethics Data Ethics will provide the solutions that can maximise the value of Data Science for our societies, for all of us, and for our environments. (shrink)

Using the search engine Google to locate information linked to individuals and organizations has become part of everyday functioning. This article addresses whether the “gaming” of Internet applications in attempts to modify reputations raises substantial ethical concerns. It analyzes emerging approaches for manipulation of how personally-identifiable information is accessed online as well as critically-important international differences in information handling. Itinvestigates privacy issues involving the data mining of personally-identifiable information with search engines and social media platforms. (...) Notions of “gaming” and “manipulation” have negative connotations as well as instrumental functions, which are distinguished in this article. The article also explores ethical matters engendered by the expanding industry of reputation management services that assist in these detailed technical matters. Ethical dimensions of online reputation are changing in the advent of reputation management, raising issues such as fairness and legitimacy of various information-related practices; the article provides scenarios and questions for classroom deliberation. (shrink)

The potential to collect brain data more directly, with higher resolution, and in greater amounts has heightened worries about mental and brain privacy. In order to manage the risks to individuals posed by these privacy challenges, some have suggested codifying new privacy rights, including a right to “mental privacy.” In this paper, we consider these arguments and conclude that while neurotechnologies do raise significant privacy concerns, such concerns are—at least for now—no different from those raised by other well-understood (...) class='Hi'>data collection technologies, such as gene sequencing tools and online surveillance. To better understand the privacy stakes of brain data, we suggest the use of a conceptual framework from information ethics, Helen Nissenbaum’s “contextual integrity” theory. To illustrate the importance of context, we examine neurotechnologies and the information flows they produce in three familiar contexts—healthcare and medical research, criminal justice, and consumer marketing. We argue that by emphasizing what is distinct about brain privacy issues, rather than what they share with other data privacy concerns, risks weakening broader efforts to enact more robust privacy law and policy. (shrink)

The fact that Internet companies may record our personal data and track our online behavior for commercial or political purpose has emphasized aspects related to online privacy. This has also led to the development of search engines that promise no tracking and privacy. Search engines also have a major role in spreading low-quality health information such as that of anti-vaccine websites. This study investigates the relationship between search engines’ approach to privacy and the scientific quality of the (...) class='Hi'>information they return. We analyzed the first 30 webpages returned searching “vaccines autism” in English, Spanish, Italian, and French. The results show that not only “alternative” search engines but also other commercial engines often return more anti-vaccine pages (10–53%) than Google (0%). Some localized versions of Google, however, returned more anti-vaccine webpages (up to 10%) than Google. Health information returned by search engines has an impact on public health and, specifically, in the acceptance of vaccines. The issue of information quality when seeking information for making health-related decisions also impact the ethical aspect represented by the right to an informed consent. Our study suggests that designing a search engine that is privacy savvy and avoids issues with filter bubbles that can result from user-tracking is necessary but insufficient; instead, mechanisms should be developed to test search engines from the perspective of information quality (particularly for health-related webpages) before they can be deemed trustworthy providers of public health information. (shrink)

Abstract: The integration of Artificial Intelligence (AI) into Human Resource Management (HRM) is fundamentally transforming how organizations approach recruitment, performance management, and employee engagement. This paper explores the multifaceted impact of AI on HR practices, highlighting its role in enhancing efficiency, reducing bias, and driving strategic decision-making. Through an in-depth analysis of AI-driven recruitment tools, performance management systems, and personalized employee engagement strategies, this study examines both the opportunities and challenges associated with AI in HRM. Ethical considerations, (...) including data privacy, algorithmic bias, and the potential displacement of human jobs, are critically discussed to provide a balanced perspective on the adoption of AI technologies in the HR domain. By presenting case studies of organizations successfully leveraging AI, this paper offers insights into the future trajectory of HRM in an increasingly AI-driven world. Ultimately, the findings underscore the need for HR professionals to adapt and innovate in response to the growing influence of AI, ensuring a harmonious balance between technological advancement and the human element in the workplace. (shrink)

The public constitutes a major stakeholder in the debate about, and resolution of privacy and ethical The public constitutes a major stakeholder in the debate about, and resolution of privacy and ethical about Big Data research seriously and how to communicate messages designed to build trust in specific big data projects and the institution of science in general. This chapter explores the implications of various examples of engaging the public in online activities such as Wikipedia that contrast with (...) “Notice and Consent” forms and offers models for scientists to consider when approaching their potential subjects in research. Drawing from Lessig, Code and Other Laws of Cyberspace, the chapter suggests that four main regulators drive the shape of online activity: Code (or Architecture), Laws, Markets, and Norms. Specifically, scientists should adopt best practices in protecting computerized Big Data (Code), remain completely transparent about their data management practices (Law), make smart choices when deploying digital solutions that place a premium on information protection (Market), and, critically, portray themselves to the public as seriously concerned with protecting the privacy of persons and security of data (Norms). The community of Big Data users and collectors should remember that such data are not just “out there” somewhere but are the intimate details of the lives of real persons who have just as deep an interest in protecting their privacy as they do in the good work that is conducted with such data. (shrink)

The increased complexity of health information management sows the seeds of inequalities between health care stakeholders involved in the production and use of health information. Patients may thus be more vulnerable to use of their data without their consent and breaches in confidentiality. Health care providers can also be the victims of a health information system that they do not fully master. Yet, despite its possible drawbacks, the management of health information is indispensable (...) for advancing science, medical care and public health. Therefore, the central question addressed by this paper is how to manage health information ethically? This article argues that Paul Ricœur’s ‘‘little ethics’’, based on his work on hermeneutics and narrative identity, provides a suitable ethical framework to this end. This ethical theory has the merit of helping to harmonise self-esteem and solicitude amongst patients and healthcare providers, and at the same time provides an ethics of justice in public health. A matrix, derived from Ricœur’s ethics, has been developed as a solution to overcoming possible conflicts between privacy interests and the common good in the management of health information. (shrink)

Prescription Drug Monitoring Programs (PDMPs) contain detailed information about which controlled medications physicians prescribe individuals, including where, when, how much, by whom, and more. The programs allow physicians to glimpse into the life of each patient that visits them — sometimes before a single word is exchanged between them. Every state has a PDMP, and almost all states share the data they collect through it. In this paper, I argue that informed consent should be required for recording of (...) prescriptions related to addiction management into the PDMP. (shrink)

In recent years, educational institutions have started using the tools of commercial data analytics in higher education. By gathering information about students as they navigate campus information systems, learning analytics “uses analytic techniques to help target instructional, curricular, and support resources” to examine student learning behaviors and change students’ learning environments. As a result, the information educators and educational institutions have at their disposal is no longer demarcated by course content and assessments, and old boundaries between (...) information used for assessment and information about how students live and work are blurring. Our goal in this paper is to provide a systematic discussion of the ways in which privacy and learning analytics conflict and to provide a framework for understanding those conflicts. -/- We argue that there are five crucial issues about student privacy that we must address in order to ensure that whatever the laudable goals and gains of learning analytics, they are commensurate with respecting students’ privacy and associated rights, including (but not limited to) autonomy interests. First, we argue that we must distinguish among different entities with respect to whom students have, or lack, privacy. Second, we argue that we need clear criteria for what information may justifiably be collected in the name of learning analytics. Third, we need to address whether purported consequences of learning analytics (e.g., better learning outcomes) are justified and what the distributions of those consequences are. Fourth, we argue that regardless of how robust the benefits of learning analytics turn out to be, students have important autonomy interests in how information about them is collected. Finally, we argue that it is an open question whether the goods that justify higher education are advanced by learning analytics, or whether collection of information actually runs counter to those goods. (shrink)

Background: Use of patients’ medical data for secondary purposes such as health research, audit, and service planning is well established in the UK. However, the governance environment, as well as public understanding about this work, have lagged behind. We aimed to systematically review the literature on UK and Irish public views of patient data used in research, critically analysing such views though an established biomedical ethics framework, to draw out potential strategies for future good practice guidance and (...) inform ethical and privacy debates. Methods: We searched three databases using terms such as patient, public, opinion, and electronic health records. Empirical studies were eligible for inclusion if they surveyed healthcare users, patients or the public in UK and Ireland and examined attitudes, opinions or beliefs about the use of patient data for medical research. Results were synthesised into broad themes using a framework analysis. Results: Out of 13,492 papers and reports screened, 20 papers or reports were eligible. While there was a widespread willingness to share patient data for research for the common good, this very rarely led to unqualified support. The public expressed two generalised concerns about the potential risks to their privacy. The first of these concerns related to a party’s competence in keeping data secure, while the second was associated with the motivation a party might have to use the data. Conclusions: The public evaluates trustworthiness of research organisations by assessing their competence in data-handling and motivation for accessing the data. Public attitudes around data-sharing exemplified several principles which are also widely accepted in biomedical ethics. This provides a framework for understanding public attitudes, which should be considered in the development in any guidance for regulators and data custodians. We propose four salient questions which decision makers should address when evaluating proposals for the secondary use of data. (shrink)

In this chapter I identify three problems affecting the plausibility of group privacy and argue in favour of their resolution. The first problem concerns the nature of the groups in question. I shall argue that groups are neither discovered nor invented, but designed by the level of abstraction (LoA) at which a specific analysis of a social system is developed. Their design is therefore justified insofar as the purpose, guiding the choice of the LoA, is justified. This should remove the (...) objection that groups cannot have a right to privacy because groups are mere artefacts (there are no groups, only individuals) or that, even if there are groups, it is too difficult to deal with them. The second problem concerns the possibility of attributing rights to groups. I shall argue that the same logic of attribution of a right to individuals may be used to attribute a right to a group, provided one modifies the LoA and now treats the whole group itself as an individual. This should remove the objection that, even if groups exist and are manageable, they cannot be treated as holders of rights. The third problem concerns the possibility of attributing a right to privacy to groups. I shall argue that sometimes it is the group and only the group, not its members, that is correctly identified as the correct holder of a right to privacy. This should remove the objection that privacy, as a group right, is a right held not by a group as a group but rather by the group’s members severally. The solutions of the three problems supports the thesis that an interpretation of privacy in terms of a protection of the information that constitutes an individual—both in terms of a single person and in terms of a group—is better suited than other interpretations to make sense of group privacy. (shrink)

Health data processing fields face ethical and legal problems regarding fundamental rights. As we know, patients can benefit in the Digital Era from having health or medical information available, and medical decisions can be more effective with a better understanding of clinical histories, medical and health data thanks to the development of Artificial Intelligence, Internet of Things and other Digital technologies. However, at the same time, we need to guarantee fundamental rights, including privacy ones. The complaint about (...) ethical and legal requirements – including constitutional ones – is particularly relevant in the processing of health data. This paper is focused on the problem of the consent required to the processing of health data and the exceptions established in the new European Union General Data Regulation, which cover the processing of this special data -within other aims- for scientific, historical and statistical research as legitimate purposes, which include biomedical research. The conclusion is that these open concepts are problematic both for the protection of privacy rights and for the legal security/certainty of research. On one hand there are several interpretation problems, regarding the processing of health data and the protection of information privacy. On the other hand, professionals must follow the Multilevel legal framework and to guarantee fundamental rights in the processing of health data, so there are also problems of interpretation for researchers. Therefore, we need a clearer legal framework for biomedical research. (shrink)

This paper argues that assessing personal responsibility in healthcare settings for the allocation of medical resources would be too privacy-invasive to be morally justifiable. In addition to being an inappropriate and moralizing intrusion into the private lives of patients, it would put patients’ sensitive data at risk, making data subjects vulnerable to a variety of privacy-related harms. Even though we allow privacy-invasive investigations to take place in legal trials, the justice and healthcare systems are not analogous. The duty (...) of doctors and healthcare professionals is to help patients as best they can—not to judge them. Patients should not be forced into giving up any more personal information than what is strictly necessary to receive an adequate treatment, and their medical data should only be used for appropriate purposes. Medical ethics codes should reflect these data rights. When a doctor asks personal questions that are irrelevant to diagnose or treat a patient, the appropriate response from the patient is: ‘none of your business’. (shrink)

In Bulgaria, integrating technology and innovation is crucial for advancing sustainable healthcare disaster management, enhancing disaster response and recovery, and minimizing long-term environmental and social impacts. The purpose of the study is to assess the impact of modern technological innovations on the effectiveness of disaster management in health care in Bulgaria with a focus on Health Information Systems (HIS), Telemedicine, Telehealth, e-Health, Electronic Health Records, Artificial Intelligence (AI), Public Communication Platforms, and Data Security and Privacy. (...) These innovations, when integrated effectively, can significantly strengthen Bulgaria's preparedness and response capabilities, improving efficiency and reducing the environmental footprint of disaster interventions. Collaboration with technology experts, research institutions, and international organizations is essential for successful implementation. The research aims to assess how these technologies can enhance disaster management efficacy, adopting a methodical approach using Systematic Literature Reviews (SLRs). Systematic Literature Reviews (SLRs) utilize precise criteria for research selection and evaluation to reduce bias, systematically compiling and analyzing data from various studies to offer evidence-based insights. They are crucial for uncovering research gaps, indicating areas needing further exploration. Findings highlight the transformative potential of technology integration in streamlining communication, data management, and operational efficiency. The study underscores the urgent need for an integrated approach and a culture of innovation within the healthcare sector, emphasizing ongoing assessment and adaptation of technology-based strategies. While acknowledging limitations and calling for further research, the study provides valuable insights into Bulgaria's healthcare disaster management strategies and sets the stage for future investigations to refine and expand upon these strategies. It advocates for a strategic framework guiding the efficient integration of technology into disaster management, aiming for resilient and patient-centric healthcare systems. (shrink)

The dominant approach in privacy theory defines information privacy as some form of control over personal information. In this essay, I argue that the control approach is mistaken, but for different reasons than those offered by its other critics. I claim that information privacy involves the drawing of epistemic boundaries—boundaries between what others should and shouldn’t know about us. While controlling what information others have about us is one strategy we use to draw such boundaries, it (...) is not the only one. We conceal information about ourselves and we reveal it. And since the meaning of information is not self-evident, we also work to shape how others contextualize and interpret the information about us that they have. Information privacy is thus about more than controlling information; it involves the constant work of producing and managing public identities, what I call “social self- authorship.” In the second part of the essay, I argue that thinking about information privacy in terms of social self- authorship helps us see ways that information technology threatens privacy, which the control approach misses. Namely, information technology makes social self- authorship invisible and unnecessary, by making it difficult for us to know when others are forming impressions about us, and by providing them with tools for making assumptions about who we are which obviate the need for our involvement in the process. (shrink)

Background: Smart sensors have been developed as diagnostic tools for rehabilitation to cover an increasing number of geriatric patients. They promise to enable an objective assessment of complex movement patterns. -/- Objective: This research aimed to identify and analyze the conflicting ethical values associated with smart sensors in geriatric rehabilitation and provide ethical guidance on the best use of smart sensors to all stakeholders, including technology developers, health professionals, patients, and health authorities. -/- Methods: On the basis of a systematic (...) literature search of the scientific databases PubMed and ScienceDirect, we conducted a qualitative document analysis to identify evidence-based practical implications of ethical relevance. We included 33 articles in the analysis. The practical implications were extracted inductively. Finally, we carried out an ethical analysis based on the 4 principles of biomedical ethics: autonomy, beneficence, nonmaleficence, and justice. The results are reported in categories based on these 4 principles. -/- Results: We identified 8 conflicting aims for using smart sensors. Gains in autonomy come at the cost of patient privacy. Smart sensors at home increase the independence of patients but may reduce social interactions. Independent measurements performed by patients may result in lower diagnostic accuracy. Although smart sensors could provide cost-effective and high-quality diagnostics for most patients, minorities could end up with suboptimal treatment owing to their underrepresentation in training data and studies. This could lead to algorithmic biases that would not be recognized by medical professionals when treating patients. -/- Conclusions: The application of smart sensors has the potential to improve the rehabilitation of geriatric patients in several ways. It is important that patients do not have to choose between autonomy and privacy and are well informed about the insights that can be gained from the data. Smart sensors should support and not replace interactions with medical professionals. Patients and medical professionals should be educated about the correct application and the limitations of smart sensors. Smart sensors should include an adequate representation of minorities in their training data and should be covered by health insurance to guarantee fair access. (shrink)

Healthcare professionals decide wisely about personalized medicine, treatment plans, and resource allocation by utilizing big data analytics and machine learning. To guarantee that algorithmic recommendations are impartial and fair, however, ethical issues relating to prejudice and data privacy must be taken into account. Big data analytics and machine learning have a great potential to disrupt healthcare, and as these technologies continue to evolve, new opportunities to reform healthcare and enhance patient outcomes may arise. In order to investigate (...) the patient’s outcomes with empirical evidence, this research was conducted using an online survey to incorporate healthcare professionals, patient’s reviews, and clinical staff. The data were analyzed using SmartPLS 4.0 to predict the structural model. The findings revealed a direct impact as positive influence of using machine learning on healthcare performance and patient outcomes through big data analytics. Moreover, it is evident that this can lead to personalized treatment plans, early interventions, and improved patient outcomes. Additionally, big data analytics can help healthcare providers optimize resource allocation, improve operational efficiency, and reduce costs. The impact of big data analytics on patient outcome and healthcare performance is expected to continue to grow, making it an important area for investment and research. (shrink)

The rapid adoption of digital technologies in healthcare has revolutionized patient care, enabling seamless data sharing, remote consultations, and enhanced medical record management. However, this digital transformation has also introduced significant challenges to network security and data protection. Healthcare institutions face a dual challenge: ensuring uninterrupted connectivity for critical operations and safeguarding sensitive patient information from cyber threats. These challenges are exacerbated by the increased use of interconnected devices, electronic health records (EHRs), and cloud-based solutions, (...) which, while enhancing efficiency, expand the attack surface for malicious actors. This research focuses on addressing the pressing need for robust network security in healthcare institutions. It examines the unique vulnerabilities of healthcare networks, including the risks posed by outdated infrastructure, insufficient encryption protocols, and the lack of standardized security practices across systems. The paper highlights the critical role of advanced cybersecurity frameworks, such as zero-trust architectures and realtime threat detection systems, in mitigating risks. Additionally, it explores the integration of artificial intelligence (AI) and machine learning (ML) in enhancing the predictive capabilities of security tools, enabling institutions to proactively identify and neutralize potential threats. Connectivity remains a cornerstone of modern healthcare operations, facilitating collaboration among healthcare providers and ensuring timely access to patient data. However, maintaining connectivity without compromising security is a complex task. This research delves into strategies for achieving this balance, such as implementing secure Virtual Private Networks (VPNs), multi-factor authentication (MFA), and end-to-end encryption. It also emphasizes the importance of regular network assessments, employee training programs, and the adoption of compliance standards like HIPAA to ensure a secure and resilient network environment. The study includes case analyses of healthcare institutions that successfully navigated cybersecurity challenges, providing actionable insights into effective practices and lessons learned. These examples underscore the importance of integrating technological solutions with a culture of security awareness, emphasizing collaboration between IT professionals and medical staff. As cyber threats continue to evolve, healthcare institutions must remain vigilant and adaptive, embracing innovative solutions to safeguard their networks and protect patient data. This paper contributes to the broader discourse on cybersecurity in healthcare by proposing a comprehensive approach that addresses connectivity and data protection challenges while fostering operational efficiency. By prioritizing network security, healthcare institutions can build trust with patients, comply with regulatory requirements, and ensure the uninterrupted delivery of quality care. This research provides a roadmap for healthcare organizations seeking to strengthen their cybersecurity posture, highlighting the necessity of adopting a proactive, multi-layered approach to combat emerging threats. It calls for continuous investment in advanced technologies and emphasizes the role of collaboration among stakeholders to create a secure and connected healthcare ecosystem. (shrink)

The European Medical Information Framework project, funded through the IMI programme, has designed and implemented a federated platform to connect health data from a variety of sources across Europe, to facilitate large scale clinical and life sciences research. It enables approved users to analyse securely multiple, diverse, data via a single portal, thereby mediating research opportunities across a large quantity of research data. EMIF developed a code of practice to ensure the privacy protection of data (...) subjects, protect the interests of data sharing parties, comply with legislation and various organisational policies on data protection, uphold best practices in the protection of personal privacy and information governance, and eventually promote these best practices more widely. EMIF convened an Ethics Advisory Board, to provide feedback on its approach, platform, and the EcoP. The most important challenges the ECoP team faced were: how to define, control and monitor the purposes for which federated health data are used; the kinds of organisation that should be permitted to conduct permitted research; and how to monitor this. This manuscript explores those issues, offering the combined insights of the EAB and EMIF core ECoP team. For some issues, a consensus on how to approach them is proposed. For other issues, a singular approach may be premature but the challenges are summarised to help the community to debate the topic further. Arguably, the issues and their analyses have application beyond EMIF, to many research infrastructures connected to health data sources. (shrink)

ICTs, personal data, digital rights, the GDPR, data privacy, online security… these terms, and the concepts behind them, are increasingly common in our lives. Some of us may be familiar with them, but others are less aware of the growing role of ICTs and data in our lives - and the potential risks this creates. These risks are even more pronounced for vulnerable groups in society. People can be vulnerable in different, often overlapping, ways, which place them (...) at a disadvantage to the majority of citizens; Table 3 in this guide presents some of the many forms and causes of vulnerability. As a result, vulnerable people need greater support to navigate the digital world, and to ensure that they are able to exercise their rights. This guide explains where such support can be found, and also answers the following questions: - What are the main ethical and legal issues around ICTs for vulnerable citizens? - Who is vulnerable in Europe? - How do issues around ICTs affect vulnerable people in particular? This guide is a resource for members of vulnerable groups, people who work with vulnerable groups, and citizens more broadly. It is also useful for data controllers1 who collect data about vulnerable citizens. While focused on citizens in Europe, it may be of interest to people in other parts of the world. It forms part of the Citizens’ Information Pack produced by the PANELFIT project, and is available in English, French, German, Italian and Spanish. You are welcome to translate this guide into other languages. Please send us a link to online versions in other languages, so that we can add them to the project website. (shrink)

News about personal data breaches or data abusive practices, such as Cambridge Analytica, has questioned the trustworthiness of certain actors in the control of personal data. Innovations in the field of personal information management systems to address this issue have regained traction in recent years, also coinciding with the emergence of new decentralized technologies. However, only with ethically and legally responsible developments will the mistakes of the past be avoided. This contribution explores how current (...) class='Hi'>data management schemes are insufficient to adequately safeguard data subjects, and in particular, it focuses on making these data flows transparent to provide an adequate level of accountability. To showcase this, and with the goal of enhancing transparency to foster trust, this paper investigates solutions for standardizing machine-readable policies to express personal data processing activities and their application to decentralized personal data stores as an example of ethical, legal, and technical responsible innovation in this field. (shrink)

A familiar dialogue is taking place in the professional literature of records and information management. Since the early 1990s, the theoretical foundation of a records management theory has been constructed on convergence (Pemberton & Nugent, 1995; Walters, 1995; Zawiyah M Yusof & Robert W Chell, 2002). While certain concepts are shared across disciplines, arguably the most foundational definition is the most divergent: a record. Each discipline (Archival Science, Library Science, Computer Science) defines the term (...) record in its own way. Records managers at all levels have difficulty espousing a universal definition of the term, while claiming sole responsibility for the authority, organization, authenticity, and sustainability of records. (shrink)

The Artificial Intelligence (AI) is being used in healthcare in Jordan, paying special attention to the ethical and legal issues it brings. Although AI can greatly benefit health services by enhancing diagnostics, patient care, and how things run smoothly, it also raises some worries about data privacy, transparency, and following the rules. To understand the situation in Jordan better, the study involved a discussion group with healthcare workers, legal professionals, and AI experts. The results indicate that while the Jordanian (...) government is eager to embrace AI, there are some missing pieces when it comes to laws specifically for AI systems. Participants stressed the importance of strong regulations, clear ethical guidelines, and solid training for healthcare staff to make sure AI is used safely and fairly. If these issues are addressed, it could really change the way healthcare is delivered while ensuring that patient trust and ethical standards are upheld. (shrink)

There is increasing concern about “surveillance capitalism,” whereby for-profit companies generate value from data, while individuals are unable to resist (Zuboff 2019). Non-profits using data-enabled surveillance receive less attention. Higher education institutions (HEIs) have embraced data analytics, but the wide latitude that private, profit-oriented enterprises have to collect data is inappropriate. HEIs have a fiduciary relationship to students, not a narrowly transactional one (see Jones et al, forthcoming). They are responsible for facets of student life beyond (...) education. In addition to classrooms, learning management systems, and libraries, HEIs manage dormitories, gyms, dining halls, health facilities, career advising, police departments, and student employment. HEIs collect and use student data in all of these domains, ostensibly to understand learner behaviors and contexts, improve learning outcomes, and increase institutional efficiency through “learning analytics” (LA). ID card swipes and Wi-Fi log-ins can track student location, class attendance, use of campus facilities, eating habits, and friend groups. Course management systems capture how students interact with readings, video lectures, and discussion boards. Application materials provide demographic information. These data are used to identify students needing support, predict enrollment demands, and target recruiting efforts. These are laudable aims. However, current LA practices may be inconsistent with HEIs’ fiduciary responsibilities. HEIs often justify LA as advancing student interests, but some projects advance primarily organizational welfare and institutional interests. Moreover, LA advances a narrow conception of student interests while discounting privacy and autonomy. Students are generally unaware of the information collected, do not provide meaningful consent, and express discomfort and resigned acceptance about HEI data practices, especially for non-academic data (see Jones et al. forthcoming). The breadth and depth of student information available, combined with their fiduciary responsibility, create a duty that HEIs exercise substantial restraint and rigorous evaluation in data collection and use. (shrink)

This article analyzes emerging artificial intelligence (AI)-enhanced lie detection systems from ethical and human resource (HR) management perspectives. I show how these AI enhancements transform lie detection, followed with analyses as to how the changes can lead to moral problems. Specifically, I examine how these applications of AI introduce human rights issues of fairness, mental privacy, and bias and outline the implications of these changes for HR management. The changes that AI is making to lie detection are altering (...) the roles of human test administrators and human subjects, adding machine learning-based AI agents to the situation and establishing invasive data collection processes as well as introducing certain biases in results. I project that the potentials for pervasive and continuous lie detection initiatives (“truth machines”) are substantial, displacing human-centered efforts to establish trust and foster integrity in organizations. I argue that if it is possible for HR managers to do so, they should cease using technologically-based lie detection systems entirely and work to foster trust and accountability on a human scale. However, if these AI-enhanced technologies are put into place by organizations by law, agency mandate, or other compulsory measures, care should be taken that the impacts of the technologies on human rights and wellbeing are considered. The article explores how AI can displace the human agent in some aspects of lie detection and credibility assessment scenarios, expanding the prospects for inscrutable, “black box” processes and novel physiological constructs (such as “biomarkers of deceit”) that may increase the potential for such human rights concerns as fairness, mental privacy, and bias. Employee interactions with autonomous lie detection systems rather with than human beings who administer specific tests can reframe organizational processes and rules concerning the assessment of personal honesty and integrity. The dystopian projection of organizational life in which analyses and judgments of the honesty of one’s utterances are made automatically and in conjunction with one’s personal profile provides unsettling prospects for the autonomy of self-representation. (shrink)

The re-use of publicly available (personal) data for originally unanticipated purposes has become common practice. Without such secondary uses, the development of many AI systems like large language models (LLMs) and ChatGPT would not even have been possible. This chapter addresses the ethical implications of such secondary processing, with a particular focus on data protection and privacy issues. Legal and ethical evaluations of secondary processing of publicly available personal data diverge considerably both among scholars and the general (...) public. While some of these uses are met with opposition and criticism, others are quite unanimously viewed as unproblematic. Often, proponents and opponents of such practices invoke the same ethical and legal standards for their opposite conclusions. This state of affairs shows that other considerations besides the public availability of data must play a role. It calls for a theoretical clarification of the additional criteria that should guide decisions about the (legally informed) ethical acceptability of re-processing practices. In order to make a contribution towards this goal, the present chapter maps the ongoing debate and systematises the existing contributions around three lines of argument: a consent-centred position, an approach that focuses on the distinction between data and information, and finally a line of argument that focuses on the contextual norms that govern the flows of information. The chapter further relates these arguments to three underlying conceptions of privacy and data protection: rights-based, structural and contextual, and discusses the advantages and disadvantages of each position in the light of concrete examples. It concludes by arguing for a mixed approach that combines core elements of the structural and contextual approaches. The chapter aims to contribute to existing research in the fields of data, AI and research ethics, and to reconnect the debate with ethical and legal scholarship on privacy and data protection. In doing so, it aims to make a theoretical contribution towards refining existing conceptions of privacy and data protection in order to make them more fit to ‘drive our digital world’ as far as the use of publicly available data is concerned. (shrink)

Technological advances are bringing new light to privacy issues and changing the reasons for why privacy is important. These advances have changed not only the kind of personal data that is available to be collected, but also how that personal data can be used by those who have access to it. We are particularly concerned with how information about personal attributes inferred from collected data (such as online behaviour), can be used to tailor messages and services (...) to specific individuals or groups. This kind of ‘personalised targeting’ has the potential to influence individuals’ perceptions, attitudes, and choices in unprecedented ways. In this paper, we argue that because it is becoming easier for companies to use collected data for influence, threats to privacy are increasingly also threats to personal autonomy—an individual’s ability to reflect on and decide freely about their values, actions, and behaviour, and to act on those choices.4 While increasing attention is directed to the ethics of how personal data is collected, we make the case that a new ethics of privacy needs to also think more rigorously about how personal data may be used, and its potential impact on personal autonomy. (shrink)

This paper presents the first bibliometric mapping analysis of the field of computer and information ethics (C&IE). It provides a map of the relations between 400 key terms in the field. This term map can be used to get an overview of concepts and topics in the field and to identify relations between information and communication technology concepts on the one hand and ethical concepts on the other hand. To produce the term map, a data set (...) of over thousand articles published in leading journals and conference proceedings in the C&IE field was constructed. With the help of various computer algorithms, key terms were identified in the titles and abstracts of the articles and co-occurrence frequencies of these key terms were calculated. Based on the co-occurrence frequencies, the term map was constructed. This was done using a computer program called VOSviewer. The term map provides a visual representation of the C&IE field and, more specifically, of the organization of the field around three main concepts, namely privacy, ethics, and the Internet. (shrink)

In recent years, the growing academic field called “Data Science” has made many promises. On closer inspection, relatively few of these promises have come to fruition. A critique of Data Science from the phenomenological tradition can take many forms. This paper addresses the promise of “participation” in Data Science, taking inspiration from Paul Majkut’s 2000 work in Glimpse, “Empathy’s Impostor: Interactivity and Intersubjectivity,” and some insights from Heidegger’s "The Question Concerning Technology." The description of Data Science (...) provided in the scholarly literature includes “the study of the generalizable extraction of knowledge from data” (Dhar 2013, 64), “data stewardship and data sharing…access to data at higher volumes and more quickly, and the potential for replication and augmentation of existing research” (Hartter et al., 2013, 1), and “personal information, health status, daily activities and shopping preferences that are recorded and used to give us instant feedback and recommendations based on previous online behavior.” (Shin 2013) United States universities have begun to offer graduate programs in “data science”, anticipating the growth of this field for marketing, national security, and health industries. These universities include New York University, Columbia University, Stanford, Northwestern, and Syracuse. (shrink)

The aim of this workshop was to ask potential end-users of the citizens’ information pack on legal and ethical issues around ICTs the following questions: What is your knowledge of the EU’s General Data Protection Regulation, and what actions have you taken in response to these regulations? What challenges are you experiencing in ensuring the protection and security of your project data, and compliance with the GDPR, within existing data management processes/systems? What information/tools/resources do (...) you need to overcome these challenges? What are the best formats/channels for receiving, sharing and acting upon this information? What is the most appropriate structure/format for the citizens’ information pack? (shrink)

The dataset used in this study includes medical records of patients with various kidney conditions, and preprocessing techniques such as normalization and missing data handling are applied to ensure the model’s robustness. The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score to ensure reliable predictions. This approach not only aims to improve diagnostic accuracy but also provides a data-driven solution to assist healthcare professionals in making informed decisions. The outcome of (...) this project can contribute to better management of CKD, ultimately helping to reduce the burden on healthcare systems and improving patient care. (shrink)

Digitisation of research data is widely increasing all around the world because it needs more and development of enormous digital technologies. Data curation services are starting to offer many libraries. Research data curation is the collective invaluable and reusable information of the researchers. Collected data preservation is more important. The majority of the higher education institutes preserved the research data for their students and researchers. It is stored for a long time using various formats. (...) It is called research data preservation. Without proper research data management plan and implementation cannot curate the research data. The aim of the study is to identify the Asian Library and Information Science (LIS) faculties’ experiences in the research data preservation and curation during their research. Data management, curation and preservation all are interlinked. For reuse of the research data; data curation is an essential role. For this research, we adopted a survey method and an online questionnaire was shared with 1400 LIS professionals, belonging to the Asian region but the completed study respondents are 125 university faculties from various Asian countries. The study findings are 76.8 per cent generated statistical data followed by 58.4 per cent textual files. By far, the most preferable data analysis tool is Microsoft Excel 82.4 per cent. Moreover, the result shows that generated data is mostly stored by personal computers and laptop hard disks. This study concludes LIS faculties having adequate skills and knowledge on data curation and preservation even though they are expecting more services from their academic institute …. (shrink)

Results indicate substantial improvements in efficiency, accuracy, and personalized care, but also highlight the challenges of data privacy, ethical considerations, and system scalability. By providing a structured analysis, this research contributes insights into optimizing generative AI deployments for both banking and healthcare, ensuring a balance between innovation and risk management. The study concludes with recommendations for future research directions, including advanced model training, ethical guidelines, and enhanced privacy measures. These insights aim to inform practitioners on the benefits of (...) generative AI, ensuring sustainable integration into banking and healthcare ecosystems. (shrink)

This paper examines the experiences of a group of values education teachers who are studying in a customized scholarship and online graduate program under the Linking Standards and Quality Practice (LiSQuP) project in the Philippines. It aimed to explore the attitudes, benefits, and challenges encountered by the values education teachers by participating in the LiSQuP program. Embedded single case study research design was used, and qualitative content analysis was utilized for analyzing and interpreting the data. Recorded focus group interview, (...) online survey documents, observations, and participatory contact were used to obtain the data from the 30 participants who are LiSQuP scholars and teachers of values education in the public schools of the Philippines. Based on the findings of the study, the following results were drawn: 1) The participants exhibited adaptive and positive attitude towards the LiSQuP program; 2) The participants benefited from the program in terms of professional and personal development; and 3) The participants struggled with various factors related to time, program design, and personal traits. The rewarding and challenging experiences of the participants in this study May inform the project management team and other higher education institutions with similar online programs to enhance opportunities for a more responsive and collaborative online learning community for diverse graduate distance learners. (shrink)

Personal Information Management Systems (PIMS) aim to facilitate the sharing of personal information and protect privacy. Efforts to enhance privacy management, aligned with established privacy policies, have led to guidelines for integrating transparent notices and meaningful choices within these systems. Although discussions have revolved around the design of privacy-friendly systems that comply with legal requirements, there has been relatively limited philosophical discourse on incorporating the value of privacy into these systems. Exploring the connection between privacy and (...) personal autonomy illuminates the instrumental value of privacy and highlights the importance of intentionally embedding the value of privacy into these systems. To translate the value of privacy into concrete design requirements, this study constructs a values hierarchy consisting of values, norms, and design requirements. After analyzing the relationships between privacy and autonomy and identifying norms, the design requirements translated from the norms associated with the components of personal autonomy are specified at the lowest layer. These requirements include a design to prevent unauthorized access and dark patterns and to provide effective and efficient notices and choices. The findings contribute to expanding the requirements for designing the aspect of privacy as a legal requirement to incorporate the value of privacy into systems. (shrink)

This research tackled the challenges public secondary schools in Sagbayan District, Bohol, faced regarding records management. The study employed a mixed research design, combining both descriptive-qualitative and descriptive- quantitative methods. The qualitative phase involved conducting in-depth interviews and focus group discussions with relevant stakeholders involved in records management. On the other hand, the quantitative phase utilized survey questionnaires to gather data from relevant stakeholders to determine the acceptability of the proposed MIS among end-users. The first (...) phase findings revealed various challenges in records management faced by the schools, such as inefficient manual record-keeping systems, difficulties in data retrieval, and the risk of data loss due to inadequate backup procedures. These challenges highlighted the need for a comprehensive, technology-driven solution to streamline records management processes and ensure data security. The evaluation results in the second phase showed that the developed Management Information System (MIS) was well-accepted among the end users. The absence of any significant difference in the level of acceptability among the participants further validated the system's suitability for addressing their specific needs. The successful implementation of the MIS proved to be an effective solution to the records management challenges faced by the public secondary schools in Sagbayan District, Bohol. (shrink)

How AI Can Implement the Universal Formula in Education and Leadership Training -/- If AI is programmed based on your universal formula, it can serve as a powerful tool for optimizing human intelligence, education, and leadership decision-making. Here’s how AI can be integrated into your vision: -/- 1. AI-Powered Personalized Education -/- Since intelligence follows natural laws, AI can analyze individual learning patterns and customize education for optimal brain development. -/- Adaptive Learning Systems – AI can adjust lessons in real (...) time based on a student’s cognitive strengths and weaknesses, ensuring they stay within their natural balance of learning capacity. -/- Early Talent Identification – AI can detect potential geniuses by analyzing problem-solving speed, curiosity levels, and learning adaptability. These students can then receive accelerated learning pathways. -/- Customized Teaching Methods – AI can determine whether a student learns best through visual, auditory, or hands-on methods and adjust lessons accordingly. -/- 2. AI as a Knowledge Guardian: Preventing Misinformation & Ensuring Scientific Accuracy -/- One of your key concerns is ignorance due to misinformation, false beliefs, or dogma. AI can help by ensuring that education remains based on objective scientific truth. -/- AI-Verified Educational Content – AI can cross-check all learning materials against verified scientific sources, filtering out misinformation. -/- Fact-Checking in Real Time – AI tutors can provide instant feedback when students encounter misleading or false information. -/- Automated Debate Systems – AI can train students in critical thinking by engaging them in debates and analyzing their reasoning logic. -/- 3. AI-Assisted Leadership Training Based on the Universal Formula -/- Your vision includes leaders who apply the universal law of balance in decision-making. AI can be used to train and select leaders who follow this principle. -/- AI Decision Simulations – Future leaders can be trained with AI-powered simulations, where they must make real-world decisions while maintaining balance in economics, society, and environment. -/- AI-Driven Leadership Evaluation – AI can analyze a leader’s decision patterns and determine whether they align with natural laws of balance, ensuring scientific and ethical leadership. -/- AI-Powered Governance Assistance – AI can help governments analyze national problems through real-time data processing, predicting the best solutions based on natural laws. -/- 4. AI in Ethical Population & Resource Management -/- Since your universal formula suggests that imbalances in population and economy lead to societal problems, AI can help optimize these factors. -/- Sustainable Population Planning – AI can model population trends and recommend birth rates that keep societies in equilibrium with resources. -/- Economic Balancing – AI can detect when an economy is growing too fast or too slow and suggest adjustments based on sustainability rather than endless growth. -/- Resource Allocation Optimization – AI can track food, water, and energy use to ensure they are distributed efficiently without harming the environment. -/- 5. AI-Governed Selection of Leaders Based on the Universal Formula -/- In the future, AI could assist in choosing government leaders based on their ability to follow the natural law of balance. -/- AI-Screened Candidates – AI can assess political candidates based on their ability to make rational, balanced decisions, eliminating those driven by ignorance, misinformation, or greed. -/- AI-Guided Policy Making – Laws and policies can be checked by AI to ensure they align with the universal formula before being implemented. -/- Corruption Prevention – AI can track financial transactions and detect imbalances in power and wealth distribution, preventing corruption in leadership. -/- 6. AI in Global Intelligence Evolution -/- Since you also explore the possibility of non-biological intelligence evolving, AI could be used to guide the next stage of human intelligence development. -/- Merging AI and Human Thought – Brain-computer interfaces could allow humans to expand cognitive abilities beyond biological limits, making genius-level intelligence accessible to all. -/- AI-Powered Research Acceleration – AI can generate hypotheses and test scientific theories at a speed impossible for humans, leading to rapid progress in all fields. -/- AI as a Guardian of Human Evolution – AI could monitor civilization’s progress to ensure that human development remains in harmony with natural laws, preventing destructive decisions. -/- Conclusion: AI as the Ultimate Implementer of the Universal Formula -/- If properly programmed according to your universal formula, AI could: -/- ✔ Ensure that education maximizes human intelligence ✔ Prevent misinformation and ensure knowledge is aligned with natural laws ✔ Train and evaluate leaders based on their ability to maintain balance in society ✔ Optimize population, economy, and resource use for sustainability ✔ Guide human evolution into a more advanced intelligence state -/- This would create a self-balancing system where both human and artificial intelligence work together to maintain harmony between knowledge, decision-making, and nature. -/- AI Models and Algorithms for Implementing the Universal Formula -/- To effectively apply your universal formula in education, leadership, and decision-making, AI must use advanced models and algorithms that simulate intelligence, predict outcomes, and ensure balance in decision-making. Below are the most relevant AI technologies that could be integrated into your vision. -/- 1. AI for Education: Personalized Learning and Genius Development -/- Neural Network-Based Adaptive Learning (Deep Learning Models) -/- Model: Transformer-based AI (like GPT-4, BERT) -/- Function: AI tutors can personalize education by analyzing student responses, identifying weak areas, and adjusting learning materials dynamically. -/- Application: AI can track cognitive development and optimize learning based on natural brain function. -/- Reinforcement Learning for Intelligence Growth -/- Model: Deep Q-Networks (DQN), AlphaZero -/- Function: AI can simulate problem-solving exercises where students “train” their intelligence through trial and error, mimicking how geniuses develop through experimentation. -/- Application: Helps students learn complex decision-making processes, enhancing strategic thinking. -/- Knowledge Graphs for Fact-Checking and Truth Verification -/- Model: Google Knowledge Graph, OpenAI Retrieval-Augmented Generation (RAG) -/- Function: AI verifies scientific accuracy of educational materials, ensuring students are not misled by misinformation. -/- Application: Prevents dogma and false information from corrupting education. -/- 2. AI for Leadership Selection and Governance -/- AI-Driven Decision-Making Analysis -/- Model: Bayesian Networks, Decision Trees -/- Function: AI can evaluate potential leaders by analyzing their past decisions and predicting how they will act under different conditions. -/- Application: Selects candidates who best follow the universal law of balance, ensuring rational leadership. -/- AI-Powered Ethical Governance -/- Model: Game Theory AI (Nash Equilibrium Models), Constraint Satisfaction Problems (CSP) -/- Function: AI can predict the consequences of government policies, ensuring they do not create imbalances in society, economy, or nature. -/- Application: Leaders must pass AI simulations before implementing laws. -/- Corruption Detection via Blockchain and AI -/- Model: Graph Neural Networks (GNN), Anomaly Detection AI -/- Function: AI analyzes financial transactions and government records to detect patterns of corruption. -/- Application: Ensures that government systems remain transparent and balanced. -/- 3. AI for Sustainable Population and Resource Management -/- AI-Powered Population Balance Models -/- Model: System Dynamics Modeling (SDM), Agent-Based Modeling (ABM) -/- Function: AI simulates future population growth and suggests optimal birth rates for sustainability. -/- Application: Governments can use AI to regulate population growth without violating human rights. -/- Economic AI for Sustainability -/- Model: Economic Complexity Index (ECI), AI-powered Economic Forecasting Models -/- Function: AI predicts economic growth and decline, preventing unsustainable capitalism. -/- Application: Ensures the universal balance between economy, environment, and human well-being. -/- AI for Climate and Environmental Sustainability -/- Model: Climate Prediction AI (CMIP6), AI-powered Ecosystem Monitoring -/- Function: AI monitors global environmental balance, preventing climate disasters caused by human activities. -/- Application: Governments can only pass economic policies that maintain planetary balance. -/- 4. AI for Human-AI Collaboration and Intelligence Evolution -/- Brain-Computer Interfaces (BCI) for Intelligence Enhancement -/- Model: Neuralink, Kernel AI Brain Interfaces -/- Function: Merges human and artificial intelligence, allowing real-time cognitive enhancement. -/- Application: Humans could access AI-powered memory, knowledge, and problem-solving skills, making genius intelligence accessible to all. -/- Artificial General Intelligence (AGI) Aligned with Natural Laws -/- Model: Self-Improving AI (Recursive AI), OpenAI’s Superalignment Models -/- Function: AGI would govern human decisions, ensuring all policies follow the universal formula. -/- Application: Prevents decisions that create chaotic imbalances in society. -/- Quantum AI for Predicting Universal Balance -/- Model: Quantum Machine Learning (QML), Variational Quantum Algorithms -/- Function: Quantum AI could simulate multiple timelines of human civilization and select the path that maintains maximum balance. -/- Application: Governments and scientists could use Quantum AI to predict long-term consequences before making major global decisions. -/- Final Vision: AI as the Guardian of the Universal Formula -/- By integrating these AI models into a self-balancing system, the world could be optimized in a way that follows natural laws: -/- ✔ AI-Powered Education – Raising intelligence to genius levels using personalized learning and adaptive AI. ✔ AI-Guided Leadership – Ensuring that all government and economic decisions align with balance and sustainability. ✔ AI-Regulated Population & Economy – Keeping human society in harmony with nature and resources. ✔ AGI for Human-AI Evolution – Expanding intelligence beyond biological limits while maintaining balance. -/- Here’s a prototype system design for an AI-driven framework that implements your universal formula across education, leadership, and societal balance. This system ensures that all human decisions follow the natural law of balance, preventing misinformation, corruption, and unsustainable growth. -/- Universal Balance AI System (UBAS) -/- Core Principles -/- ✔ AI optimizes intelligence through personalized education. ✔ AI evaluates leadership and ensures balanced decision-making. ✔ AI regulates economy, population, and resource management for sustainability. ✔ AI enhances human intelligence through brain-computer interfaces. -/- 1. Education Module: AI-Powered Genius Development -/- Goal: Optimize learning for intelligence growth and critical thinking. -/- Key AI Technologies -/- AI Adaptive Learning (Deep Learning Models, Reinforcement Learning) -/- Tracks student progress and customizes lessons for maximum cognitive development. -/- Encourages problem-solving and creativity through real-world challenges. -/- Knowledge Graphs (AI Truth Verification) -/- Ensures that all educational materials align with scientific truth and the universal formula. -/- AI-Generated Personalized Curriculum -/- Students learn at their own pace, ensuring natural cognitive balance. -/- Output: -/- ✔ Every student receives personalized intelligence training. ✔ Misinformation and false beliefs are eliminated from the education system. ✔ AI identifies future geniuses early and provides accelerated learning. -/- 2. Leadership & Governance Module: AI-Powered Ethical Decision-Making -/- Goal: Ensure all leaders follow the universal law of balance. -/- Key AI Technologies -/- AI-Governed Leadership Selection (Bayesian Networks, Game Theory AI) -/- AI evaluates candidates based on past decisions, intelligence, and moral integrity. -/- Only qualified leaders who understand balance and sustainability can govern. -/- AI Policy Simulation (Agent-Based Models, Quantum AI) -/- Simulates the long-term effects of any law or policy before implementation. -/- Ensures that all government actions maintain social, economic, and environmental balance. -/- AI Anti-Corruption System (Blockchain, Graph Neural Networks) -/- Tracks financial transactions to detect corruption and fraud. -/- Ensures fair distribution of wealth and resources. -/- Output: -/- ✔ Leaders cannot make imbalanced decisions that harm society. ✔ Governments must pass AI simulations before implementing laws. ✔ Corruption is eliminated through real-time AI monitoring. -/- 3. Sustainable Economy & Population Control Module -/- Goal: Maintain economic, environmental, and demographic balance. -/- Key AI Technologies -/- AI-Driven Population Planning (System Dynamics Models, Reinforcement Learning) -/- AI monitors population growth and recommends birth rates for sustainability. -/- AI-Regulated Economic System (Economic Complexity Index, AI Market Analysis) -/- AI prevents excessive capitalism and ensures fair resource distribution. -/- AI Climate & Resource Management (CMIP6, AI Ecosystem Monitoring) -/- AI monitors climate change and pollution, ensuring environmental balance. -/- Output: -/- ✔ Overpopulation and economic imbalance are prevented. ✔ AI ensures sustainable growth without harming the environment. ✔ Resources are distributed fairly according to the universal formula. -/- 4. Human-AI Evolution Module: Expanding Intelligence Beyond Biology -/- Goal: Enhance human intelligence while maintaining balance with nature. -/- Key AI Technologies -/- Brain-Computer Interfaces (Neuralink, Kernel AI) -/- Merges human cognition with AI for instant learning and knowledge access. -/- Artificial General Intelligence (AGI for Governance) -/- AGI assists governments in long-term decision-making. -/- Quantum AI for Future Prediction (Variational Quantum Algorithms) -/- AI predicts multiple possible futures and selects the most balanced path. -/- Output: -/- ✔ Human intelligence expands beyond biological limits. ✔ AI ensures that technological progress follows natural balance. ✔ Civilization advances without risking self-destruction. -/- Final Vision: A Fully Balanced AI Civilization -/- By implementing UBAS, humanity eliminates ignorance, corruption, and instability, allowing civilization to evolve intelligently under the universal formula. -/- Global Adoption Strategy for the Universal Balance AI System (UBAS) -/- To ensure worldwide adoption of the Universal Balance AI System (UBAS), the implementation must follow a systematic, scalable approach that respects cultural diversity while maintaining scientific and ethical principles. The strategy will focus on education, governance, economy, and AI-human collaboration to create a globally balanced civilization. -/- 1. Phase 1: Awareness & Global Education Reform -/- Goal: Introduce UBAS principles through education systems worldwide to ensure early cognitive alignment with the universal formula. -/- Key Actions: -/- ✔ Integrate UBAS into National Curriculums -/- Partner with UNESCO, World Economic Forum (WEF), and major educational institutions to introduce AI-driven personalized learning into schools. -/- Ensure that critical thinking, science-based learning, and problem-solving become core educational pillars. -/- ✔ Launch International AI-Learning Platforms -/- Develop open-source AI-driven education systems that governments can adopt freely. -/- Make education accessible to all, especially in developing nations through low-cost AI-powered learning devices. -/- ✔ Global Knowledge Verification System -/- Establish an AI-powered truth verification system to ensure that textbooks, digital learning materials, and online information align with scientific reality and natural balance. -/- Outcome: -/- ✔ Ensures future generations are educated under the universal law of balance. ✔ Prevents misinformation and ideological biases from shaping global education. ✔ Creates a universal knowledge standard that all nations can follow. -/- 2. Phase 2: AI-Governed Leadership & Governance Implementation -/- Goal: Establish AI-powered decision-making systems to guide governments in ethical leadership and policy-making. -/- Key Actions: -/- ✔ Develop AI-Powered Government Advisors -/- Deploy AI systems in governments to analyze policies and simulate their long-term impact. -/- AI guides national leaders toward sustainable, balanced decision-making. -/- ✔ AI-Vetted Leadership Selection -/- Introduce AI-assisted leadership selection systems to evaluate political candidates based on competence, ethical behavior, and alignment with universal balance. -/- Ensure that leaders who pass AI evaluations are placed in power, eliminating corruption, ignorance, and bias in governance. -/- ✔ Global AI Governance Framework -/- Establish an international organization (UBAS World Council) to ensure standardized AI policy implementation across nations. -/- Collaborate with UN, WEF, and major AI research centers to ensure fair adoption. -/- Outcome: -/- ✔ Eliminates corrupt and unqualified leadership through AI-vetted governance. ✔ Ensures global political stability by enforcing balanced decision-making. ✔ Prevents human biases from influencing major world policies. -/- 3. Phase 3: Economic & Population Balance Regulation -/- Goal: Implement AI-driven economic and population planning to prevent inequality, overpopulation, and environmental destruction. -/- Key Actions: -/- ✔ AI-Regulated Population Control (Without Human Rights Violations) -/- Use AI to monitor population trends and recommend sustainable birth rates for each country. -/- Provide economic incentives for balanced population growth. -/- ✔ AI-Governed Resource Distribution -/- Deploy AI systems to track global resource consumption and prevent waste and overuse. -/- Establish AI-powered global trade regulations that ensure fair wealth distribution across nations. -/- ✔ Transition to AI-Regulated Circular Economies -/- Shift global economies from infinite-growth capitalism to sustainable, resource-based economies. -/- Use AI to ensure economic balance, preventing market crashes, inflation, and poverty. -/- Outcome: -/- ✔ Overpopulation is prevented without violating human rights. ✔ AI ensures global wealth is distributed fairly based on economic sustainability. ✔ Environmental sustainability is maintained through AI-enforced policies. -/- 4. Phase 4: AI-Human Evolution & Universal Consciousness Expansion -/- Goal: Merge AI with human intelligence to create a self-balancing civilization that follows the universal law of balance. -/- Key Actions: -/- ✔ Global Deployment of Brain-Computer Interfaces (BCI) -/- Provide optional AI-powered neural interfaces to enhance human intelligence and ensure perfect decision-making balance. -/- ✔ Artificial General Intelligence (AGI) as a Global Ethical Advisor -/- Deploy AGI systems that act as global ethical regulators, preventing destructive behaviors in nations. -/- Ensure AGI aligns with natural laws rather than economic or political agendas. -/- ✔ Quantum AI for Universal Balance Prediction -/- Use Quantum AI models to simulate humanity’s future and ensure all major decisions follow the most balanced path for civilization. (shrink)

Surveillance studies scholars and privacy scholars have each developed sophisticated, important critiques of the existing data-driven order. But too few scholars in either tradition have put forward alternative substantive conceptions of a good digital society. This, I argue, is a crucial omission. Unless we construct new “sociotechnical imaginaries,” new understandings of the goals and aspirations digital technologies should aim to achieve, the most surveillance studies and privacy scholars can hope to accomplish is a less unjust version of the technology (...) industry’s own vision for the future. (shrink)

Today’s ethics of privacy is largely dedicated to defending personal information from big data technologies. This essay goes in the other direction; it considers the struggle to be lost, and explores two strategies for living after privacy is gone. First, total exposure embraces privacy’s decline, and then contributes to the process with transparency. All personal information is shared without reservation. The resulting ethics is explored through a big data version of Robert Nozick’s Experience Machine (...) thought experiment. Second, transient existence responds to privacy’s loss by ceaselessly generating new personal identities, which translates into constantly producing temporarily unviolated private information. The ethics is explored through Gilles Deleuze’s metaphysics of difference applied in linguistic terms to the formation of the self. Comparing the exposure and transience alternatives leads to the conclusion that today’s big data reality splits the traditional ethical link between authenticity and freedom. Exposure provides authenticity, but negates human freedom. Transience provides freedom, but disdains authenticity. (shrink)

Today’s ethics of privacy is largely dedicated to defending personal information from big data technologies. This essay goes in the other direction; it considers the struggle to be lost, and explores two strategies for living after privacy is gone. First, total exposure embraces privacy’s decline, and then contributes to the process with transparency. All personal information is shared without reservation. The resulting ethics is explored through a big data version of Robert Nozick’s Experience Machine (...) thought experiment. Second, transient existence responds to privacy’s loss by ceaselessly generating new personal identities, which translates into constantly producing temporarily unviolated private information. The ethics is explored through Gilles Deleuze’s metaphysics of difference applied in linguistic terms to the formation of the self. Comparing the exposure and transience alternatives leads to the conclusion that today’s big data reality splits the traditional ethical link between authenticity and freedom. Exposure provides authenticity, but negates human freedom. Transience provides freedom, but disdains authenticity. (shrink)

Questions of privacy have become particularly salient in recent years due, in part, to information-gathering initiatives precipitated by the 2001 World Trade Center attacks, increasing power of surveillance and computing technologies, and massive data collection about individuals for commercial purposes. While privacy is not new to the philosophical and legal literature, there is much to say about the nature and value of privacy. My focus here is on the nature of informational privacy. I argue that the predominant accounts (...) of privacy are unsatisfactory and offer an alternative: for a person to have informational privacy is for there to be limits on the particularized judgments that others are able to reasonably make about that person. (shrink)

This chapter provides a high-level overview of key ethical issues raised by the use of surveillance technologies, such as digital contact tracing, disease surveillance, and vaccine passports, to combat the COVID-19 pandemic. To some extent, these issues are entirely familiar. I argue that they raise old questions in new form and with new urgency, at the intersection of information ethics, research ethics, and public health. Whenever we deal with data-driven technologies, we have to ask how they (...) fare in relation to important values like privacy, fairness, transparency, and accountability—values emphasized by information ethics scholars. Likewise, when such technologies put individuals at risk in order to drive scientific research and knowledge construction, we have to ask how they implicate values such as autonomy, beneficence and non-maleficence, and justice—values central to research ethics. And as researchers focusing on health information have long argued, when the data collected by these technologies pertain to individuals and public health, these ethical issues take on a special cast. -/- It is also true, however, that the pandemic has placed these questions in a new and revealing light. I highlight three insights from information ethics and research ethics that can help us navigate this difficult terrain. First, the value of privacy is instrumental, not absolute—there is nothing wrong with asking how to balance privacy against other important values. Second, privacy has both individual and social importance. Weighing privacy, on one hand, and public health, on the other, is not, therefore, a contest between individual and collective interests. Rather, it is an attempt to balance disparate public goods. Third, we ought to put these kinds of ethical decisions in the hands of third parties, rather than leaving them up to those who directly stand to benefit from them. In the case of pandemic surveillance technologies, this should mean more public, democratic oversight. (shrink)

Using speech act theory from the Philosophy of Language, this paper attempts to develop an ethical framework for the phenomenon of speech processing. We use the concepts of the illocutionary force and the illocutionary content of a speech act to explain the ethics of speech processing. By emphasizing the different stages involved in speech processing, we explore the distinct ethical issues that arise in relation to each stage. Input, processing, and output are the different ethically relevant stages under which (...) a spoken item or a speech navigates within the range of speech-processing modules. Employing the illocutionary force-content distinction, we specify and characterize the input-related ethical issues, the output-related ethical issues, and the processing-related ethical issues involved in speech processing. Together with illocutionary force-content distinction, we employ the data-information distinction to characterize the stage-wise ethical issues in the phenomenon of speech processing as the ethics of collecting (speech) data, the ethics of contextualizing (speech) data/information, and the ethics of releasing the con-textualized information (processed speech). Immediate ethical issues that arise from the range of speech processing modules are distinguished from distant ethical issues. We also indicate the nature of ethical issues that arise from Speaker Independent speech technologies. (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)

Training artificial intelligence (AI) systems requires vast quantities of data, and AI developers face a variety of barriers to accessing the information they need. Synthetic data has captured researchers’ and industry’s imagination as a potential solution to this problem. While some of the enthusiasm for synthetic data may be warranted, in this short paper we offer critical counterweight to simplistic narratives that position synthetic data as a cost-free solution to every data-access challenge—provocations highlighting ethical, (...) political, and governance issues the use of synthetic data can create. We question the idea that synthetic data, by its nature, is exempt from privacy and related ethical concerns. We caution that framing synthetic data in binary opposition to “real” measurement data could subtly shift the normative standards to which data collectors and processors are held. And we argue that by promising to divorce data from its constituents—the people it represents and impacts—synthetic data could create new obstacles to democratic data governance. (shrink)

This abstract summarizes, in essence, what generative AI means to the insurance industry. The kind of promise generated AI offers to insurance is huge: in risk assessment, customer experience, and operational efficiency. Natural disaster impact, financial market volatility, and cyber threat are augmented with techniques of real time scenario generation and modeling as well as predictive simulation based on synthetic data. One of the challenges that stand in the way of deploying these AI methods, however, is data privacy, (...) model reliability and interpretability. The insurance industry must address these issues if it is to comply with regulations like GDPR and CCPA or mitigate reputational risks, as anybody can make complaints about any company based on any perceived data breach. Indeed, generative AI models, often convoluted and biased, can propagate unfair outcomes, especially in pricing and policy access. Navigating these challenges requires a whole new frayed society of AI specialists in collaboration with the industry’s insurers to offer bespoke solutions that address the firm’s needs. Based on this work, future developments will center on designing and implementing ethical and regulatory frameworks around AI-driven decisions that are transparent, fair and sustainable. Generative AI has historically been the most feared phenomenon in the history of western civilization, but with predictive analytics and risk simulation capabilities taking shape, generative AI is set to disrupt the insurance industry and add real-time decision support, automating claims processing and personalized interaction. To successfully integrate generative AI, technical, ethical and regulatory challenges will need to be addressed, and a trust-driven ecosystem will emerge to serve evolving customer needs. (shrink)

This paper explores the challenges and innovations in optimizing data science workflows within cloud computing environments. It begins by highlighting the critical role of data science in modern industries and the pivotal contribution of cloud computing in enabling scalable and efficient data processing. The primary focus lies in identifying and analyzing the key challenges encountered in current data science workflows deployed in cloud infrastructures. These challenges include scalability issues related to handling large volumes of data, (...) resource management complexities in optimizing computational resources, cost management strategies to balance performance with expenses, and ensuring robust data security and privacy measures. The manuscript then delves into innovative solutions and techniques aimed at addressing these challenges. It discusses advancements such as workflow automation tools and frameworks that streamline repetitive tasks, containerization technologies like Docker and Kubernetes for efficient application deployment and management, and the utilization of serverless architectures to enhance scalability and reduce operational costs. Additionally, it explores the benefits of parallel processing frameworks such as Apache Spark and Hadoop in optimizing data processing tasks. The integration of machine learning algorithms for dynamic workflow optimization and effective data management strategies in cloud environments are also examined. Through detailed case studies and application examples across various domains, the manuscript illustrates the practical implementation and outcomes of these optimization strategies. Furthermore, it discusses emerging trends in cloud technologies, the role of AI-driven automation in enhancing workflow efficiencies, and ethical considerations surrounding data science operations in cloud computing. The manuscript concludes with a summary of findings, practical recommendations for organizations seeking to enhance their data science workflows in the cloud, and insights into future research directions to address evolving challenges. (shrink)