Ethics Of Big Data Research Articles

Big Data Adoption: Perspectives, Realities, and Myths - An Outer View in Academic Libraries

It is clear that the deluge of information has forced the current civilization into the era of Big Data adoption given the information's quick development and the widespread use of information gadgets. It has been noted that technology is becoming more and more essential to many facets of human existence, which makes this dependence relevant and important. A university's academic library is one of its three pillars. It performs vital functions such as teaching and research services and serves as the hub for auxiliary instruction, information sharing, academic activities, and cultural inheritance. All of these functions depend heavily on the ever-growing amount of data, which is sometimes referred to as "Big Data adoption." This study addresses the concerning connection between the use of Big Data and operations in many areas of human existence and offers a comprehensive explanation of Big Data and its significance in academic libraries. Aside from this, the study clarifies the general ethics of Big Data, its features, advantages, tools, the role of librarians, and the causes of the important problems related to big data adoption and the perspectives in academic libraries. Thus, the adoption of big data continues to be an intriguing new area in both science and technology. Therefore, greater familiarization and demystification of Big Data adoption is still necessary, even with the development of technology and availability of free knowledge. It will require more advanced computing abilities to comprehend and utilize it to its full potential.

A contextual integrity approach to genomic information: what bioethics can learn from big data ethics

Genomic data is generated, processed and analysed at an increasingly rapid pace. This data is not limited to the medical context, but plays an important role in other contexts in society, such as commercial DNA testing, the forensic setting, archaeological research, and genetic surveillance. Genomic information also crosses the borders of these domains, e.g. forensic use of medical genetic information, insurance use of medical genomic information, or research use of commercial genomic data. This paper (1) argues that an informed consent approach for genomic information has limitations in many societal contexts, and (2) seeks to broaden the bioethical debate on genomic information by suggesting an approach that is applicable across multiple societal contexts. I argue that the contextual integrity framework, a theory rooted in information technology and big data ethics, is an effective tool to explore ethical challenges that arise from genomic information within a variety of different contexts. Rather than focusing on individual control over information, the contextual integrity approach holds that information should be shared and protected according to the norms that govern certain distinct social contexts. Several advantages of this contextual integrity approach will be discussed. The paper concludes that the contextual integrity framework helps to articulate and address a broad spectrum of ethical, social, and political factors in a variety of different societal contexts, while giving consideration to the interests of individuals, groups, and society at large.

From academia to industry: A framework to securely implement big data and AI to predict college graduates' employment trajectories

The transition from academia to industry can be unpredictable, but what if we could forecast college graduate employment outcomes with both accuracy and robust security? This study introduces an innovative framework that leverages secure data analysis and machine learning to predict the employment trajectories of college graduates. By integrating homomorphic encryption, we safeguard the privacy of sensitive personal and academic data while enabling complex machine learning operations. Our approach involves meticulous data collection, feature engineering, encryption, and model development, resulting in a robust model that addresses privacy concerns without sacrificing prediction accuracy. We demonstrate our model's superiority over traditional approaches, achieving a notable increase in both security and stability. This research illuminates the potential of encrypted data analysis in reshaping predictive modeling methods, offering insights for educational institutions, policymakers, and students. Our findings not only address a pressing issue in employment forecasting but also lay the groundwork for secure and ethical big data applications across various domains.

Contemporary Applied Ethics

In essence, ethics is the study of right and wrong. Itis the philosophical study ofmoralphenomena.Itrepresents the moral code that guides a person’s choices and behaviors throughout their life.Ethical issues, particularly concerning emerging technologies such as Internet of things, cloud computing, big data, artificial intelligence, and robotics, are now major concerns for businesses, organizations, and governments. This paperis onapplied ethics in contemporary areasand it includes cybersecurityethics, computer ethics, food ethics, business ethics, big data ethics, and research ethics.

Big Data Ethics in Education and Research

Big Data Ethics in Education and Research

The Ethics of Big Data and Artificial Intelligence in Perioperative Medicine: Is Unregulated AI Already at the Bedside?

The Ethics of Big Data and Artificial Intelligence in Perioperative Medicine: Is Unregulated AI Already at the Bedside?

Ethical ambiguity and complexity: tech workers’ perceptions of big data ethics in China and the US

ABSTRACT Despite extensive studies on ethical data use and algorithms, little is known about the ethical perspectives of tech workers – insiders of a profession that heavily relies on data and algorithms. Nor have there been sufficient cross-national comparisons of their perspectives. Relying on interviews with 98 tech workers in China and the US, this paper is guided by two questions: (1) What are tech workers' perceptions of big data ethics, and (2) what are the cross-national similarities and differences in China and the US? The study found that there are cross-national similarities in tech workers’ cautious enthusiasm about the applications of big data in their work, as well as in their complex and ambivalent ethical perspectives on the use of big data in government digital surveillance. The main cross-national differences occur in tech workers’ perceptions of whether big data may reinforce social inequalities. US-based tech workers are concerned about the reinforcement of race and gender-based inequalities through the use of big data, whereas their colleagues in China are optimistic that the use of big data may reduce income-based inequalities across geographical regions. The study's findings have implications for how to leverage tech workers’ influence and promote the ethical use of data and algorithms.

Big data ethics and its role in the innovation and technology adoption process

PurposeUpon graduating from university, many engineers will work in new product development and/or technology adoption for continuous improvement and production optimization. These jobs require employees to be cognizant of ethical practices and implications for design. However, little engineering coursework, outside the traditional ABET (Accreditation Board for Engineering and Technology) required Engineering Ethics course, accounts for the role of ethics within this process. Because of this, engineering students have few learning opportunities to practice and reflect on ethical decision-making.Design/methodology/approachThis paper highlights one approach to integrating ethics into an engineering course (outside of engineering ethics). Specifically, the study is implemented within a five-week module with a focus on big data ethics, as part of a Supply Chain Management Technology course (required for Industrial Engineering Technology majors), using metacognition as the core assessment.FindingsFour main themes were identified through the qualitative data analysis of the metacognitive reflections: (1) overreliance on content knowledge, (2) time management skills, (3) career connections and (4) knowledge extensions.Originality/valueThree notable points emerged which contribute to the literature. First, this study showcased one example of how an ethics module can be integrated into an engineering course (other than Engineering Ethics). Second, this study demonstrated how metacognitive reflections can be used to reinforce student self-awareness of the learning process and connections to big data ethics in the workplace. Finally, this study exhibited how metacognitive reflection assignments can be deployed as a teaching and learning assessment tool, providing an opportunity for the instructor to make immediate changes as needed.

Big Data Ethics in Education and Research

Big data ethics involves adherence to the concepts of right and wrong behavior regarding data, especially personal data. Big Data ethics focuses on structured or unstructured data collectors and disseminators. Big data ethics is supported, at EU level, by extensive documentation, which seeks to find concrete solutions to maximize the value of big data without sacrificing fundamental human rights. The European Data Protection Supervisor (EDPS) supports the right to privacy and the right to the protection of personal data in the respect of human dignity.

“Data makes the story come to life:” understanding the ethical and legal implications of Big Data research involving ethnic minority healthcare workers in the United Kingdom—a qualitative study

The aim of UK-REACH (“The United Kingdom Research study into Ethnicity And COVID-19 outcomes in Healthcare workers”) is to understand if, how, and why healthcare workers (HCWs) in the United Kingdom (UK) from ethnic minority groups are at increased risk of poor outcomes from COVID-19. In this article, we present findings from the ethical and legal stream of the study, which undertook qualitative research seeking to understand and address legal, ethical, and social acceptability issues around data protection, privacy, and information governance associated with the linkage of HCWs’ registration data and healthcare data. We interviewed 22 key opinion leaders in healthcare and health research from across the UK in two-to-one semi-structured interviews. Transcripts were coded using qualitative thematic analysis. Participants told us that a significant aspect of Big Data research in public health is varying drivers of mistrust—of the research itself, research staff and funders, and broader concerns of mistrust within participant communities, particularly in the context of COVID-19 and those situated in more marginalised community settings. However, despite the challenges, participants also identified ways in which legally compliant and ethically informed approaches to research can be crafted to mitigate or overcome mistrust and establish greater confidence in Big Data public health research. Overall, our research indicates that a “Big Data Ethics by Design” approach to research in this area can help assure (1) that meaningful community and participant engagement is taking place and that extant challenges are addressed, and (2) that any new challenges or hitherto unknown unknowns can be rapidly and properly considered to ensure potential (but material) harms are identified and minimised where necessary. Our findings indicate such an approach, in turn, will help drive better scientific breakthroughs that translate into medical innovations and effective public health interventions, which benefit the publics studied, including those who are often marginalised in research.

The Leading Role of University Libraries on the Ethics of Providing Big Data and the Analysis and the Extent of its Impact on Scientific Research: A survey of the libraries of the universities of the Kordofan sector.

Information institutions, especially university libraries, are facing the inevitability of dealing with the big data revolution that emerged as a result of the increasing digitalization in size and quality. Accordingly, university libraries are concerned with dealing big data in many activities in terms of acquiring various sources, making available to search in databases available on knowledge banks, and statistical analyzes of the data used in addition to the tools for managing research data. In the light of the foregoing, the study sought to identify the significant role of university libraries in analyzing big data, and clarifying the ethics of its use in scientific research, as well as making it accessible to researchers to help solve problems and analyze the difficulties that libraries, researchers, and library workers may face in this huge revolution. This study also aimed at confronting the big data revolution and addressing it, taking into consideration the proficiency in dealing with its use software so that workers can analyze big data using sound scientific methods. The descriptive approach was used in relation to the data for presenting and defining the basic concepts of big data ethics. In addition, a survey method was used by studying a sample of library and information specialists in the universities of the Kordofan sector, in addition to the questionnaire. The data collected was analyzed by the SPSS program and reached a number of results in which 90% of the surveyed sample proved the possibility of employing big data analytics to develop university library services and support decision makers in the library, which is directly reflected on researchers and scientific research. The study recommends the need to develop an agreed upon scientific ethical standard to be adopted by the Sudanese Association for Libraries and Information with the Sudanese University Libraries Coalition approved by the Federation of Sudanese Universities and to be signed by the Ministry of Higher Education Scientific Research as a Sudanese scientific and legal specification on how to use big data and its analyzes in Sudanese university libraries.

Discussion on the Path of Ideological and Political Education from the Perspective of Big Data Ethics

Discussion on the Path of Ideological and Political Education from the Perspective of Big Data Ethics

Ethical Issues in Social Science Research Employing Big Data

This paper analyzes the ethics of social science research (SSR) employing big data. We begin by highlighting the research gap found on the intersection between big data ethics, SSR and research ethics. We then discuss three aspects of big data SSR which make it warrant special attention from a research ethics angle: (1) the interpretative character of both SSR and big data, (2) complexities of anticipating and managing risks in publication and reuse of big data SSR, and (3) the paucity of regulatory oversight and ethical recommendations on protecting individual subjects as well as societies when conducting big data SSR. Against this backdrop, we propose using David Resnik’s research ethics framework to analyze some of the most pressing ethical issues of big data SSR. Focusing on the principles of honesty, carefulness, openness, efficiency, respect for subjects, and social responsibility, we discuss three clusters of ethical issues: those related to methodological biases and personal prejudices, those connected to risks arising from data availability and reuse, and those leading to individual and social harms. Finally, we advance considerations to observe in developing future ethical guidelines about big data SSR.

Using Knowledgeable Agents of the Digital and data feminism to uncover social identities in the #blackgirlmagic Twitter community

ABSTRACT Online spaces have the capacity to be powerful informal learning and identity development spaces for marginalized communities. However, there is still much work to be done to uncover these complex social identities using ethical big data analyses. In this study, I draw on the theory of Knowledgeable Agents of the Digital, data feminism, and critical reflexivity practices to engage with a #blackgirlmagic Twitter dataset from 2016 to 2019. Using Epistemic Network Analysis, findings suggest that the #blackgirlmagic community self-defined their social identities around Black beauty, academic/professional accomplishments, and social justice. Because the women and girls of #blackgirlmagic were agentive in rewriting and sharing narratives of themselves, they were acting as knowledgeable agents of the digital. These findings may be useful for (1) uncovering other instances of knowledgeable agents from non-dominant populations and how they navigate a racialized and gendered society, and (2) providing suggestions for analyzing online big data through an ethical, intersectional feminist lens.

The Ethical Implications of Big Data Research in Public Health: "Big Data Ethics by Design" in the UK-REACH Study.

In this article, we analyze legal and ethical issues raised in Big Data health research projects in the Covid-19 era and consider how these issues might be addressed in ways that advance positive values (e.g., furtherance of respect for persons and accordance with relevant legal frameworks) while mitigating or eliminating any negative aspects (e.g., exacerbation of social inequality and injustice). We apply this analysis specifically to UK-REACH (The United Kingdom Research Study into Ethnicity and Covid-19 Outcomes in Healthcare Workers), a project with which we are involved. We argue that Big Data projects like UK-REACH can be conducted in an ethically robust manner and that funders and sponsors ought to encourage similar projects to drive better evidence-based public policy in public health. As part of this, we advocate that a Big Data ethics-by-design approach be undertaken when such projects are constructed. This principle extends the work of those who advocate ethics by design by addressing prominent issues in Big Data health research projects; it holds that ethical values and principles in Big Data health research projects are best adhered to when they are already integrated into the project aims and methods at the design stage. In advocating this principle, we present a unique perspective regarding pressing ethical problems around large-scale, data-driven Covid-19 research, as well as legal issues associated with processing ostensibly anonymized health data.

The Challenges of Big Data for Research Ethics Committees: A Qualitative Swiss Study.

Big data trends in health research challenge the oversight mechanism of the Research Ethics Committees (RECs). The traditional standards of research quality and the mandate of RECs illuminate deficits in facing the computational complexity, methodological novelty, and limited auditability of these approaches. To better understand the challenges facing RECs, we explored the perspectives and attitudes of the members of the seven Swiss Cantonal RECs via semi-structured qualitative interviews. Our interviews reveal limited experience among REC members with the review of big data research, insufficient expertise in data science, and uncertainty about how to mitigate big data research risks. Nonetheless, RECs could strengthen their oversight by training in data science and big data ethics, complementing their role with external experts and ad hoc boards, and introducing precise shared practices.

Identifying home locations in human mobility data: an open-source R package for comparison and reproducibility

ABSTRACT Identifying meaningful locations, such as home or work, from human mobility data has become an increasingly common prerequisite for geographic research. Although location-based services (LBS) and other mobile technology have rapidly grown in recent years, it can be challenging to infer meaningful places from such data, which – compared to conventional datasets – can be devoid of context. Existing approaches are often developed ad-hoc and can lack transparency and reproducibility. To address this, we introduce an R package for inferring home locations from LBS data. The package implements pre-existing algorithms and provides building blocks to make writing algorithmic ‘recipes’ more convenient. We evaluate this approach by analyzing a de-identified LBS dataset from Singapore that aims to balance ethics and privacy with the research goal of identifying meaningful locations. We show that ensemble approaches, combining multiple algorithms, can be especially valuable in this regard as the resulting patterns of inferred home locations closely correlate with the distribution of residential population. We hope this package, and others like it, will contribute to an increase in use and sharing of comparable algorithms, research code and data. This will increase transparency and reproducibility in mobility analyses and further the ongoing discourse around ethical big data research.

Data Experts as the Balancing Power of Big Data Ethics

In this theoretical paper, we explore Big Data ethics in the broader context of general data ethics, stakeholder groups, demand for governance and regulation, social norms, and human values. We follow and expand on the digital divide, governance, and regulatory theories, and we apply them to many levels and contexts, such as state and society, organization, enterprise governance of IT (EGIT), and data projects, among others. We introduce the new role and responsibility of data experts as an important stakeholder group in the balance of power of Big Data ethics because they simultaneously hold a position in groups of data-rich organizations and data-poor users. We argue that the balancing role of data experts consists of motivation and competence, a sense of responsibility for data ethics, and the possibility and means to influence Big Data issues. Finally, we conclude our research by model mapping the role of data experts in Big Data ethics and proposing them as a balancing power.

Group Roundtable: Queer Tech Futures, Social Justice, and Community-Based Technology Education

Abstract This essay describes a year-long community-based collaboration between faculty at Purdue University, a game designer at University of Chicago, and MAVEN Youth. Project partners sought to develop a community technology curriculum that centers the lives of LGBTQ and non-binary youth and imagines queer bodies as central to any future we wish to inhabit. Over the year-long project, the partners developed a series of social justice game design workshops for LGBTQ youth and a speculative design Hack-4-Queer Youth Futures. These types of collaborations and “making-and-telling” practices are vital to imagining inclusive and livable futures. This collaboration is an outgrowth of stakeholder engagement for the Big Data Ethics project at Purdue University supported by the Andrew W. Mellon Foundation. The goal of the group roundtable format is to gain better insight into the potential for embedding critical science and technology studies (STS) and social justice pedagogy into community-based tech diversity initiatives.

A qualitative study of big data and the opioid epidemic: recommendations for data governance

BackgroundThe opioid epidemic has enabled rapid and unsurpassed use of big data on people with opioid use disorder to design initiatives to battle the public health crisis, generally without adequate input from impacted communities. Efforts informed by big data are saving lives, yielding significant benefits. Uses of big data may also undermine public trust in government and cause other unintended harms.ObjectivesWe aimed to identify concerns and recommendations regarding how to use big data on opioid use in ethical ways.MethodsWe conducted focus groups and interviews in 2019 with 39 big data stakeholders (gatekeepers, researchers, patient advocates) who had interest in or knowledge of the Public Health Data Warehouse maintained by the Massachusetts Department of Public Health.ResultsConcerns regarding big data on opioid use are rooted in potential privacy infringements due to linkage of previously distinct data systems, increased profiling and surveillance capabilities, limitless lifespan, and lack of explicit informed consent. Also problematic is the inability of affected groups to control how big data are used, the potential of big data to increase stigmatization and discrimination of those affected despite data anonymization, and uses that ignore or perpetuate biases. Participants support big data processes that protect and respect patients and society, ensure justice, and foster patient and public trust in public institutions. Recommendations for ethical big data governance offer ways to narrow the big data divide (e.g., prioritize health equity, set off-limits topics/methods, recognize blind spots), enact shared data governance (e.g., establish community advisory boards), cultivate public trust and earn social license for big data uses (e.g., institute safeguards and other stewardship responsibilities, engage the public, communicate the greater good), and refocus ethical approaches.ConclusionsUsing big data to address the opioid epidemic poses ethical concerns which, if unaddressed, may undermine its benefits. Findings can inform guidelines on how to conduct ethical big data governance and in ways that protect and respect patients and society, ensure justice, and foster patient and public trust in public institutions.