The exponential accumulation, processing and accrual of big data in healthcare are only possible through an equally rapidly evolving field of big data analytics. The latter offers the capacity to rationalize, understand and use big data to serve many different purposes, from improved services modelling to prediction of treatment outcomes, to greater patient and disease stratification. In the area of infectious diseases, the application of big data analytics has introduced a number of changes in (...) the information accumulation models. These are discussed by comparing the traditional and new models of data accumulation. Big data analytics is fast becoming a crucial component for the modelling of transmission—aiding infection control measures and policies—emergency response analyses required during local or international outbreaks. However, the application of big data analytics in infectious diseases is coupled with a number of ethical impacts. Four key areas are discussed in this paper: automation and algorithmic reliance impacting freedom of choice, big data analytics complexity impacting informed consent, reliance on profiling impacting individual and group identities and justice/fair access and increased surveillance and population intervention capabilities impacting behavioural norms and practices. Furthermore, the extension of big data analytics to include information derived from personal devices, such as mobile phones and wearables as part of infectious disease frameworks in the near future and their potential ethical impacts are discussed. Considered together, the need for a constructive and transparent inclusion of ethical questioning in this rapidly evolving field becomes an increasing necessity in order to provide a moral foundation for the societal acceptance and responsible development of the technological advancement. (shrink)

In this article, we show how it is possible to lawfully buy an election. The method we describe for buying an election is novel. The key things that make it possible to buy an election are the existence of public voter registration lists where one can see whether a given elector has voted in a particular election, and the existence of Big Data Analytics that with a high degree of accuracy can predict what a given elector will vote (...) in an upcoming election. Someone interested in buying an election can enter an employment contract with all, or some of, the opponent electors where these electors are paid to do a job that prevents them from voting. By purchasing access to the public voter registration lists, it is possible to verify ex-post whether the opponent electors that one has signed a contract with have abstained. In the last two sections of the article, we discuss several barriers that can undermine an attempt to buy an election in the manner we identify. (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)

“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)

Agricultural Big Data analytics (ABDA) is being proposed to ensure better farming practices, decision-making, and a sustainable future for humankind. However, the use and adoption of these technologies may bring about potentially undesirable consequences, such as exercises of power. This paper will analyse Brey’s five distinctions of power relationships (manipulative, seductive, leadership, coercive, and forceful power) and apply them to the use agricultural Big Data. It will be shown that ABDA can be used as a form of (...) manipulative power to initiate cheap land grabs and acquisitions. Seductive power can be exercised by pressuring farmers into situations they would not have otherwise chosen (such as installing monitors around their farm and limited access to their farm and machinery). It will be shown that agricultural technology providers (ATPs) demonstrate leadership power by getting farmers to agree to use ABDA without informed consent. Coercive power is exercised when ATPs threaten farmers with the loss of ABDA if they do not abide by the policies and requirements of the ATP or are coerced to remain with the ATP because of fear of legal and economic reprisal. ATPs may use ABDA to determine willingness-to-pay rates from farmers, using this information to force farmers into precarious and vulnerable positions. Altogether, this paper will apply these five types of power to the use and implementation of ABDA to demonstrate that it is being used to exercise power in the agricultural industry. (shrink)

Can we measure and predict character with predictive analytics so a business can better assess, ideally objectively, whether to lend money or extend credit to that person, beyond current objective measures of credit scores and standard financial metrics like solvency and debt ratios? We and the analysts probably do not know enough about character to try to measure it, though it might be more useful to measure and predict a person’s temperance and prudence as virtues, or self-control as psychology, (...) or sense of obligation, particularly a moral commitment or sense of duty to honor a contract and re-pay a loan. The pervasive data surveillance of people that goes with “big data” and predictive analytics is not only an invasion of privacy in general, but an impairment of the aspect of privacy called autonomy that will constrict and alter a person’s choices and development of self. (shrink)

Although big data analytics capability leads to competitive performance, the mechanism of the relationship is still unclear. To narrow the research gap, this paper investigates the mediating roles of two forms of resource integration in the relationship between two forms of BDAC [big data analytics management capability and BDA technology capability] and competitive performance. Supported by Partial Least Squares-Structural Equation Modeling and the cross-sectional survey data from 219 Chinese enterprises, the results show that the resource (...) bricolage plays a significantly mediating role in the relationships between BDA management capability and competitive performance as well as in the relationship between BDA technology capability and competitive performance. Furthermore, the mediating effect in the former relationship is stronger than that in the latter relationship. Additionally, BDA technology capability only has a direct effect on resource bricolage, while BDA management capability has a stronger effect on resource optimization than that on resource bricolage. Finally, resource bricolage has a stronger impact on competitive performance than resource optimization. These findings contribute to understanding how enterprises could apply different forms of BDAC to other kinds of resource integration to achieve outstanding competitive performance. (shrink)

Data analytics and data-driven approaches in Machine Learning are now among the most hailed computing technologies in many industrial domains. One major application is predictive analytics, which is used to predict sensitive attributes, future behavior, or cost, risk and utility functions associated with target groups or individuals based on large sets of behavioral and usage data. This paper stresses the severe ethical and data protection implications of predictive analytics if it is used to (...) predict sensitive information about single individuals or treat individuals differently based on the data many unrelated individuals provided. To tackle these concerns in an applied ethics, first, the paper introduces the concept of “predictive privacy” to formulate an ethical principle protecting individuals and groups against differential treatment based on Machine Learning and Big Data analytics. Secondly, it analyses the typical data processing cycle of predictive systems to provide a step-by-step discussion of ethical implications, locating occurrences of predictive privacy violations. Thirdly, the paper sheds light on what is qualitatively new in the way predictive analytics challenges ethical principles such as human dignity and the (liberal) notion of individual privacy. These new challenges arise when predictive systems transform statistical inferences, which provide knowledge about the cohort of training data donors, into individual predictions, thereby crossing what I call the “prediction gap”. Finally, the paper summarizes that data protection in the age of predictive analytics is a collective matter as we face situations where an individual’s (or group’s) privacy is violated using dataotherindividuals provide about themselves, possibly even anonymously. (shrink)

Currently, most studies are moving towards Big Data Analytics because they are important in research, and this is becoming increasingly important as Internet and Web 2.0 technologies become increasingly popular and how to handle this massive data. Moreover, this proliferation of the Internet and social media has revolutionized the search process. With this Big Data of data generated by users using social media or electronic platforms, the use of these details and daily activities is integrated (...) with tools designed for analysis. The topic of analyzing big social media will be discussed and an intensive explanation will be given to the topic of Big Data. This paper compares Big Data analysis techniques using several methods of analysis, the first technique using neural networks and the second technique using data clustering. The purpose of this study is to infer the ages that use social media and what are their interests in writing and in the end, who are the most widely used social media males or females. (shrink)

As organizations are benefiting from investments in big data analytics capabilities building and education, our study has analyzed the impact of big data analytics capabilities building and education on business model innovation. It has also assessed technological orientation and employee creativity as mediating and moderating variables. Questionnaire data from 499 managers at enterprises in Jiangsu, China have been analyzed using Structural Equation Modeling in SmartPLS. Big data analytics capabilities building and education strengthen technological (...) orientation and increase business model innovation. Technology orientation increases business model innovation and plays a mediating role. Employee creativity also boosts innovation. These findings show that business managers should adopt and promote a technological orientation. They should hire and train employees with big data education and training. Organizations can try to select and support employees who show creativity. (shrink)

This paper aims to contribute to the development of tools to support an analysis of Big Data as manifestations of social processes and human behaviour. Such a task demands both an understanding of the epistemological challenge posed by the Big Data phenomenon and a critical assessment of the offers and promises coming from the area of Big Data analytics. This paper draws upon the critical social and data scientists’ view on Big Data as an (...) epistemological challenge that stems not only from the sheer volume of digital data but, predominantly, from the proliferation of the narrow-technological and the positivist views on data. Adoption of the social-scientific epistemological stance presupposes that digital data was conceptualised as manifestations of the social. In order to answer the epistemological challenge, social scientists need to extend the repertoire of social scientific theories and conceptual frameworks that may inform the analysis of the social in the age of Big Data. However, an ‘epistemological revolution’ discourse on Big Data may hinder the integration of the social scientific knowledge into the Big Data analytics. (shrink)

Much bioethical literature and policy guidances for big data analytics in biomedical research emphasize the importance of trust. It is essential that potential participants trust so they will allow their data to be used to further research. However, comparatively, little guidance is offered as to what trustworthy oversight mechanisms are, or how policy should support them, as data are collected, shared, and used. Generally, “trust” is not characterized well enough, or meaningfully enough, for the term to (...) be systematically applied in policy development. Yet points made in the philosophical literature on trust can help. They allow us, not only to better distinguish the different ways the term “trust” may be interpreted, but also to better determine how different approaches to trust can align with policy and governance—in what ways they may relate to key bioethical concepts and related laws, and in what ways they can help to balance individual and group interests in data sharing. This article draws from the philosophical literature on trust to identify a relationship among consent, trust, and justice. Specifically, parallels are drawn between “character-trustworthiness” and “natural justice,” a set of widely held legal safeguards intended to ensure decision-makers follow a pattern of procedural fairness which protects the rights of the individual and thereby maintains public confidence in the decision-making process. Relevance to traditional bioethical principles, established laws, and consent procedures are addressed throughout. In conclusion, policy actions are suggested. (shrink)

Mature information societies are characterised by mass production of data that provide insight into human behaviour. Analytics has arisen as a practice to make sense of the data trails generated through interactions with networked devices, platforms and organisations. Persistent knowledge describing the behaviours and characteristics of people can be constructed over time, linking individuals into groups or classes of interest to the platform. Analytics allows for a new type of algorithmically assembled group to be formed that (...) does not necessarily align with classes or attributes already protected by privacy and anti-discrimination law or addressed in fairness- and discrimination-aware analytics. Individuals are linked according to offline identifiers and shared behavioural identity tokens, allowing for predictions and decisions to be taken at a group rather than individual level. This article examines the ethical significance of such ad hoc groups in analytics and argues that the privacy interests of algorithmically assembled groups in inviolate personality must be recognised alongside individual privacy rights. Algorithmically grouped individuals have a collective interest in the creation of information about the group, and actions taken on its behalf. Group privacy is proposed as a third interest to balance alongside individual privacy and social, commercial and epistemic benefits when assessing the ethical acceptability of analytics platforms. (shrink)

Digital ethics is increasingly applied in practice within contexts such as public service and corporate environments. However, a significant challenge remains in understanding how to implement these principles effectively without diluting their efficacy and meaningful impact. While organisations employ a range of solutions, such as committees, training, audits, or tools to bridge the often cited “principles to practice gap,” there is a recognised need to tailor these solutions to address domain-relevant challenges, such as in data analytics. Despite the (...) substantial role of data analytics in driving decision-making processes and fostering innovation across various sectors, there is a notable gap in research focused on how digital ethics is operationalised in data analytics. Our work presented here addresses this gap by discussing how operationalisation of digital ethics can be achieved within this domain. The challenges and insights from the Analytics Center of Excellence at Merck KGaA, a multinational science and technology company, are utilised as a case study for implementing digital ethics within this domain. We present the development and deployment of Merck’s Digital Ethics Check (MDEC), an instrument tailored to address the ethical challenges in data analytics. The present work underscores the advantages of a semi-automated integration of digital ethics into data analytics. In doing so, we highlight the relevance of the challenges encountered in the practical application of digital ethics within the domain of data analytics in a business context. Our work provides a practical impetus to guide organisations seeking to embed ethical considerations within their data analytics operations. (shrink)

This paper offers a sketch of the complicated conflicts which arise—and metastasize seemingly daily—in the era of Big Data. Given the public’s ubiquitous-yet-ostensibly-voluntary data surrender, and industry’s ubiquitous-yet-ostensibly-anodyne collection of the same, inaction is not an option for any near-just society. By revisiting the philosophical basis for Panoptic apparatus, sketching the tumultuous history of US contract law trying to protect the public from itself, and comparing existing industry codes for similarly-situated—read: terrifyingly invasive—fields, the paper will provide a preliminary (...) framework for identifying and confronting the galaxy of problems associated with data analytics. (shrink)

Industry 4.0 and digital technological innovations have facilitated faster, flexible, and efficient production, consumption, and transmission of information. Consequently, technological advancements such as Artificial Intelligence (AI), Blockchain Technologies (BT), and Business Data Analytics (BDA) are reshaping the daily tasks of accountants worldwide and the accounting field can capitalize and benefit from new opportunities. Notably, the accounting profession is rapidly evolving, as are the skills and careers of accounting professionals. In response to these changes, the accounting profession has developed (...) competency frameworks and career path publications to assist its members in adapting, addressing challenges, and seizing new opportunities. However, the university curriculum often introduces these technological advancements as separate modules rather than embedding them into the existing course content. Technology is reshaping accounting and educational institutions must equip students with the necessary skills to succeed in digital environments. By integrating digital technologies into the curriculum, students can acquire knowledge about modern accounting practices to meet the evolving needs of the industry. This study follows a qualitative research method that utilizes desktop research since it is theoretical in nature. It builds on existing research studies to explore the integration of AI, BT, and BDA into the University of Johannesburg’s (UJ) Commercial Accounting Diploma Curriculum. It also expands on the educational framework by proposing learning outcomes to be covered in the curriculum and providing teaching guidelines to support the new insertions. The study concludes that, the current curriculum does not integrate digital technologies and suggests the adoption of a new curriculum that addresses this gap. (shrink)

Indian hydrocarbon industry plays a significant role globally since it’s the 4th largest in terms of Refining capacity, 4th largest in Liquified Natural Gas (LNG) terminal capacity, and 7th largest exporter of refined products. India’s Jamnagar refinery, one of the world’s largest, establishes it’s dominance in the global oil and gas refining sector.Recent advancement in Big Data Analytics (BDA) adoption have impacted performance of the organization. To harness the best out of Big Data (BD) technologies, the Indian (...) hydrocarbon industry must undergo significant transformation in operational efficiency, predictive maintenance, and decision making processes. This literature review examines the scenario of BD adoption in the Indian hydrocarbon industry, finding barriers, and drivers focusing on hydrocarbon (oil and gas) exploration, production, refining, and supply chain management.Study identifies key areas where BD adoption plays a transformative role in data analysis for predictive maintenance, operational efficiency, enhancing personnel health & safety, and optimizing resource allocation. Findings suggest that BD adoption in the Indian hydrocarbon industry is still progressing, but there need for significant strategic investments in BD technologies and resource skill enhancement. This literature review contributes to academia and industry, by identifying gaps in current research and recommendations for integrating big data technologies in the Indian hydrocarbon industry, overcoming adoption barriers. (shrink)

This study aims to explore the role of digital education in the development of skills and employability for engineering students through researching the role of big data analytics courses. The empirical study proposes the hypothesis that both soft and hard skills have positive effects on human capital, individual attributes, and the career development dimensions of engineering students. This is achieved through constructing a framework of three dimensions of engineering students’ employability and two competency development dimensions of big (...) class='Hi'>data analytics courses. A questionnaire survey was conducted with 155 college engineering students and a structural equation model was used to test the hypotheses. The results found that courses on big data analytics have a positive impact on engineering students’ abilities in both hard skills and soft skills dimensions, while soft skills have a more significant impact on engineering students’ employability. The study has practical and theoretical implications that further enriches the knowledge base on engineering education and broadens our understanding of the role of digitalization in enhancing the skills and employability of engineering students. (shrink)

Social media posts are full of potential for data mining and analysis. Recognizing this potential, platform providers increasingly restrict free access to such data. This shift provides new challenges for social scientists and other non-profit researchers who seek to analyze public posts with a purpose of better understanding human interaction and improving the human condition. This paper seeks to outline some of the recent changes in social media data analysis, with a focus on Twitter, specifically. Using Twitter (...) data from a 24-hour period following The Sisters in Spirit Candlelight Vigil, sponsored by the Native Women’s Association of Canada, this article compares three free-use Twitter application programming interfaces for capturing tweets and enabling analysis. Although recent Twitter data restrictions limit free access to tweets, there are many dynamic options for social scientists to choose from in the capture and analysis of Twitter and other social media platform data. This paper calls for critical social media data analytics combined with traditional, qualitative methods to address the developing ‘data gold rush.’. (shrink)

Intelligent big data analysis is an evolving pattern in the age of big data science and artificial intelligence. Analysis of organized data has been very successful, but analyzing human behavior using social media data becomes challenging. The social media data comprises a vast and unstructured format of data sources that can include likes, comments, tweets, shares, and views. Data analytics of social media data became a challenging task for companies, such as (...) Dailymotion, that have billions of daily users and vast numbers of comments, likes, and views. Social media data is created in a significant amount and at a tremendous pace. There is a very high volume to store, sort, process, and carefully study the data for making possible decisions. This article proposes an architecture using a big data analytics mechanism to efficiently and logically process the huge social media datasets. The proposed architecture is composed of three layers. The main objective of the project is to demonstrate Apache Spark parallel processing and distributed framework technologies with other storage and processing mechanisms. The social media data generated from Dailymotion is used in this article to demonstrate the benefits of this architecture. The project utilized the application programming interface of Dailymotion, allowing it to incorporate functions suitable to fetch and view information. The API key is generated to fetch information of public channel data in the form of text files. Hive storage machinist is utilized with Apache Spark for efficient data processing. The effectiveness of the proposed architecture is also highlighted. (shrink)

Big social data have enabled new opportunities for evaluating the applicability of social science theories that were formulated decades ago and were often based on small- to medium-sized samples. Big Data coupled with powerful computing has the potential to replace the statistical practice of sampling and estimating effects by measuring phenomena based on full populations. Preparing these data for analysis and conducting analytics involves a plethora of decisions, some of which are already embedded in previously collected (...) data and built tools. These decisions refer to the recording, indexing and representation of data and the settings for analysis methods. While these choices can have tremendous impact on research outcomes, they are not often obvious, not considered or not being made explicit. Consequently, our awareness and understanding of the impact of these decisions on analysis results and derived implications are highly underdeveloped. This might be attributable to occasional high levels of over-confidence in computational solutions as well as the possible yet questionable assumption that Big Data can wash out minor data quality issues, among other reasons. This article provides examples for how to address this issue. It argues that checking, ensuring and validating the quality of big social data and related auxiliary material is a key ingredient for empowering users to gain reliable insights from their work. Scrutinizing data for accuracy issues, systematically fixing them and diligently documenting these processes can have another positive side effect: Closely interacting with the data, thereby forcing ourselves to understand their idiosyncrasies and patterns, can help us to move from being able to precisely model and formally describe effects in society to also understand and explain them. (shrink)

Constellations, Volume 29, Issue 2, Page 146-160, June 2022.

With the development of network technology, enterprises face the explosive growth of data every day. Therefore, to fully mine the value of massive data, big data analysis technology has become the key to developing the core competitiveness of enterprises. However, few empirical studies have investigated the influencing mechanism of the BDA capability of an enterprise on its operational performance. To fill this gap, this study explores how BDA technology capability influences enterprise operation performance, based on dynamic capabilities (...) theory and resource-based theory. It proposes the key variables, including the connectivity, compatibility, and modularization of big data analysis technical capability, enterprise's operational performance, and the fit between data and tools, to establish a model and study the correlation between the variables. The results highlight the mediating role of data-tool fit in the relationships between BDA capability and the enterprise's operational performance, which is a major finding that has not been underlined in the extant literature. This study provides valuable insight for operational managers to help them in mobilizing BDA capability for enterprises' operational management and improving operational performance. (shrink)

Advances in science and technology have allowed for incredible improvements in healthcare. Additionally, the digital revolution in healthcare provides new ways of collecting and storing large volum...

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)

This paper will examine the development of a data warehouse aimed at improving decision-making in higher education, which focuses on the identification of students at-risk of academic failure through machine learning techniques. This research utilizes South East European University (SEEU) as a case study to show how data warehousing can integrate various student data—including demographics, academic performance, grades, attendance, and engagement—into an integrated framework that enables predictive analytics. The overall approach allows SEEU decision-makers, administrators, and faculty (...) to proactively identify and assist at-risk students, which improves student retention and their academic success. The results underscore the crucial role of data warehousing in enhancing student achievement and facilitating informed decision-making in higher education. The paper concludes with concrete suggestions for leveraging data to enhance decision-making processes in a digital educational institution. (shrink)

Modern companies are faced with a large amount of data about their employees, company processes and market conditions. Using this data to make decisions about personnel development can help companies effectively manage their personnel potential, predict training and development needs of employees, and plan strategic steps from the standpoint of personnel management. Research in this direction can also help identify optimal methods of attracting and retaining talented employees, which in turn will contribute to increasing the company’s competitiveness in (...) the market. Incorporating data and analytics into staff development decision-making processes can lead to more informed and objective decisions. The use of key performance indicators (KPIs) and metrics can provide valuable information about employee performance, training needs, and promotion. Personalized Development Plans: Data and analytics enable HR professionals to create personalized development plans for employees based on their strengths, weaknesses and career aspirations. This personalized approach can increase employee engagement and retention. Optimizing recruitment processes and talent retention with the help of data analytics opens up new opportunities for increasing the competitiveness of organizations. (shrink)

This paper discusses the empirical, Application Programming Interface -based analysis of very large Facebook Pages. Looking in detail at the technical characteristics, conventions, and peculiarities of Facebook’s architecture and data interface, we argue that such technical fieldwork is essential to data-driven research, both as a crucial form of data critique and as a way to identify analytical opportunities. Using the “We are all Khaled Said” Facebook Page, which hosted the activities of nearly 1.9 million users during the (...) Egyptian Revolution and beyond, as empirical example, we show how Facebook’s API raises important questions about data detail, completeness, consistency over time, and architectural complexity. We then outline an exploratory approach and a number of analytical techniques that take the API and its idiosyncrasies as a starting point for the concrete investigation of a large dataset. Our goal is to close the gap between Big Data research and research about Big Data by showing that the critical investigation of technicity is essential for empirical research and that attention to the particularities of empirical work can provide a deeper understanding of the various issues Big Data research is entangled with. (shrink)

As web analytics become increasingly common in newsrooms worldwide, it is imperative to understand how they are employed to meet news organizations’ journalistic and business goals. This study examines the evolution and applications of web analytics in newsrooms through a comprehensive analysis of relevant literature. It situates the rise of web analytics in the larger historical, social, and financial context. It explores the strategies news organizations adopt to integrate web analytics in news production. After discussing the (...) impact of analytics on news production, the project juxtaposes the pessimistic ethical concerns—primarily the prediction of market logic uprooting journalistic logic—surrounding the use of web analytics in newsrooms with ground realities. Finally, in view of the analysis, we reflect on the ethical concerns against analytics, the state of editorial autonomy in newsrooms, the implications of newly empowered audience for news ecosystem, and conclude with a practical view of analytics in newsrooms. (shrink)

This open access book explains how leading business organizations attempt to achieve the responsible and ethical use of artificial intelligence (AI) and other advanced information technologies. These technologies can produce tremendous insights and benefits. But they can also invade privacy, perpetuate bias, and otherwise injure people and society. To use these technologies successfully, organizations need to implement them responsibly and ethically. The question is: how to do this? Data ethics management, and this book, provide some answers. -/- The authors (...) interviewed and surveyed data ethics managers at leading companies. They asked why these experts see data ethics as important and how they seek to achieve it. This book conveys the results of that research on a concise, accessible way. -/- Much of the existing writing on data and AI ethics focuses either on macro-level ethical principles, or on micro-level product design and tooling. The interviews showed that companies need a third component: data ethics management. This third element consists of the management structures, processes, training and substantive benchmarks that companies use to operationalize their high-level ethical principles and to guide and hold accountable their developers. Data ethics management is the connective tissue makes ethical principles real. It is the focus of this book. -/- This book should be of use to organizations that wish to improve their own data ethics management efforts, legislators and policymakers who hope to build on existing management practices, scholars who study beyond compliance business behavior, and members of the public who want to understand better the threats that AI poses and how to reduce them. (shrink)

Acceptable analyticities, i.e. contradictions or tautologies, constitute problematic evidence for the idea that language includes a deductive system. In recent discussion, two accounts have been presented in the literature to explain the available evidence. According to one of the accounts, grammatical analyticities are accessible to the system but a pragmatic strengthening repair mechanism can apply and prevent the structures from being actually interpreted as contradictions or tautologies. The proposed data, however, leaves it open whether other versions of the meaning (...) modulation operation are required. Novel evidence we present argues that a loosening version of the repair mechanism must be available. Our observation concerns acceptable lexical contradictions that cannot be rescued if only a strengthening version of the pragmatic strategy is available. (shrink)

It was in the offices of administrative statistics that data work was held in the nineteenth century, where the most massive amount of numerical information was processed. Reconstructing the know-how of this era leads to the formation of the theory of the average and its international dissemination. Indeed, it not only governed the calculations but also the organization of these offices. Once sealed in the work of the astronomer Adolphe Quetelet and promoted internationally, mainly in Europe, this theory of (...) the average became the key to the statistical know-how of that century. It went hand in hand with presuppositions on the dispersion of observations today considered as obsolete. We can therefore put an end to the legend of the universality of the law of large numbers. (shrink)

This section is dedicated to the securing and the analysis of data obtained from expert interviews. It discusses the different steps that have to be taken in order to ensure that the data collected are systematically tied back to the conceptual framework of the research project.

An analytic cognitive style denotes a propensity to set aside highly salient intuitions when engaging in problem solving. We assess the hypothesis that an analytic cognitive style is associated with a history of questioning, altering, and rejecting supernatural claims, both religious and paranormal. In two studies, we examined associations of God beliefs, religious engagement, conventional religious beliefs and paranormal beliefs with performance measures of cognitive ability and analytic cognitive style. An analytic cognitive style negatively predicted both religious and paranormal beliefs (...) when controlling for cognitive ability as well as religious engagement, sex, age, political ideology, and education. Participants more willing to engage in analytic reasoning were less likely to endorse supernatural beliefs. Further, an association between analytic cognitive style and religious engagement was mediated by religious beliefs, suggesting that an analytic cognitive style negatively affects religious engagement via lower acceptance of conventional religious beliefs. Results for types of God belief indicate that the association between an analytic cognitive style and God beliefs is more nuanced than mere acceptance and rejection, but also includes adopting less conventional God beliefs, such as Pantheism or Deism. Our data are consistent with the idea that two people who share the same cognitive ability, education, political ideology, sex, age and level of religious engagement can acquire very different sets of beliefs about the world if they differ in their propensity to think analytically. (shrink)

The paper by Dupras and Bunnik is a useful addition to the literature on privacy in regards to datasets of human tissue/materials. In particular, the paper addresses import...

Analytic Philosophy: The History of an Illusion argues that the crisis is deeper and more longstanding than is usually recognized. Synthesizing data from early and recent studies as well as from canonical primary texts, it argues (1) that analytic philosophy has never involved significant agreement on substantive philosophical views, and thus that it has always been in this state of crisis, (2) that this fact was long hidden by the illusion that analytic philosophy was originally united in the metaphilosophical (...) thesis that philosophy is linguistic analysis, and (3) that both the rise of analytic philosophy under this illusion and the preservation of its privileged status since the illusion's demise have been facilitated by a scientistic 'stance' that minimizes the traditional philosophical duty to examine one's most fundamental assumptions. (shrink)

This essay reviews _Cultural Analytics_ by Lev Manovich as a foundational text that not only charts the historical and technical emergence of cultural analytics but also provokes a deeper interrogation of how digital and generative AI-driven methods reconfigure the very ontology, cognition, and ethics of cultural inquiry. Cultural analytics combines computer science, data visualization, and media arts to study cultural phenomena at scale; generative AI further extends this paradigm by producing new artifacts that blur boundaries of authorship (...) and challenge traditional categories. This review, following Manovich’s admonition throughout the book, concludes that that meaningful human-AI co-creative cultural studies require both advanced computational methods and philosophically informed safeguards, ensuring that the richness of human experience and the agency of communities remain central. (shrink)

Many studies find reflective thinking predicts less belief in God or less religiosity — so-called analytic atheism. However, the most widely used tests of reflection confound reflection with ancillary abilities such as numeracy, some studies do not detect analytic atheism in every country, experimentally encouraging reflection makes some non-believers more open to believing in God, and one of the most common sources of online research participants seems to produce lower data quality. So analytic atheism may be less than universal (...) or partially explained by confounding factors. To test this, we developed better measures, controlled for more confounds, and employed more recruitment methods. All four studies detected signs of analytic atheism above and beyond confounds (N > 70,000 people from five of six continental regions). We also discovered analytic apostasy: the better a person performed on reflection tests, the greater their odds of losing their religion since childhood — even when controlling for confounds. Analytic apostasy even seemed to explain analytic atheism: apostates were more reflective than others and analytic atheism was undetected after excluding apostates. Religious conversion was rare and unrelated to reflection, suggesting reflection’s relationships to conversion and deconversion are asymmetric. Detected relationships were usually small, indicating reflective thinking is a reliable albeit marginal predictor of apostasy. (shrink)

Education at all levels is increasingly augmented and enhanced by data mining and analytics, catalysed by the growing prevalence of automated distance learning. With an unprecedented capacity to scale both horizontally and vertically, data mining and analytics are set to be a transformative part of the future of education. We reflect on the assumptions behind data mining and the potential consequences of learning analytics, with reference to an issue brief prepared for the U.S. Department (...) of Education entitled Enhancing Teaching and Learning Through Educational Data Mining and Learning Analytics. We argue that the associated gains conceal subtle, but important risks. Data-ism, an underpinning paradigm, assigns unjustified veracity to data-driven science and the application of personalised analytics may compromise individual privacy, agency and inventiveness. This holds serious ethical implications, particularly when considering the impact on minors, rendering wholesale adoption premature. (shrink)

At least since W. V. O. Quine's famous critique of the analytic/synthetic distinction, philosophers have been deeply divided over whether there are any analytic truths. One line of thought suggests that the simple fact that people have ' intuitions of analyticity' might provide an independent argument for analyticities. If defenders of analyticity can explain these intuitions and opponents cannot, then perhaps there are analyticities after all. We argue that opponents of analyticity have some unexpected resources for explaining these intuitions and (...) that, accordingly, the argument from intuition fails. (shrink)

The COVID-19 pandemic has impacted data collection for longitudinal studies in developmental sciences to an immeasurable extent. Restrictions on conducting in-person standardized assessments have led to disruptive innovation, in which novel methods are applied to increase participant engagement. Here, we focus on remote administration of behavioral assessment. We argue that these innovations in remote assessment should become part of the new standard protocol in developmental sciences to facilitate data collection in populations that may be hard to reach or (...) engage due to burdensome requirements. We present a series of adaptations to developmental assessments and a detailed discussion of data analytic approaches to be applied in the less-than-ideal circumstances encountered during the pandemic-related shutdown. Ultimately, these remote approaches actually strengthen the ability to gain insight into developmental populations and foster pragmatic innovation that should result in enduring change. (shrink)

Higher education institutions are mining and analyzing student data to effect educational, political, and managerial outcomes. Done under the banner of “learning analytics,” this work can—and often does—surface sensitive data and information about, inter alia, a student’s demographics, academic performance, offline and online movements, physical fitness, mental wellbeing, and social network. With these data, institutions and third parties are able to describe student life, predict future behaviors, and intervene to address academic or other barriers to student (...) success (however defined). Learning analytics, consequently, raise serious issues concerning student privacy, autonomy, and the appropriate flow of student data. We argue that issues around privacy lead to valid questions about the degree to which students should trust their institution to use learning analytics data and other artifacts (algorithms, predictive scores) with their interests in mind. We argue that higher education institutions are paradigms of information fiduciaries. As such, colleges and universities have a special responsibility to their students. In this article, we use the information fiduciary concept to analyze cases when learning analytics violate an institution’s responsibility to its students. (shrink)

Analytic information is the data with syntax that has meaning for a business. Leaders need to have faith in the accuracy and truth of information to determine its value for decision-making. This chapter outlines three philosophical theories for establishing the truth of information. The correspondence theory of truth comes from the school of analytic philosophy where there is a tight semantic connection between the data of a message and its meaning. The coherence theory relies on its consistency with (...) related contextual information like climate change science, for instance. The pragmatic theory claims truth evolves during a process of debate within a team. All three theories have relevance to the management of information in a business. (shrink)