Digital Rights Management and Corporate Hegemony

To the extent that data constitutes creative work in a tangible form, it represents intellectual property and implicates copyright. Understanding data mining, thus, invokes an appreciation of the principle and law of copyright. With the advent of digital rights management (DRM), the discourse of the U.S. law of copyright has shifted from fair use to circumventions of copyright-protecting software by hackers. In light of the ever-rising market power of transnational media corporations, this essay engages the political philosophy of Antonio Gramsci to address a normative question of who ought to control protected content and to what extent. It identifies six inflection points for any effort to divest copyright law of the disproportionate influence of corporate lobbying. Underlying the discussion is a presumption that reform of the law of copyright through dialogue among key stakeholders – creative individuals, technology innovators, legislators, policy makers, international mediators, data miners and other scholars, and transnational corporations – would help preempt any Gramsci-prescribed socialist reaction to the exclusive copyright regime enabled by DRM.

Ethical and Legal Data Mining

In the process of data mining, techniques used, and results gathered, became ethically or even legally questionable due to the concern of the violation of individuals’ privacy when specific information is obtained, manipulated, and disseminated by other entities without an individual’s knowledge or consent. In other words, it is not the concern of what data is being mined, but more so regarding who is the miner of these data. This chapter is based on a review of the existing studies, which shows that not enough attention has been paid to the study of the “miner,” as well as the ethical and legal qualifications of the miner, from the perspectives of organizational development practitioners (OD) and organizational psychologists (OP) in approaching for certifying or licensing. During the review, several cases are being included to justify the certifying or licensing approach, thus upholding ethical data mining, especially in relation to the socio-economic development of a community, or county. This research concludes with a few practical recommendations for both data miner(s) and entities that are involved with data mining.

Spidering Scripts for Opinion Monitoring

With the advent of Web 2.0 technologies, online forms of communication are rich sources of data to study socio-economic growth patterns and consumer behaviours. In this research field, the more robust development of data mining and opinion monitoring depends on fully automating data collection to monitor the evolution of customer opinions and preferences in real time. Although web crawlers or spiders can assist researchers in an innovative and effective way, this data collection approach could give rise to ethical concerns on the cost of web crawling processes and on data protection and privacy. With a focus on opinion monitoring, the chapter aims to discuss the ethical and legal issues of data mining in relation to spidering scripts. This contribution proposes a detailed analysis of the ethical and legal aspects of online data collection by comparing existing legislations. For illustrative purposes, a spidering software is presented to discuss its potential and explore ethical solutions in the data-mining sphere.

Measuring Student Learning Responsibly

This chapter investigates an ethical mechanism of organizational measurement for student learning that is based on the learning analytics gathered from various learning-related activities over an extended period of time. In the context of today’s Web 2.0, such learning analytics are often collected from an electronic learning environment, such as a Web-based course management system (CMS), providing various tools of interest in scaffolding student learning: blogs, wikis, online forums, RSS, and many other innovative resources to facilitate learning online. This mechanism, intended to be ethically sound, could be considered as an instance of an accountability system typically installed in institutions of higher education and/or secondary schools, serving to gather evidence of student learning in a virtual learning environment involving electronic presence from both teachers and students in the context of learning development. It is understood that today’s university as a higher education institution (HEI) must put in place such an accountability system to measure student college experience, as her sustained commitment to continuous improvement in the quality of student learning; yet, without the context of data analysis, the transformation of any existing accountability infrastructure in support of assessment for student learning could hardly be innovated effectively, especially regarding the productivity and coordination of its staff, both academic and administrative. The question is how innovatively a HEI could establish such an accountability system to measure and assess student learning responsibly by collecting, analyzing, and interpreting student learning analytics designed into their various learning activities.

Social Science Data Analysis

Social scientists address some of the most pressing issues of society such as health and wellness, government processes and citizen reactions, individual and collective knowledge, working conditions and socio-economic processes, and societal peace and violence. In an effort to understand these and many other consequential issues, social scientists invest substantial resources to collect large quantities of data, much of which are not fully explored. This chapter proffers the argument that privacy protection and responsible use are not the only ethical considerations related to data mining social data. Given (1) the substantial resources allocated and (2) the leverage these “big data” give on such weighty issues, this chapter suggests social scientists are ethically obligated to conduct comprehensive analysis of their data. Data mining techniques provide pertinent tools that are valuable for identifying attributes in large data sets that may be useful for addressing important issues in the social sciences. By using these comprehensive analytical processes, a researcher may discover a set of attributes that is useful for making behavioral predictions, validating social science theories, and creating rules for understanding behavior in social domains. Taken together, these attributes and values often present previously unknown knowledge that may have important applied and theoretical consequences for a domain, social scientific or otherwise. This chapter concludes with examples of important social problems studied using various data mining methodologies including ethical concerns.

Data Mining Techniques to Improve Early Warning Systems across the Bay of Bengal

This chapter is a conceptual contribution to this book on data mining applications upholding ethical issues related to two extremely important aspects of the Bangladeshi population: the early warning system and the disaster management system. The chapter tries to provide a few conceptual ideas to introduce ethical data mining application in these systems to support the agencies that are involved for an improved, efficient, and transparent support system in the country, especially across the Bay of Bengal. Resembling a triangular shape (deltaic), a major portion of the bay touches the southern portion of Bangladesh. Sediments from rivers have made the bay a shallow sea. Due to its shallowness and shape, monsoon rains and cyclone storms become destructive, causing great loss of life along the southern part of the country. Moreover, the three mighty rivers (Padma, Jamuna, and Meghna) form one of the largest river systems in the world. They have a large number of distributaries and tributaries, which cause a major portion of the country to be inundated by monsoon rain. In addition, being the lowest landing zone of the Himalayan water, Bangladesh becomes victim to floods almost every year. Loss of lives, destruction of properties, suffering of numerous people and hampering of economic development have become part and parcel of Bangladeshi communities. This chapter suggests that the newly emerged data mining techniques can be introduced to collect, synthesize, analyze, archive, disseminate, and even make future forecasts forming a reliable early warning system across the Bay of Bengal.

Big Data Dilemmas

This chapter delineates the theory and practice of ethical big data mining for socio-economic development in four parts. The first part enunciates the ethical role of big data mining for socio-economic development by theorizing big data as a 20th Century phenomenon and its surging significance in the 21st Century digital era. The second part elucidates ethical values relating to big data mining with particular emphasis on the interplay of theory and practice. The third part connects classical theories of ethics to propose a code of conduct that relates to core ethical values such as privacy, confidentiality, objectivity, transparency, conflict of interest, and common good. The fourth and final part identifies privacy as a major challenge of ethical big data mining and postulates needed research directions. This chapter also features a list of additional reading and big data terms with concise definitions explicating their relevance to big data mining for socio-economic development.

Reuse of Excess Research Data for New Researches

The chapter presents a specific niche dilemma regarding the ethical aspects of utilization of data gathered for one specific research for another research, which was not specified to the data suppliers (sample) at the time of gathering, or that the data suppliers were not even informed of the possibility of such occurrence. That dilemma of the reuse of research data stems from motives that are also (among others) rooted in the aim to increase the public good, and the dilemma is between potential benefits and potential harm. As the forces that create the dilemma are growing in concurrence with current trends in research and research financing, the dilemma commands some attention, even if it seems at first glance to be minor compared to issues related to business data mining and governmental data bases. The discussion ends with a possible solution, but the reader is encouraged to think about the dilemma and understand it rather than “solve” it. The novice can regard this as an introduction to the dilemma while the experienced researcher will view it as a summary. However, this sort of research need to be supported to uphold the ethical aspects of data mining and their various applications in the socio-economic development processes of a country, such as generic or specific researches, entrepreneurship development through innovation, and trading, commerce or e-governance through the utilization of innovative technologies.

Ethical Data Mining and Social Science Data Exploration and Description

As data mining is the process of discovering significant, valuable, and interesting relationships in large and complex volumes of data (especially in data-enriched areas of socio-economic domains and in this socio-economic aspect of a society), data mining applications essentially act as effective instruments for providing support for measuring socio-economic pattern in a society. Although social and ethical matters are nowadays concerns to the society of which people are the only elements, in the days of technology innovations, computers are being manipulated with programs to act more like people, and eventually several social and ethical matters come into focus related to computer programming, or artificial intelligence. Researchers from nearly every social science discipline have found themselves in the position of simultaneously evaluating many questions, testing many hypotheses, or comparing many point estimates. In program evaluation, this arises, for instance, when comparing the impact of several different policy interventions; comparing the status of social indicators like test scores, poverty rates, teen pregnancy rates etc. across multiple schools, states, or countries; examining whether treatment effects vary meaningfully across different sub groups of the population; or examining the impact of a program on many different outcomes. Hence, the relevance of positioning of this chapter in a book of ethical data mining applications for socio-economic development of a community, society, or country fits well as the ethical data mining in social science research is crucial as such data information is highly useful in testing many of the hypotheses of economic or socio-economic in nature.

Improved Decision Support System to Develop a Public Policy to Reduce Dropout Rates for Four Minorities in a Society

This chapter proposes a decision support system applied to public schooling, especially for reducing dropout rates for minorities. That is relevant enough to enable understanding of ethical data mining in a strategic planning context. This understanding explains the importance of adequate different aspects related with Strategic Planning. The authors focus their analysis on a specific problem related with reducing dropout rates based on decision support systems under uncertainty. To this end, surveys are performed to gather information about this problem using Data Mining techniques to profile a number of behavioral patterns and choices that describe social behaviors. Ethical Data Mining is used for reasons of culture to improve the socio economic development. In addition, the chapter describes innovative models that capture salient variables of modernization, and how these variables give raise to intervening aspects that end up shaping behavioral patterns in ethical and social aspects. Finally, the chapter remarks and extends discussions of the authors’ approach and will provide general guidelines for future work in diverse application domains, including further analysis on how those public politics organize and operate.