The benefits and drawbacks of data mining technologies

We have been witnessing a flurry of unprecedented developments in the technologies of data mining and their diverse applications. One can point to flagship projects of high visibility such as smart cities in which a variety of IT technologies, smart sensors, and human-centric interfaces play a pivotal role. In all of these applications, floods of data need to be stored, processed, and interpreted. The Internet of Things has shaped the current landscape of IT and influenced relationships between the technology and users. The advantages of all these projects exhibit far reaching and indisputable benefits in numerous areas of management, marketing, engineering, and manufacturing, including new manufacturing paradigms such as the Industry 4.0 initiative. Facial recognition is a new and booming technology with tangible benefits in biometrics, advertising, medical areas, and ways of identifying missing people. Innovative facial recognition by Facebook has opened new directions and unleashed new opportunities. The volume of surveillance cameras alone (in the United States itself amounting to 30 million; Vlahos, 2009) clearly points at the enormous volume of data. This technology has been designed mostly using white, Caucasian subjects; there is also a need to expand so that the wide range of global diversity is included. Data Mining and Knowledge Discovery serve as umbrella terms that host a truly remarkable spectrum of applications in all areas of human endeavors. By the same token, which is not surprising at all, the technology of data mining and knowledge discovery acts as a double-edged sword. The controversies around the use of the data and highly visible cases of violating data privacy have attracted public attention; one can refer to Cambridge Analytica or Epic Games. No doubt, data handling ethics scenarios are a legal and political minefield that calls for delicate balancing mechanisms between achieving benefits of data mining and preventing unethical practices. The lack of data mining ethics in various organizations at different levels has become a highly contentious issue. There are collections of different legal frameworks existing in various countries. What makes the situation worse, sometimes there is a lack of full understanding of possible implications of the usage of the rapidly progressing technologies. The academic community has been cognizant of these issues from the outset of emergence of the concept of knowledge discovery and technologies of data mining by proactively studying a variety of countermeasures including such approaches as adversarial learning or a suite of diverse privacy mechanisms, among others. One can point here to interesting studies and review materials on privacy preserving in data mining (Cuzzocrea, 2017) (Wang, Luo, Zhao, & Le, 2009), security (Xu, Jiang, Wang, Yuan, & Ren, 2014), legal aspects (Carmichael, Stalla-Bourdillon, & Staab, 2016), and general considerations on ethics and technology (Munoz, 2004). WIREs Data Mining and Knowledge Discovery was designed to be a comprehensive interdisciplinary resource on all subjects within the umbrella of Data Mining and Knowledge Discovery, including new types of technologies like facial recognition and smart cities. As the Editor-in-Chief, I am fully aware of the drawbacks of these technologies and take all necessary measures to diligently cope with this subject and bring ethical issues into the picture. We regard comprehensive discussions on new, potentially controversial technologies and their ethical implications as a mission equally important as the dissemination of high-quality technical knowledge about mining data. As a matter of fact, the journal has a topic dedicated to ethics in data mining with subtopics on fairness, privacy, and legal issues. We actively solicit articles and encourage article proposals on these important and timely topics. We are confident that with our rigorous editorial and peer review practices, we can provide a truly comprehensive resource on the technologies of Data Mining and Knowledge Discovery, the benefits, and the drawbacks.