The ethics of uncertainty for data subjects

Modern health data practices come with many practical uncertainties. In this paper, I argue that data subjects’ trust in the institutions and organizations that control their data, and their ability to know their own moral obligations in relation to their data, are undermined by significant uncertainties regarding the what, how, and who of mass data collection and analysis. I conclude by considering how proposals for managing situations of high uncertainty might be applied to this problem. These emphasize increasing organizational flexibility, knowledge, and capacity, and reducing hazard

Integrating IoT Analytics into Marketing Decision Making: A Smart Data-Driven Approach

With the advent of the Internet of Things (IoT), businesses have gained access to vast amounts of data generated by interconnected devices. Leveraging IoT analytics and marketing intelligence, organizations can extract valuable insights from this data to enhance decision-making processes. This paper presents a comprehensive methodology for data-driven decision-making in the context of IoT analytics and marketing intelligence. A real-time example is used to illustrate the application of this methodology, followed by an inference and discussion of the results. The rise of IoT has enabled real-time data collection from a wide array of interconnected devices, offering unprecedented opportunities for businesses to gain actionable insights. This paper focuses on the intersection of IoT analytics and marketing intelligence, exploring how data-driven decision-making can empower organizations to optimize their marketing strategies, customer experiences, and overall business performance

Algorithmic Decision-Making Systems: A Conceptualization and Agenda for Green IS Research

Algorithmic decision-making systems (ADMSs), consisting of the two distinct but related concepts of artificial intelligence (AI) and big data analytics (BDA), represent the most current computing advances for decision-making. ADMSs are associated with significant opportunities and challenges in a wide range of high-impact application areas. However, the conceptual confusion around ADMSs limits information systems (IS) research in comprehensively studying them and their impacts within a clearly defined cumulative tradition. This literature review develops an inclusive conceptualization of ADMS through the ideas of AI and BDA to mitigate such shortcomings. The conceptualization of ADMS is inductively generated following a grounded theory approach used to analyze the content of 54 IS articles. The resulting conceptualization includes eleven key aspects representing the intricate socio-technical nature of current computing processes for decision-making. Lastly, a green IS research agenda is proposed to illustrate the applicability of the ADMS conceptualization to IS scholarship