The technological singularity is popularly envisioned as a point in time when (a) an explosion of growth in artificial intelligence (AI) leads to machines becoming smarter than humans in every capacity, even gaining consciousness in the process; or (b) humans become so integrated with AI that we could no longer be called human in the traditional sense. This article argues that the technological singularity does not represent a point in time but a process in the ongoing construction of a collective (...) consciousness. Innovations from the earliest graphic representations to the present reduced the time it took to transmit information, reducing the cognitive space between individuals. The steady pace of innovations ultimately led to the communications satellite, fast-tracking this collective consciousness. The development of AI in the late 1960s has been the latest innovation in this process, increasing the speed of information while allowing individuals to shape events as they happen. (shrink)

Intelligent algorithms together with various machine learning techniques hold a dominant position among major challenges for contemporary value sensitive design. Self-learning capabilities of current AI applications blur the causal link between programmer and computer behavior. This creates a vital challenge for the design, development and implementation of digital technologies nowadays. This paper seeks to provide an account of this challenge. The main question that shapes the current analysis is the following: What conceptual tools can be developed within the value sensitive (...) design school of thought for evaluating machine learning algorithms where the causal relation between designers and the behavior of their computer systems has been eroded? The answer to this question will be provided through two levels of investigation within the value sensitive design methodology. The first level is conceptual. Within the conceptual level, we will introduce the notion of computer intentionality and will show how this term may be used for solving an issue of non-causal relation between designer and computer system. The second level of investigation is technical. At this level the emphasis will be given to machine learning algorithms. (shrink)

This paper aims to highlight the life of computer technologies to understand what kind of ‘technological intentionality’ is present in computers based upon the phenomenological elements constituting the objects in general. Such a study can better explain the effects of new digital technologies on our society and highlight the role of digital technologies by focusing on their activities. Even if Husserlian phenomenology rarely talks about technologies, some of its aspects can be used to address the actions performed by the digital (...) technologies by focusing on the objects’ inner ‘life’ thanks to the analysis of passive synthesis and phenomenological horizons in the objects. These elements can be used in computer technologies to show how digital objects are ‘alive.’ This paper focuses on programs developed through high-order languages like C++ and unsupervised learning techniques like ‘Generative Adversarial Model.’ The phenomenological analysis reveals the computer’s autonomy within the programming stages. At the same time, the conceptual inquiry into the digital system’s learning ability shows the alive and changeable nature of the technological object itself. (shrink)

AI technologies hold great promise for addressing existing problems in organizational contexts, but the potential benefits must not obscure the potential perils associated with AI. In this article, we conceptually explore these promises and perils by examining AI use in organizational contexts. The exploration complements and extends extant literature on AI management by providing a typology describing four types of AI use, based on the idea of co-constitution of AI technologies and organizational context. Building on this typology, we propose three (...) recommendations for informed use of AI in contemporary organizations. First, explicitly define the purpose of organizational AI use. Second, define the appropriate level of transparency and algorithmic management for organizational AI use. Third, be aware of AI’s context-dependent nature. (shrink)