This paper revives phenomenological elements to have a better framework for addressing the implications of technologies on society. For this reason, we introduce the motto “back to the technologies themselves” to show how some phenomenological elements, which have not been highlighted in the philosophy of technology so far, can be fruitfully integrated within the postphenomenological analysis. In particular, we introduce the notion of technological intentionality in relation to the passive synthesis in Husserl’s phenomenology. Although the notion of technological intentionality has (...) already been coined in postphenomenology, it is “in tension” with the notion of technological mediation since there are still no clear differences between these two concepts and studies on how they relate one to another. The tension between mediation and intentionality arises because it seems intuitively reasonable to suggest that intentionality differs from mediation in a number of ways; however, these elements have not been clearly clarified in postphenomenology so far. To highlight what technological intentionality is and how it differs from mediation, we turn the motto “back to the things themselves” into “back to the technologies themselves,” showing how the technologies have to be taken into consideration by themselves. More specifically, we use the concept of passive synthesis developed by Husserl, and we apply it to technologies to show their inner passive activity. The notion of the passive synthesis enables to demonstrate how technologies are able to connect to a wider (technological) environment without the subjects’ activity. Consequently, we claim that technologies have their pole of action, and they passively act by themselves. (shrink)

This article advocates for a hermeneutic model for children-AI interactions in which the desirable purpose of children’s interaction with artificial intelligence systems is children's growth. The article perceives AI systems with machine-learning components as having a recursive element when interacting with children. They can learn from an encounter with children and incorporate data from interaction, not only from prior programming. Given the purpose of growth and this recursive element of AI, the article argues for distinguishing the interpretation of bias within (...) the artificial intelligence ethics and responsible AI discourse. Interpreting bias as a preference and distinguishing between positive and negative bias is needed as this would serve children's healthy psychological and moral development. The human-centric AI discourse advocates for an alignment of capacities of humans and capabilities of machines by a focus both on the purpose of humans and on the purpose of machines for humans. The emphasis on mitigating negative biases through data protection, AI law, and certain value-sensitive design frameworks demonstrates that the purpose of the machine for humans is prioritized over the purpose of humans. These top–down frameworks often narrow down the purpose of machines to do-no-harm and they miss accounting for the bottom-up views and developmental needs of children. Therefore, applying a growth model for children-AI interactions that incorporates learning from negative AI-mediated biases and amplifying positive ones would positively benefit children’s development and children-centric AI innovation. Consequently, the article explores: What challenges arise from mitigating negative biases and amplifying positive biases in children-AI interactions and how can a growth model address these? To answer this, the article recommends applying a growth model in open AI co-creational spaces with and for children. In such spaces human–machine and human–human value alignment methods can be collectively applied in such a manner that children can become sensitized toward the effects of AI-mediated negative biases on themselves and others; enable children to appropriate and imbue top-down values of diversity, and non-discrimination with their meanings; enforce children’s right to identity and non-discrimination; guide children in developing an inclusive mindset; inform top-down normative AI frameworks by children’s bottom-up views; contribute to design criteria for children-centric AI. Applying such methods under a growth model in AI co-creational spaces with children could yield an inclusive co-evolution between responsible young humans in the loop and children-centric AI systems. (shrink)

Many researchers from robotics, machine ethics, and adjacent fields seem to assume that norms represent good behavior that social robots should learn to benefit their users and society. We would like to complicate this view and present seven key troubles with norm-compliant robots: (1) norm biases, (2) paternalism (3) tyrannies of the majority, (4) pluralistic ignorance, (5) paths of least resistance, (6) outdated norms, and (7) technologically-induced norm change. Because discussions of why norm-compliant robots can be problematic are noticeably absent (...) from the robot and machine ethics literature, this paper fills an important research gap. We argue that it is critical for researchers to take these issues into account if they wish to make norm-compliant robots. (shrink)

Most accounts of responsibility focus on one type of responsibility, moral responsibility, or address one particular aspect of moral responsibility such as agency. This article outlines a broader framework to think about responsibility that includes causal responsibility, relational responsibility, and what I call “narrative responsibility” as a form of “hermeneutic responsibility”, connects these notions of responsibility with different kinds of knowledge, disciplines, and perspectives on human being, and shows how this framework is helpful for mapping and analysing how artificial intelligence (...) (AI) challenges human responsibility and sense-making in various ways. Mobilizing recent hermeneutic approaches to technology, the article argues that next to, and interwoven with, other types of responsibility such as moral responsibility, we also have narrative and hermeneutic responsibility—in general and for technology. For example, it is our task as humans to make sense of, with and, if necessary, against AI. While from a posthumanist point of view, technologies also contribute to sense-making, humans are the experiencers and bearers of responsibility and always remain in charge when it comes to this hermeneutic responsibility. Facing and working with a world of data, correlations, and probabilities, we are nevertheless condemned to make sense. Moreover, this also has a normative, sometimes even political aspect: acknowledging and embracing our hermeneutic responsibility is important if we want to avoid that our stories are written elsewhere—through technology. (shrink)

Most accounts of responsibility focus on one type of responsibility, moral responsibility, or address one particular aspect of moral responsibility such as agency. This article outlines a broader framework to think about responsibility that includes causal responsibility, relational responsibility, and what I call “narrative responsibility” as a form of “hermeneutic responsibility”, connects these notions of responsibility with different kinds of knowledge, disciplines, and perspectives on human being, and shows how this framework is helpful for mapping and analysing how artificial intelligence (...) (AI) challenges human responsibility and sense-making in various ways. Mobilizing recent hermeneutic approaches to technology, the article argues that next to, and interwoven with, other types of responsibility such as moral responsibility, we also have narrative and hermeneutic responsibility—in general and for technology. For example, it is our task as humans to make sense of, with and, if necessary, against AI. While from a posthumanist point of view, technologies also contribute to sense-making, humans are the experiencers and bearers of responsibility and always remain in charge when it comes to this hermeneutic responsibility. Facing and working with a world of data, correlations, and probabilities, we are nevertheless condemned to make sense. Moreover, this also has a normative, sometimes even political aspect: acknowledging and embracing our hermeneutic responsibility is important if we want to avoid that our stories are written elsewhere—through technology. (shrink)

Artificial intelligence (AI) profoundly influences a number of societal structures today, including religious dynamics. Using Bernard Lonergan’s critical realism as a lens, this article investigates the intersections of AI and religious traditions in their shared pursuit of the common good. Beginning with Lonergan’s principle that humans construct their understanding through cognitive processes, we examine how AI-mediated realities align with or challenge traditional religious tenets. By delving into specific cases, we spotlight AI’s role in reshaping religious symbols, rituals, and even creating (...) novel spiritual meanings. Using Lonergan’s insights on the balance between subjectivity and objectivity, I analyze AI’s potential to both create new sacred spaces and challenge religious orthodoxy. The crux of the discussion centers on the negotiation between religious values and technological innovation, assessing how AI can bolster religious life while maintaining its core essence. Ultimately, this article underscores the importance of the common good in the age of AI-driven religious evolution. (shrink)