Two lines of development can be used to assess the extent to which computers have become “emotional” in the history of modern digitality. The first concerns the growing—and increasingly affective—significance of digital devices for both society and individuals. Over time, users began to integrate these technologies not only into professional contexts but also into their private lives, forming emotional attachments to them. The second line marks a shift within computer science itself, exemplified by the emergence of Affective Computing: digital devices were endowed with emotion-recognition capacities and other affect-related functionalities. Taken together, these two developments give rise to a history of attributions, some extending across entire discourses—such as those surrounding robotics and artificial intelligence. Central questions include whether, and under what conditions, users perceive technological artifacts as particular kinds of actors, or even as entities possessing subjective standpoints, sensations such as pleasure and pain, and emotional states (Dreksler et al. 2025; Weizenbaum 1977). This special issue of “Philosophy & Digitality” focuses primarily on the second line of development, Affective Computing, while this introduction also highlights the points at which the two strands of development overlap.

Computers are true shapeshifters. As “nearly universal” machines, modern electronic computers have evolved into many forms and functions, from large supercomputers in research facilities to television sets with 24/7 access to streaming services to pocket sized mobile phones and others (Haigh and Ceruzzi 2021, 3). In addition, computers have been able to synthesize many types of older media such as telephone, television, film, photography, and telegraphy into a widely distributed technological infrastructure. Modern electronic digital computers are extremely versatile media that break down barriers between leisure and work, between private and public space as well as bodies and their environment, with both good and problematic results.

Today, digital computers are no longer designed exclusively as technological tools for solving specific scientific, administrative, or military problems, as was the case when they were first conceived in the 1930s and 1940s (Galison 1994; Gugerli 2018). Computers are not just problem-solving machines, but are aligned with, or even connected to, the human body, and with this, indirectly, if you want to say so, to the human “soul” by processing individual sensory experiences. A standard smartphone alone is packed with more than a dozen microsensors and is at least as much a “measuring station” as a telephone (Gramelsberger 2023, 12–13). Based on data collected from various sources, computers started more and more to technologically detect, and, as some say, to “recognize” human expressions and behavior as representations of internal affects and emotional states (Picard and Klein 2002).

In the form of networked smartphones, computer technology has undoubtedly developed from expensive calculators for very few people in 1950 to affordable companions for many in 2025. This development was accompanied by a process of such devices getting more and more emotionally relevant for users (Turkle 2017). No longer seen as just “giant brains” (Berkeley 1949), but also as emotionally expressive and reactive devices, computers are now objects that are loved and hated in more intense ways than other technical objects. Over the course of a few decades, computers have become able not only to stimulate, but also simulate emotions as well as to detect them. At the same time they have become emotionally as well as socially significant in many ways (Weber-Guskar 2021). As miniaturized objects of our daily use, they carry personal meaning, for example when we archive messages from lost loved ones on them or when we are annoyed by them because work constantly reaches us through their communication channels. Computers are no longer just complicated and often seemingly boring high technology as they were in their early days, but they are emotionally charged.

However, for almost fifty years, from the mid-20th century to the mid-1990s, computers and emotions were—at least at first glance—separate areas of research. Therefore, detecting, processing or even simulating human emotions (their physiological precipitations or equivalents) are still relatively new areas of computer science. They were first introduced in the 1990s under the name Affective Computing at MIT in response to the “emotion turn” in neuroscience and have since achieved several milestones (Damasio 1994; LeDoux 1996; Picard 1997, 10–11), such as Rafael Calvo states in this issue.

According to Rosalind Picard’s early description of the approach, Affective Computing is “computing that relates to, arises from, or deliberately influences emotions” (Picard 1997, 3; Vgl. 1995, 1). Affective Computing experienced a major breakthrough around 2010, when facial recognition was advanced and more wearable sensor technology became available to measure physical changes such as skin resistance, thermal patterns, heart rate, and other emotionally significant parameters (Picard et al. 2016). In parallel with the development of the technological infrastructure, the research field was further established academically with influential journals such as the IEEE Transactions on Affective Computing and the Sage Journal Emotion Review (Institute of Electrical and Electronics Engineers 2010; Dalgleish et al. 2009), to name a few. Affective Computing is an interdisciplinary research approach at the intersection of computer science and the humanities, particularly behavioral sciences and emotion psychology. In 2014, Rafael Calvo and colleagues published the Oxford Handbook on Affective Computing, which highlights the strong connection between engineering on the one hand and behavioral research, especially emotional psychology, on the other hand. This standard work also identifies and considers the numerous areas of application—from learning to games to health care (D’Mello and Graesser 2015; Yannakakis and Paiva 2015; Bickmore 2015). In the 2014 introduction, the editors write accordingly:

„As we write, Affective Computing (AC) is about to turn 18. Though relatively young but entering the age of maturity. AC is a blossoming multidisciplinary field encompassing computer science, engineering, psychology, education, neuroscience, and many other disciplines. AC research is diverse indeed.”(Calvo et al. 2014, 1)

More than ten years later, AC has not only matured further, but has also grown in diversity. The approaches and applications of Affective Computing have become more differentiated since 2014, including ethical and normative issues (Ahmadpour et al. 2025). Not only have application bundles with different areas of significance established themselves, ranging from communication in psychotherapy and medical care (e.g. depression detection) to surveillance of public spaces, intelligent driving, and advertising to highly controversial interview programs in migration control (Sanchez-Monedero and Dencik 2022). But also, Affective Computing has merged with other fields, above all that of machine learning and AI.

In the words of the representatives of Affective Computing like Rana el Kaliouby or Ana Paiva, digital technology meanwhile should become “emotionally intelligent” and “empathic” ((Kaliouby 2017), (Paiva et al. 2021)) or at least “artificially empathic” (Misselhorn 2021)—an expression much contested in this context (Weber-Guskar 2022; Montemayor et al. 2022; Malinowska 2021). The discussion about empathy in media and technology use has by no means been limited to the field of Affective Computing, but in recent years has also included VR technology and artificial intelligence as a whole (McStay 2018; Nakamura 2020; Barbot and Kaufman 2020; Messeri 2024). In Affective Computing, however, the question of the desired or rejected combination, confrontation, or separation of human emotionality and affectivity on the one hand and technical systems on the other comes to a head (Tuschling 2014).

Affective Computing as an approach to computer development with multiple applications has become a discursive showcase as well for critically analyzing different forms of the relationship between human emotionality and digital technology in all its (normative) consequences, as well as for predicting massive changes in them. This special issue aims to offer a current cross-section of some of the observing disciplines involved. The focus is less on formulating a single position than on carefully clarifying some premises of the debates that have been conducted.

The ability to experience emotions, to understand the emotions of others and empathise with them is still widely regarded as a uniquely human characteristic. Consequently, the measurement and processing of affect-related data and pattern-recognition of emotionality, which form the core of Affective Computing, represent, in the eyes of many observers, a transgression of one of the last boundaries separating man and machines (Goldie et al. 2011, 728). Unsurprisingly, Affective Computing raised normative concerns from early on, as it claims to develop “emotionally intelligent” technologies with multiple applications in many parts of the life world (Bösel 2021; Angerer and Bösel 2015; Picard 2003; Hudlicka et al. 1999). In the following, we will distinguish between ethical and conceptually normative concerns, in which the interdisciplinary approaches of this special issue both differ and converge. While ethical concerns focus on moral issues in the narrower sense, conceptually normative concerns relate more strongly to the media-technological conditions of Affective Computing, as we will now briefly explain.

Unlike artificial intelligence in general, Affective Computing has not yet directly aimed at the strongest version of itself, namely the development of machines with conscious affective states and emotional experiences (Picard 1997, 60 f. See also Rafael Calvo in this issue; Toda 1962). When it has focused on the generation of emotions, this has meant modeling certain functional features of emotions, such as their connection to actions and evaluations (Ortony et al. 2022), but it has not aimed at what is considered the core of genuine (as opposed to artificial) human and animal emotions, namely the felt quality of emotions, which is a conscious experience.

At the end of the 19th century, a controversy arose over the scientific and technical representability of individual experience and feeling, which is primarily associated with Emil Du Bois-Reymond, the renowned physiologist and natural scientist. The controversy, also known as the Ingoramus et Ignorabimus dispute, dates back to a lengthy public lecture of him in 1872. Du Bois-Reymond defined two boundaries of modern science that are still being discussed today (Du Bois-Reymond 1912). One limit to the knowledge of nature is the origin of matter and force, the other is consciousness and feeling. This second limit to the knowledge of nature—that of consciousness and feeling—is gaining new relevance in view of the advances in artificial intelligence and the debate about sentient large language models (Chalmers 2024; 2023). Du Bois-Reymond mentions not only cognition and conscious perception as characteristics of consciousness, but also feeling (as does Antonio Damasio and others, today, too). Against the radical physicalism of his time, which in his eyes wanted to declare consciousness and a sense of self to be epiphenomena of purely cognitive brain activity, he states:

“What conceivable connection exists between certain movements of certain atoms in my brain on the one hand, and on the other hand the facts that are original for me, cannot be further defined, cannot be denied: I feel pain, feel pleasure; I taste sweet, smell the scent of roses, hear the sound of an organ, see red, ...” (Du Bois-Reymond 1912, 458).

It is precisely this boundary of recognizing nature in the form of consciousness, feeling and perception of the self that modern Affective Computing and Emotion AI seem, at first sight, to be overcoming or at least making surmountable.

However, Du Bois-Reymond’s speech serves less to pose the general question of technical simulatability and the intentional or systematic limits of science and artificial intelligence, which has been linked to his scientific imperatives to acknowledge these boundaries with a pledge to Ignoramus et Ignorabimus—we don’t know and won’t know—and discussed controversially (Bayertz 2007; Wahsner 2007; Bieri 1994; Nagel 1974; Du Bois-Reymond 1912, 464). Rather, the exact wording of Du Bois-Reymond’s speech reveals general basic assumptions about feeling, emotion and sensation that are virulent in the discourses on Affective Computing and Emotion AI. Du Bois-Reymond declares pain and pleasure, taste, smell, sound and color to be inescapable, unconditional given presuppositions of individual perception and experience that support our concept of individuality and self (Du Bois-Reymond 1912, 458). The physiologist links emotion, feeling and perception in an almost romanticized way with an immediate analogous experience, as we would say today.

Du Bois Reymond’s framing of emotion as scientifically incomprehensible—and therefore technically unrepeatable—is, however, what makes it possible to divide the various normative conceptual assessments of Affective Computing and Emotion AI: Are emotion and affect unquantifiable, non-discretizable and therefore non-computable? Or do Affective Computing and Emotion AI emulate what emotion and affect are sufficiently well? Or—a perspective pursued in some of the papers presented here—do we need to define more precisely what modern technology does and how it emerged, before we can evaluate it?

Affective Computing has often been interpreted in humanities and cultural studies as a possible overall vision of digital culture. From this macroperspective, criticism often focuses on large-scale normative questions about technical emotionality or empathy as a whole, about manipulation, deception, and surveillance. While these questions are of continuing importance, the impression nevertheless arises that the actual significance of Affective Computing for computer development is being missed and slipping away. While it cannot be ruled out that the one, totalizing killer application will still be developed, updating all dystopias and deception scenarios, after years of latency, the impression is rather that Affective Computing will not be THE one formation, but rather, unlike but not dissimilar to AI—and above all with AI—has developed into an essential particle of the texture of digital culture (see for a definition of digital: Tuschling 2025). It is not the grand scenario of control and general manipulation by Affective Computing that has become reality, but rather the small yet all the more solid integration of users into their digital environments and obligations.

Affective Computing has not only expanded as a mature approach. Rather, it has become woven into platform culture and last not least into AI, without simply being replaced by it. After all, Affective Computing has been combined with artificial neural networks and, in short, has merged with AI discursively, methodologically, and technically (Zhang et al. 2025). Side strands of Affective Computing-research include computer vision and certain cultural significant changes in expressing emotions in verbal, written, pictorial and other forms (Kutsuzawa et al. 2022). In general, Affective Computing has moved to the center of attention as a not insignificant building block of AI (Crawford 2021) (see Campolo in this issue).

The convergence of Affective Computing and AI raises questions about the normative implications of these technologies with new acuity. This special issue responds to this and brings together contributions that examine why and how Affective Computing should be classified, evaluated and problematized from various disciplinary perspectives.