This proposal emerges from the tensions residing in my artistic practice, serving as both the impetus for and the critical lens through which I explore the intersection of datafication and human emotions as socio-culturally mediated. Prompted by philosopher Hubert Dreyfus' contention that the nuanced, embodied nature of human experience eludes computational capture, this inquiry grapples with the gap between the truth-generational claim of emotional AI and multifaceted spectrum of human emotional landscape. Leveraging my artistic practice, this proposal serves as a critique but also as a creative interrogation of the processes by which emotions are quantified, datafied, processed, and classified using machine learning, a subfield of AI.
The case study central to this proposal, emerges from an extended engagement with my own emotional responses to triggering stimuli as a migrant in exile, meticulously logged over a period of six months. While leveraging the instrumentarium of emotional AI by processing the affective data using variational autoencoder (VAE), a generative algorithm, the project subsequently subverts the pragmatism of the field by building an immersive acoustic performance out of the training output supplemented with live data. This strategy, thus, stands as a methodological pivot, redirecting the predictive aspirations towards an evocative expression of complex emotional states using the affective language of sound.
Such subversion is built on hybridization between computational logic, critical perspectives from social sciences and humanities that comment on diversity of lived experiences, and creative force of artistic practice. The project, thus, engages with a ‘method assemblage' to develop computational and creative practices that are not only technically robust but also socially informed, ethically grounded, and deeply resonant with human experience.
One such method, ‘feminist objectivity', recognizes that technoscientific knowledge is always situated, materially grounded, and partial as a way to reorganize informatics perspectives uses ‘data visceralization' as an artistic technique. Data visceralization, a process enacted within my project employs artistic strategies to transform abstract data into experiential, sensory events to make them felt in the body.
In the case of this project, by underscoring the capacity of sound as a sensory medium to embody and convey complex emotional landscapes, data visceralization challenges disciplinary views on data representation such as data visualization. I take the method across its regenerative stance to intermingle embodied artistic strategies with data feminist ethics of care, to fuse the materiality of the body with contingencies of computation.
The ethics of care touches upon the use of custom and small-scale datasets for analysis and training to question big data that operates on the grounds of extreme capture and scale thinking. The last method, an artistic one, that I frame as ‘transduction' then acts as a kind of glue that mediates a space of sharp contrast between critical perspective of feminist objectivity with computation using event-making and experience-building capacities of artistic practice.
This proposal, thus, articulates an artistic vision for an integrative, nuanced approach to understanding and engaging with human emotions in the age of artificial intelligence. It calls for a reorientation away from big data practices of emotional AI towards methodologies that embrace the full material, socio-political, and experiential dimensions of emotional experience. Through a synthesis of artistic creation, interdisciplinary exploration, and critical reflexivity, this proposal aims to retrace the path from statistical operations and apparatuses of capture under the banner of AI to land back on and reactivate complex material forces that feeds their operation—in this case emotional tones collected from the body.