Papers by Ekaterina Kormilitsyna
POM 2021 Conference proceedings, 2022
With the rise of artificial intelligence and related computational tools in everyday dealings wit... more With the rise of artificial intelligence and related computational tools in everyday dealings with knowledge organisation, production, and distribution, incl. for example archives and history- related applications, we’re concerned whether these computational methods 'colonize' and fundamentally change our common approaches to what constitutes studying and knowing a subject matter. We will unpack upon these concerns, looking at phenomena such as a lack of completion and categorisation in biodiversity archives, or new methods of creating artificial fossils as ways of filling gaps within historical datasets and potentially narratives. We also call back into how ontological architectures of computer science have emerged and how they defined ways in which knowledge is accessed. Via the examples of various case studies and thought experiments, the paper tries to examine the initial concern and predict its potential consequences, building upon the question as to what degree machine-learning-based approaches can augment our methods of analysis not just in history but in cultural behaviours.
In other words, how might computational models of ontology be producing an epistemological shift within the quality of knowing by imposing a knowledge system of references, linked nodes, hashtags, and databases that are never entirely complete in representing subjects they are set to define. Thus, asking if we shall hold on to our approaches of comprehension of things and their emergence or instead succumb to the generative, on-demand, a click away, always-at-your- fingertips forms of knowing and comprehending?
The use of machine-learning in historical analysis and reproduction as a scientific tool brings t... more The use of machine-learning in historical analysis and reproduction as a scientific tool brings to the forefront ethical questions of bias contamination within data and automation of its analysis. Via the examples of various confusing para-scientific interventions, including AI- based Voynich Manuscript decryptions as well as artistic investigation such as the speculative series Content Aware Studies, the paper examines the various sides of this inquiry and its consequences. It also looks into material repercussions of objects as synthetic documents of emerging machine-rendered histories. This text attempts to instrumentalise recent theoretical developments such as agential realism in analysis of computation in its advanced forms and their derivatives, including AI, its outputs, and their ontologies. The questions in focus of this text are what are the ethical, philosophical, and historical challenges we’re facing when using such automated means of knowledge-production and investigation? What epistemics do such methodologies hold by uncovering deeper and sharply unsuspected new knowledge or instead masking unacknowledged biases? The series Content Aware Studies is one of the key case studies as it vividly illustrates results of machine-learning technologies as means for automation and augmentation of historical and cultural documents, museology, and historiography taking speculative forms of restoration not only within historical and archaeological contexts but also in contemporary applications across machine vision and sensing technics, such as for example, LiDAR scanning. On the other side, these outputs provide a case study for critical examination through the lens of cultural sciences of potential misleading trajectories in knowledge production and epistemic focal biases that occur at the level of the described above applications and processes. Preoccupied with warnings and ontologies around biases, authenticities, and materialities it seeks vividly to illustrate them. As data in this text is seen as crude material and building blocks of inherent bias, the new materialist framework helps address these notions in a non-anthropocentric way, while seeking to locate the subjects of investigations as encounters between non- organic bodies. In the optics of a non-human agency of the AI- investigator, what of our historical knowledge and interpretation encoded into the datasets will survive this digital digestion? How are historical narratives, documents, their meaning, and function perverted when their analysis has been outsourced to machine vision and cognition? In other words, what happens to historical knowledge and documentation in the age of information-production epidemic and computational reality-engineering?
Drafts by Ekaterina Kormilitsyna
A research paper looking at depictions of adolescence and virtual reality in anime narratives.
... more A research paper looking at depictions of adolescence and virtual reality in anime narratives.
Written at Queen Mary, University of London
Uploads
Papers by Ekaterina Kormilitsyna
In other words, how might computational models of ontology be producing an epistemological shift within the quality of knowing by imposing a knowledge system of references, linked nodes, hashtags, and databases that are never entirely complete in representing subjects they are set to define. Thus, asking if we shall hold on to our approaches of comprehension of things and their emergence or instead succumb to the generative, on-demand, a click away, always-at-your- fingertips forms of knowing and comprehending?
Drafts by Ekaterina Kormilitsyna
Written at Queen Mary, University of London
In other words, how might computational models of ontology be producing an epistemological shift within the quality of knowing by imposing a knowledge system of references, linked nodes, hashtags, and databases that are never entirely complete in representing subjects they are set to define. Thus, asking if we shall hold on to our approaches of comprehension of things and their emergence or instead succumb to the generative, on-demand, a click away, always-at-your- fingertips forms of knowing and comprehending?
Written at Queen Mary, University of London