Experiential AI

Experiential AI is proposed as a new research agenda in which artists and scientists come together to dispel the mystery of algorithms and make their mechanisms vividly apparent. It addresses the challenge of finding novel ways of opening up the field of artificial intelligence to greater transparency and collaboration between human and machine. The hypothesis is that art can mediate between computer code and human comprehension to overcome the limitations of explanations in and for AI systems. Artists can make the boundaries of systems visible and offer novel ways to make the reasoning of AI transparent and decipherable. Beyond this, artistic practice can explore new configurations of humans and algorithms, mapping the terrain of inter-agencies between people and machines. This helps to viscerally understand the complex causal chains in environments with AI components, including questions about what data to collect or who to collect it about, how the algorithms are chosen, commissioned and configured or how humans are conditioned by their participation in algorithmic processes.Comment: To appear in AI Matters 5(1): 25-31 (2019

Agency and legibility for artists through Experiential AI

Experiential AI is an emerging research field that addresses the challenge of making AI tangible and explicit, both to fuel cultural experiences for audiences, and to make AI systems more accessible to human understanding. The central theme is how artists, scientists and other interdisciplinary actors can come together to understand and communicate the functionality of AI, ML and intelligent robots, their limitations, and consequences, through informative and compelling experiences. It provides an approach and methodology for the arts and tangible experiences to mediate between impenetrable computer code and human understanding, making not just AI systems but also their values and implications more transparent, and therefore accountable. In this paper, we report on an empirical case study of an experiential AI system designed for creative data exploration of a user-defined dimension, to enable creators to gain more creative control over the AI process. We discuss how experiential AI can increase legibility and agency for artists, and how the arts can provide creative strategies and methods which can add to the toolbox for human-centred XAI.Comment: 1st International Workshop on Explainable AI for the Arts (XAIxArts), ACM Creativity and Cognition (C&C) 2023. Online, 3 pages. arXiv admin note: text overlap with arXiv:2306.0063

Experiential AI: A transdisciplinary framework for legibility and agency in AI

Experiential AI is presented as a research agenda in which scientists and artists come together to investigate the entanglements between humans and machines, and an approach to human-machine learning and development where knowledge is created through the transformation of experience. The paper discusses advances and limitations in the field of explainable AI; the contribution the arts can offer to address those limitations; and methods to bring creative practice together with emerging technology to create rich experiences that shed light on novel socio-technical systems, changing the way that publics, scientists and practitioners think about AI.Comment: 10 pages, 3 appendice

Towards a heuristic model for experiential AI:analysing the Zizi Show in the new real

Based on the rapid pace of evolving creative practice in AI arts, we identify and respond to an urgent need to develop frameworks for analysing the critical dimensions (including social/political) of this emerging field. This paper offers a comprehensive case study of The Zizi Show, by Jake Elwes, developed as part of The New Real and Experiential AI programme at the Edinburgh Futures Institute within the University of Edinburgh. Based on this case study analysis, we propose the structuring of distinct project characteristics into four categories (socio-cultural and institutional aspects; technology and media; experience and affect; and audience and impact) which form the basis for a heuristic model. The statements/descriptors collected in each category serve to capture creative and design strategies that can lead design processes from cultural and technological perspectives, enable projects’ cross-examination and evaluation and surface blindspots in the creative process

On creative practice and generative AI:Co-shaping the development of emerging artistic technologies

In recent years, advances in artificial intelligence (AI) and machine learning have given rise to powerful new tools and methods for creative practitioners. 2022–2023 in particular saw an explosion in generative AI tools, models and use cases. Noting the long history of critical arts engaging with AI, this chapter considers both the application of generative AI in the creative indus-tries, and ways in which artists co-shape the development of these emerging technologies. After reviewing the landscape of generative AI in visual arts, music and games, we propose four areas of critical interest for the future co-shaping of generative AI and creative practice in the areas of communi-ties and open source, deeper engagement with AI, beyond the human and cultural feedbacks

Towards Ontologically Grounded and Language-Agnostic Knowledge Graphs

Knowledge graphs (KGs) have become the standard technology for the representation of factual information in applications such as recommendation engines, search, and question-answering systems. However, the continual updating of KGs, as well as the integration of KGs from different domains and KGs in different languages, remains to be a major challenge. What we suggest here is that by a reification of abstract objects and by acknowledging the ontological distinction between concepts and types, we arrive at an ontologically grounded and language-agnostic representation that can alleviate the difficulties in KG integration.Comment: 7 pages, conference pape