# Experiential AI

## TL;DR

Experiential AI advocates for integrating art and science to enhance transparency, understanding, and collaboration in AI systems by making algorithms more visible and comprehensible through artistic practices.

## Contribution

It introduces a novel research agenda where art mediates between code and human understanding, fostering transparency and new human-AI configurations.

## Key findings

- Artists can visualize AI boundaries and reasoning.
- Artistic practices reveal complex causal chains in AI environments.
- Enhanced understanding of data collection and algorithm configuration.

## Abstract

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.