Experiential AI

Drew Hemment, Ruth Aylett, Vaishak Belle, Dave Murray-Rust, Ewa Luger, Jane Hillston, Michael Rovatsos, Frank Broz

Research output: Contribution to journalArticlepeer-review


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 in- telligence to greater transparency and collab- oration 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 transpar- ent and decipherable. Beyond this, artistic practice can explore new configurations of hu- mans and algorithms, mapping the terrain of inter-agencies between people and machines. This helps to viscerally understand the com- plex 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.
Original languageEnglish
Pages (from-to)25-31
Number of pages7
JournalAI Matters
Issue number1
Publication statusPublished - Apr 2019


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