TransparencyExplainabilityAccountabilitycs.CY
Experiential AI
Drew Hemment, Ruth Aylett, Vaishak Belle, Dave Murray-Rust, Ewa Luger, Jane Hillston, Michael Rovatsos, Frank Broz
August 6, 2019
AI Matters
19 citations
Key Takeaways
-
Art can bridge the gap between technical complexity and public understanding: The paper proposes that artistic practice offers a more effective way to communicate how AI systems work than traditional technical explanations alone, helping non-experts grasp algorithmic mechanisms in tangible, visceral ways.
-
Transparency requires making invisible systems visible: Artists can reveal the "boundaries" and decision-making processes of AI systems that are typically hidden from users, addressing a key challenge in responsible AI by exposing what data is collected, how algorithms are selected, and how they're configured.
-
Collaboration between artists and scientists is essential for responsible AI: The paper argues that bringing together creative and technical expertise helps explore not just how AI works, but also the broader ethical questions about data collection, algorithmic choice, and the ways AI systems influence human behavior.
-
Understanding human-machine relationships requires new approaches: Beyond explaining individual algorithms, the research agenda emphasizes mapping how humans and machines interact and influence each other, helping people understand the "complex causal chains" in AI-driven environments rather than viewing algorithms as isolated technical tools.
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.