Experiential AI: Between Arts and Explainable AI
Top Cited Papers
Abstract
Experiential artificial intelligence (AI) is an approach to the design, use, and evaluation of AI in cultural or other real-world settings that foregrounds human experience and context. It combines arts and engineering to support rich and intuitive modes of model interpretation and interaction, making AI tangible and explicit. The ambition is to enable significant cultural works and make AI systems more understandable to nonexperts, thereby strengthening the basis for responsible deployment. This paper discusses limitations and promising directions in explainable AI, contributions the arts offer to enhance and go beyond explainability and methodology to support, deepen, and extend those contributions.Keywords
This publication has 17 references indexed in Scilit:
- AI in the Public Eye: Investigating Public AI Literacy Through AI Art2023
- Sensible AI: Re-imagining Interpretability and Explainability using Sensemaking Theory2022
- Expanding Explainability: Towards Social Transparency in AI systems2021
- One Explanation Does Not Fit AllKI - Künstliche Intelligenz, 2020
- Reframing the Role of Novelty within Social HRI: from Noise to Information2019
- Explanation in artificial intelligence: Insights from the social sciencesArtificial Intelligence, 2019
- Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making2018
- Methods for interpreting and understanding deep neural networksDigital Signal Processing, 2018
- “FearNot!”: a computer-based anti-bullying-programme designed to foster peer interventionEuropean Journal of Psychology of Education, 2010
- Experiential Knowledge: A New Concept for the Analysis of Self-Help GroupsSocial Service Review, 1976