Menu

‘Board of Dinner’ XAI for Empathy project

Exhibition / Installation

Partner Programme

17 — 22 Sept 2024

Multi-Disciplinary Design, Other

17 Sept11:00—21:00

18 Sept11:00—21:00

19 Sept11:00—21:00

20 Sept11:00—21:00

21 Sept10:00—21:00

22 Sept10:00—21:00

In Person

Free, no ticket required


Online

Free, no ticket required

Borough Yards Unit 6

UNIT 006, BOROUGH YARDS, LONDON SE1 9AD

London

SE1 9AD

Can Explainable AI (XAI) gear us toward an empathetic future? Step into the 'Board of Dinner' AI menu/recipe generator, an interactive experience where you elect your board member, see a transparent 'board meeting' process, get inspired for your next meal, and perhaps discover the inner dialogue of others.

XAI for Empathy project is an invitation to reimagine AI's explainability as a force for empathy. Co-developed by the creative collective Deep Food and Joseph Wan (MA in Data-Driven Design, University of Applied Sciences Utrecht), the project presents an interactive installation called "Board of Dinner". The work features a ‘multiple agent AI’-powered Menu/Recipe Generator designed to be customisable in reflecting various values and transparent in exposing its thinking process. In a world where AI is often seen as performance-driven yet opaque, the Board of Dinner explores alternatives for unfolding AI's non-human ‘thinking’ mechanism in terms relatable to humans. Stepping into the installation, you will be invited to elect each member of your ‘Board of Dinner’. The ‘Board’ will make a suggestion for your next meal. You can try to include as many perspectives in your board as possible or even the opposite. As the Board generates your personalized menu suggestions alongside their meeting record, you may discover a unique understanding of your choice of human values from the lens of AI. To cook or not to cook; the experience starts with food and leads to issues beyond—it's about exploring the potential of AI to bridge human limitations in understanding and empathy. Can we steer AI to enhance trust and mitigate biases through transparency, or should we beware of the other way around? Join our creative experimentation and speculate on the good and bad of XAI together!