The Cake that is Intelligence and Who Gets to Bake it: An AI Analogy and its Implications for Participation

TL;DR

This paper expands the AI analogy of a cake to encompass the entire AI development life-cycle, highlighting social implications and promoting cross-disciplinary understanding and participation.

Contribution

It introduces a comprehensive re-conceptualization of the AI life-cycle analogy, linking technical processes with social impacts and fostering inclusive dialogue.

Findings

Identifies social ramifications at each AI development stage.

Maps technical foundations to social outcomes.

Provides actionable recommendations for broader participation.

Abstract

In a widely popular analogy by Turing Award Laureate Yann LeCun, machine intelligence has been compared to cake - where unsupervised learning forms the base, supervised learning adds the icing, and reinforcement learning is the cherry on top. We expand this 'cake that is intelligence' analogy from a simple structural metaphor to the full life-cycle of AI systems, extending it to sourcing of ingredients (data), conception of recipes (instructions), the baking process (training), and the tasting and selling of the cake (evaluation and distribution). Leveraging our re-conceptualization, we describe each step's entailed social ramifications and how they are bounded by statistical assumptions within machine learning. Whereas these technical foundations and social impacts are deeply intertwined, they are often studied in isolation, creating barriers that restrict meaningful participation. Our re-conceptualization paves the way to bridge this gap by mapping where technical foundations interact with social outcomes, highlighting opportunities for cross-disciplinary dialogue. Finally, we conclude with actionable recommendations at each stage of the metaphorical AI cake's life-cycle, empowering prospective AI practitioners, users, and researchers, with increased awareness and ability to engage in broader AI discourse.