Publikation
The Cake that is Intelligence and Who Gets to Bake it: An AI Analogy and its Implications for Participation
Martin Mundt; Anaelia Ovalle; Felix Friedrich; Pranav Agrawal; Subarnaduti Paul; Manuel Brack; Kristian Kersting; William Agnew
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2502.03038, Pages 1-21, Computing Research Repository, 2025.
Zusammenfassung
n 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.