Consciousness in Artificial Intelligence? A Framework for Classifying Objections and Constraints

TL;DR

A taxonomical framework is developed for classifying challenges to the possibility of consciousness in digital artificial intelligence systems and for disambiguating between challenges to computational functionalism and challenges to digital consciousness, as well as between different ways of parsing such challenges.

Abstract

We develop a taxonomical framework for classifying challenges to the possibility of consciousness in digital artificial intelligence systems. This framework allows us to identify the level of granularity at which a given challenge is intended (the levels we propose correspond to Marr's levels) and to disambiguate its degree of force: is it a challenge to computational functionalism that leaves the possibility of digital consciousness open (degree 1), a practical challenge to digital consciousness that suggests improbability without claiming impossibility (degree 2), or an argument claiming that digital consciousness is strictly impossible (degree 3)? We apply this framework to 14 prominent examples from the scientific and philosophical literature. Our aim is not to take a side in the debate, but to provide structure and a tool for disambiguating between challenges to computational functionalism and challenges to digital consciousness, as well as between different ways of parsing such challenges.

Figures (1)

• Figure 1: Functionally equivalent networks with different $\Phi$-structures. (A) The input–output function realized by three different systems (shown in (C)): a count of eight instances of input I = 1 leads to output O = 1. (B) The global state-transition diagram is also the same for the three systems: if I = 0, the systems will remain in their current global state, labeled as 0-7; if I = 1, the systems will move one state forward, cycling through their global states, and activate the output if S = 0. (C) Three systems constituted of three binary units but differing in how the units are connected and interact. As a consequence, the one-to-one mapping between the 3-bit binary states and the global state labels differ. However, all three systems initially transition from 000 to 100 to 010. Analyzed in state 100, the first system (top) turns out to be a single complex that specifies a $\Phi$-structure with six distinctions and many relations, yielding a high value of $\Phi$. The second system (middle) is also a complex, with the same $\Phi$s value, but it specifies a $\Phi$-structure with fewer distinctions and relations, yielding a lower value of $\Phi$. Finally, the third system (bottom) is reducible ($\Phi$s = 0) and splits into two smaller complexes (entities) with minimal $\Phi$-structures and low $\Phi$.