General Intelligence: An Ecumenical Heuristic for Artificial Consciousness Research?

The science of consciousness has advanced but faces difficulty reaching consensus about artificial consciousness. Practical and ethical questions about whether artificial systems can suffer may soon arise, yet methods for assessing consciousness in humans and animals do not straightforwardly apply to artificial systems. The author proposes developing ecumenical heuristics for artificial consciousness that are intuitively plausible, theoretically neutral, and scientifically tractable. General intelligence—understood as robust, flexible, integrated cognition and behavior—is argued to satisfy these criteria and could provide a basis for making tentative assessments of which artificial systems are most likely to be conscious.

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Abstract

The science of consciousness has made great strides in recent decades. However, the proliferation of competing theories makes it difficult to reach consensus about artificial consciousness. While for purely scientific purposes we might wish to adopt a ‘wait and see’ attitude, we may soon face practical and ethical questions about whether, for example, agents artificial systems are capable of suffering. Moreover, many of the methods used for assessing consciousness in humans and even non-human animals are not straightforwardly applicable to artificial systems. With these challenges in mind, I propose that we look for ecumenical heuristics for artificial consciousness to enable us to make tentative assessments of the likelihood of consciousness arising in different artificial systems. I argue that such heuristics should have three main features: they should be (i) intuitively plausible, (ii) theoretically-neutral, and (iii) scientifically tractable. I claim that the concept of general intelligence — understood as a capacity for robust, flexible, and integrated cognition and behavior — satisfies these criteria and may thus provide the basis for such a heuristic, allowing us to make initial cautious estimations of which artificial systems are most likely to be conscious.