Can Machines Philosophize?

TL;DR

This paper introduces a new framework inspired by the Turing test to evaluate how well machines can mirror human philosophical views, demonstrated through a survey on scientific realism involving AI-generated machine personas and human respondents.

Contribution

The paper presents a novel methodological framework for assessing machine philosophical views by impersonation and statistical analysis, applied to scientific realism.

Findings

Machines' philosophical views are similar to humans' on average.

Machines show weaker scientific realism than humans.

Machines exhibit more coherence in their philosophical positions.

Abstract

Inspired by the Turing test, we present a novel methodological framework to assess the extent to which a population of machines mirrors the philosophical views of a population of humans. The framework consists of three steps: (i) instructing machines to impersonate each human in the population, reflecting their backgrounds and beliefs, (ii) administering a questionnaire covering various philosophical positions to both humans and machines, and (iii) statistically analyzing the resulting responses. We apply this methodology to the debate on scientific realism, a long-standing philosophical inquiry exploring the relationship between science and reality. By considering the outcome of a survey of over 500 human participants, including both physicists and philosophers of science, we generate their machine personas using an artificial intelligence engine based on a large language model. We reveal that the philosophical views of a population of machines are, on average, similar to those endorsed by a population of humans, irrespective of whether they are physicists or philosophers of science. As compared to humans, however, machines exhibit a weaker inclination toward scientific realism and a stronger coherence in their philosophical positions. Given the observed similarities between the populations of humans and machines, this methodological framework may offer unprecedented opportunities for advancing research in the empirical social sciences by complementing human participants with their machine-impersonated counterparts.