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Can Machines Philosophize?

Michele Pizzochero, Giorgia Dellaferrera

TL;DR

Can Machines Philosophize? introduces a Turing-inspired, population-level framework to test whether AI can reproduce the distribution of philosophical views found in human populations. It uses a three-step method: generate machine personas that impersonate each human in a target group, administer an identical philosophical questionnaire to humans and machines, and compare the resulting response distributions with rigorous statistics. In a case study on scientific realism, machine-generated responses align closely with human responses on average but show a weaker endorsement of realism and greater coherence across positions. The work suggests AI-assisted, population-scale methods can augment empirical social science by expanding sample sizes and enabling controlled cross-population comparisons across disciplines.

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.

Can Machines Philosophize?

TL;DR

Can Machines Philosophize? introduces a Turing-inspired, population-level framework to test whether AI can reproduce the distribution of philosophical views found in human populations. It uses a three-step method: generate machine personas that impersonate each human in a target group, administer an identical philosophical questionnaire to humans and machines, and compare the resulting response distributions with rigorous statistics. In a case study on scientific realism, machine-generated responses align closely with human responses on average but show a weaker endorsement of realism and greater coherence across positions. The work suggests AI-assisted, population-scale methods can augment empirical social science by expanding sample sizes and enabling controlled cross-population comparisons across disciplines.

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.

Paper Structure

This paper contains 3 sections, 1 figure.

Table of Contents

  1. Machines: Like or unlike humans?
  2. Philosophical views in the scientific realism debate
  3. Assessing the views of machines

Figures (1)

  • Figure 1: Schematic illustration of the methodology developed. Our methodology unfolds in three steps. First, a population of machines is prompted to impersonate a population of humans, reflecting the academic profile or philosophical beliefs of each individual. Second, each individual in the two populations is administered a questionnaire consisting of a list of statements covering various philosophical positions. Individuals are requested to rate their agreement with each statement by assigning an 'agreement score' on a scale ranging from 0% to 100%. Third, the results of the survey are statistically analyzed to quantify the analogies and differences in the philosophical views held by a population of humans and the corresponding population of machines.