AI-Driven Agents with Prompts Designed for High Agreeableness Increase the Likelihood of Being Mistaken for a Human in the Turing Test

TL;DR

This study demonstrates that AI agents with high agreeableness, designed through prompts, are more likely to be mistaken for humans in Turing Tests, emphasizing personality engineering's role in human-AI interaction.

Contribution

The paper introduces personality engineering in AI, showing that high agreeableness increases human-likeness and confusion rates in Turing Tests, bridging psychology and AI design.

Findings

Highly agreeable AI agents achieved over 60% confusion rate.

All tested AI agents exceeded 50% confusion rate in Turing Tests.

Personality traits influence human perception of AI as human-like.

Abstract

Large Language Models based on transformer algorithms have revolutionized Artificial Intelligence by enabling verbal interaction with machines akin to human conversation. These AI agents have surpassed the Turing Test, achieving confusion rates up to 50%. However, challenges persist, especially with the advent of robots and the need to humanize machines for improved Human-AI collaboration. In this experiment, three GPT agents with varying levels of agreeableness (disagreeable, neutral, agreeable) based on the Big Five Inventory were tested in a Turing Test. All exceeded a 50% confusion rate, with the highly agreeable AI agent surpassing 60%. This agent was also recognized as exhibiting the most human-like traits. Various explanations in the literature address why these GPT agents were perceived as human, including psychological frameworks for understanding anthropomorphism. These findings highlight the importance of personality engineering as an emerging discipline in artificial intelligence, calling for collaboration with psychology to develop ergonomic psychological models that enhance system adaptability in collaborative activities.