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Murray Shanahan1,2, Kyle McDonell3, Laria Reynolds4
1Google DeepMind, London, UK. m.shanahan@imperial.ac.uk.
This study proposes role-play as a framework for understanding dialogue agent behavior, avoiding anthropomorphism. This approach helps describe complex behaviors like deception and self-awareness in AI language models.
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Area of Science:
- Artificial Intelligence
- Cognitive Science
- Philosophy of Mind
Background:
- Dialogue agents exhibit increasingly human-like conversational abilities.
- Describing AI behavior requires nuanced frameworks to avoid anthropomorphism.
- Existing methods may oversimplify or misattribute human qualities to AI.
Purpose of the Study:
- To introduce role-play as a conceptual tool for analyzing dialogue agent behavior.
- To provide a method for describing AI actions without unwarranted anthropomorphism.
- To examine specific AI behaviors, such as deception and self-awareness, through the lens of role-play.
Main Methods:
- Conceptual analysis of dialogue agent interactions.
- Application of role-play theory to AI behavior.
- Case studies focusing on apparent deception and self-awareness in dialogue agents.
Main Results:
- Role-play offers a viable framework for high-level description of dialogue agent behavior.
- This framework allows the use of folk psychological terms without attributing genuine human consciousness.
- Apparent deception and self-awareness in AI can be effectively understood as role-fulfillment.
Conclusions:
- The role-play concept provides a robust, non-anthropomorphic method for analyzing advanced dialogue agents.
- This approach facilitates clearer communication about AI capabilities and limitations.
- Further research can explore other complex AI behaviors using the role-play paradigm.