Dual-Process Approach to the Problem of Artificial Intelligence Agency Perception

Abstract

Thanks to advances in machine learning in recent years the ability of AI agents to act independently of human oversight, respond to their environment, and interact with other machines has significantly increased, and is one step closer to human-like performance. For this reason, we can observe contemporary researchers’ efforts towards modeling agency in artificial systems. In this light, the aim of this paper is to develop a dual-process approach to the problem of AI agency perception, and to discuss possible triggers of various agency perceptions. The article discusses the agency attribution phenomenon, based on which the argument for the dual-process nature of agency perception is developed. Two distinct types of thinking (processing) involved in human reasoning on AI agency are suggested: Type 1 and Type 2. The first one is fast, automatic, routine, and often unconscious; the second is a slower, controlled, more conscious one. These two distinct types of processing can yield differing and sometimes conflicting results for human cognition and interaction. The preliminary philosophical findings may contribute to further investigations in philosophy of mind or cognitive psychology and could also be empirically tested in HCI and UX studies.