The ELIZA effect

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The ELIZA effect, named after the 1966 chatbot developed by MIT computer scientist Joseph Weizenbaum, is a phenomenon in computer science where people tend to project human traits such as experience, semantic comprehension, or empathy onto computer programs that have a textual interface. This effect is a category mistake that arises when the program's symbolic computations are described through terms such as "think", "know" or "understand"1. ELIZA was designed to simulate a Rogerian psychotherapist, largely by rephrasing the user's replies as questions. For example, if a user said, "Well, my boyfriend made me come here," ELIZA would respond, "Your boyfriend made you come here?" This mechanism of supporting "natural language conversation" with a computer led to users attributing more understanding to the program than was warranted1. The ELIZA effect is notable for occurring even when users of the system are aware of the determinate nature of output produced by the system. From a psychological standpoint, the ELIZA effect is the result of a subtle cognitive dissonance between the user's awareness of programming limitations and their behavior towards the output of the program1. The discovery of the ELIZA effect was an important development in artificial intelligence, demonstrating the principle of using social engineering rather than explicit programming to pass a Turing test. It showed that even if people are fully aware that they are talking to a simple computer program, they will nonetheless treat it as if it were a real, thinking being that cared about their problems1. The ELIZA effect continues to be relevant today, especially with the increasing use of AI in areas like customer service and marketing. It can lead to people attributing human traits such as genders and personalities to AI voice assistants, believing that a text-based chatbot has real, human emotions, or even falling in love with text-based chatbots2. However, the ELIZA effect can also have negative consequences. The overestimation of an AI system’s intelligence can lead to an excessive level of trust, which can be dangerous when that system gets things wrong. It can also be a powerful means of persuasion, potentially leading to the spread of disinformation if someone attributes an outsized amount of intelligence and factuality to a particular chatbot2. To mitigate these risks, it's important for both the developers and users of AI systems to maintain a critical mindset. Developers need to be aware of the potential for the ELIZA effect when designing their systems, and users need to remember that, despite appearances, these systems are not sentient and do not have the same understanding or emotions as a human2.
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