Dehumanizing Machines: Mitigating Anthropomorphic Behaviors in Text Generation Systems
This addresses concerns about harmful effects of human-like AI outputs for users and society, but is incremental as it builds on prior work without introducing new methods.
The paper tackled the problem of mitigating anthropomorphic behaviors in text generation systems to prevent harmful outcomes like user over-reliance, by compiling an inventory of interventions from literature and crowdsourcing, and developing a conceptual framework for evaluating them.
As text generation systems' outputs are increasingly anthropomorphic -- perceived as human-like -- scholars have also increasingly raised concerns about how such outputs can lead to harmful outcomes, such as users over-relying or developing emotional dependence on these systems. How to intervene on such system outputs to mitigate anthropomorphic behaviors and their attendant harmful outcomes, however, remains understudied. With this work, we aim to provide empirical and theoretical grounding for developing such interventions. To do so, we compile an inventory of interventions grounded both in prior literature and a crowdsourcing study where participants edited system outputs to make them less human-like. Drawing on this inventory, we also develop a conceptual framework to help characterize the landscape of possible interventions, articulate distinctions between different types of interventions, and provide a theoretical basis for evaluating the effectiveness of different interventions.