论文标题
信任动态和用户对建议错误的态度：初步结果
Trust dynamics and user attitudes on recommendation errors: preliminary results
论文作者
论文摘要
基于人工智能的系统可以用作数字化纽约技术，可以引导或强迫用户做出决定并不总是与他们的真正利益保持一致的决定。当此类系统正确解决公平，问责制，透明度和道德问题时，系统中用户的信任将取决于系统的输出。本文的目的是提出一个模型，以探讨好和坏建议如何影响理想化的推荐系统的整体信任，该系统对资源提出建议的建议有限。还考虑了不同用户态度对信任动态的影响。使用仿真，我们进行了大量实验，可以观察到：1）在某些情况下，所有用户最终都接受了建议； 2）用户态度（通过单个参数平衡好/坏建议后的增益/损失）对信任动态产生了很大的影响。
Artificial Intelligence based systems may be used as digital nudging techniques that can steer or coerce users to make decisions not always aligned with their true interests. When such systems properly address the issues of Fairness, Accountability, Transparency, and Ethics, then the trust of the user in the system would just depend on the system's output. The aim of this paper is to propose a model for exploring how good and bad recommendations affect the overall trust in an idealized recommender system that issues recommendations over a resource with limited capacity. The impact of different users attitudes on trust dynamics is also considered. Using simulations, we ran a large set of experiments that allowed to observe that: 1) under certain circumstances, all the users ended accepting the recommendations; and 2) the user attitude (controlled by a single parameter balancing the gain/loss of trust after a good/bad recommendation) has a great impact in the trust dynamics.
