信任动态和用户对推荐错误的态度：初步结果
Trust dynamics and user attitudes on recommendation errors: preliminary results
摘要：
基于人工智能的系统可以用作数字轻推技术，其可以引导或强迫用户做出并不总是与他们的真实兴趣一致的决定。当这样的系统正确地解决了公平、问责制、透明度和道德问题时，用户对系统的信任将取决于系统的输出。本文的目的是提出一个模型，探讨如何好的和坏的建议影响的整体信任在一个理想化的推荐系统，问题的建议，资源有限的能力。不同的用户的信任动态的态度的影响也被认为是。通过模拟，我们进行了大量的实验，可以观察到：1）在某些情况下，所有用户都结束了接受推荐; 2）用户态度（由一个单一的参数控制，在好/坏推荐后平衡信任的获得/损失）对信任动态有很大的影响。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
