The increased relevance of social media in our daily life has been
accompanied by efforts to manipulate online conversations and opinions.
Deceptive social bots -- automated or semi-automated accounts designed to
impersonate humans -- have been successfully exploited for these kinds of
abuse. Researchers have responded by developing AI tools to arm the public in
the fight against social bots. Here we review the literature on different types
of bots, their impact, and detection methods. We use the case study of
Botometer, a popular bot detection tool developed at Indiana University, to
illustrate how people interact with AI countermeasures. A user experience
survey suggests that bot detection has become an integral part of the social
media experience for many users. However, barriers in interpreting the output
of AI tools can lead to fundamental misunderstandings. The arms race between
machine learning methods to develop sophisticated bots and effective
countermeasures makes it necessary to update the training data and features of
detection tools. We again use the Botometer case to illustrate both algorithmic
and interpretability improvements of bot scores, designed to meet user
expectations. We conclude by discussing how future AI developments may affect
the fight between malicious bots and the public.

社交媒体中出现了越来越多的机器人欺诈行为，研究者们开发了不同类型的人工智能工具，以帮助公众对抗这些欺诈行为，其中包括 Botometer 等常用的机器人检测工具。本文综述了机器人的不同类型、它们的影响以及检测方法，并通过对 Botometer 的案例研究，详细讨论了 AI 检测工具在人们中的使用，同时谈到了在机器学习方法和反机器人攻击之间的必要性和影响。