论文标题

提供解释的欺骗性AI系统与人机决策中的诚实AI系统一样令人信服

Deceptive AI Systems That Give Explanations Are Just as Convincing as Honest AI Systems in Human-Machine Decision Making

论文作者

Danry, Valdemar, Pataranutaporn, Pat, Epstein, Ziv, Groh, Matthew, Maes, Pattie

论文摘要

辨别真实信息和虚假信息之间的能力对于做出正确的决策至关重要。但是,随着基于AI的虚假信息运动的最新增加,了解欺骗性系统对人类信息处理的影响已经变得至关重要。在实验(n = 128)中,我们调查了易感人士对欺骗性的AI系统的欺骗性AI系统是如何通过研究识别假新闻的能力在将AI系统视为人类事实检查者或AI事实检查系统的能力以及这些事实检查者提供的解释时如何变化的。我们发现欺骗性的解释大大降低了准确性,这表明人们相信欺骗性的AI解释与诚实的AI解释一样。尽管在从AI系统获得帮助之前,人们的称重头条与真实头条相比,人们的加权辨别精度要高得多,但我们发现,在AI系统的帮助下,当对真实头条和虚假头条的诚实解释时,识别准确性显着提高,并且在欺骗对真实的头条新闻上的欺骗性解释时会显着下降。此外,与AI-Fact检查器相比,我们没有观察到来自人类事实检查器的解释之间的识别差异。同样,我们发现信任没有显着差异。这些发现例证了欺骗性AI系统的危险,以及寻找新颖的方法来限制其影响人类信息处理的需求。

The ability to discern between true and false information is essential to making sound decisions. However, with the recent increase in AI-based disinformation campaigns, it has become critical to understand the influence of deceptive systems on human information processing. In experiment (N=128), we investigated how susceptible people are to deceptive AI systems by examining how their ability to discern true news from fake news varies when AI systems are perceived as either human fact-checkers or AI fact-checking systems, and when explanations provided by those fact-checkers are either deceptive or honest. We find that deceitful explanations significantly reduce accuracy, indicating that people are just as likely to believe deceptive AI explanations as honest AI explanations. Although before getting assistance from an AI-system, people have significantly higher weighted discernment accuracy on false headlines than true headlines, we found that with assistance from an AI system, discernment accuracy increased significantly when given honest explanations on both true headlines and false headlines, and decreased significantly when given deceitful explanations on true headlines and false headlines. Further, we did not observe any significant differences in discernment between explanations perceived as coming from a human fact checker compared to an AI-fact checker. Similarly, we found no significant differences in trust. These findings exemplify the dangers of deceptive AI systems and the need for finding novel ways to limit their influence human information processing.

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