In recent years Artificial Intelligence (AI) has gained much popularity, with the scientific community as well as with the public. Often, AI is ascribed many positive impacts for different social domains such as medicine and the economy. On the other side, there is also growing concern about its precarious impact on society and individuals, respectively. Several opinion polls frequently query the public fear of autonomous robots and artificial intelligence, a phenomenon coming also into scholarly focus. As potential threat perceptions arguably vary with regard to the reach and consequences of AI functionalities and the domain of application, research still lacks necessary precision of a respective measurement that allows for wide-spread research applicability. We propose a fine-grained scale to measure threat perceptions of AI that accounts for four functional classes of AI systems and is applicable to various domains of AI applications. Using a standardized questionnaire in a survey study (N = 891), we evaluate the scale over three distinct AI domains (medical treatment, job recruitment, and loan origination). The data support the dimensional structure of the proposed Threats of AI (TAI) scale as well as the internal consistency and factoral validity of the indicators. Implications of the results and the empirical application of the scale are discussed in detail. Recommendations for further empirical use of the TAI scale are provided.

近年来，人工智能（AI）在科学界和公众中都越来越受欢迎。通常，人工智能对医学和经济等不同的社会领域产生了许多积极影响。另一方面，人们也越来越担心其分别对社会和个人的不稳定影响。几项民意测验经常质疑公众对自动驾驶机器人和人工智能的恐惧，这一现象也成为学术界关注的焦点。由于潜在的威胁感知在AI功能的范围和后果以及应用领域方面可能有所不同，因此研究仍缺乏相应测量的必要精度，从而无法广泛应用研究。我们提出了一种细粒度的量表，用于衡量对AI的威胁感知，它涵盖了AI系统的四个功能类别，并且适用于AI应用程序的各个领域。在一项调查研究（N = 891）中使用标准化问卷，我们评估了三个不同的AI领域（药物治疗，工作招聘和贷款发起）的量表。数据支持拟议的AI威胁（TAI）量表的维度结构以及指标的内部一致性和因式有效性。详细讨论了结果的含义和量表的经验应用。提供了进一步经验性使用TAI量表的建议。我们评估了三个不同AI领域（医疗，工作招聘和贷款发起）的规模。数据支持拟议的AI威胁（TAI）量表的维度结构以及指标的内部一致性和因式有效性。详细讨论了结果的含义和量表的经验应用。提供了进一步经验性使用TAI量表的建议。我们评估了三个不同AI领域（医疗，工作招聘和贷款发起）的规模。数据支持拟议的AI威胁（TAI）量表的维度结构以及指标的内部一致性和因式有效性。详细讨论了结果的含义和量表的经验应用。提供了进一步经验性使用TAI量表的建议。