The Threats of Artificial Intelligence Scale (TAI)

Abstract

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.

Artificial intelligence ethics by design. Evaluating public perception on the importance of ethical design principles of artificial intelligence

“…This is in line with empirical evidence showing that legal regulations are perceived not only as effective, but also as demanded countermeasures against discriminatory AI (Kieslich et al, 2020). As AI technology is considered a potential risk or even threat—at least among a share of the public (Kieslich et al, 2021; Liang and Lee, 2017)—setting up a clear legal framework for regulation might be a way to further enhance trust and acceptance toward AI. In this respect, the EU has already taken on a pioneering role, as the EU commission recently proposed a legal framework for the handling of AI technology (European Commission, 2021).…”

“…The question wording was adapted from Lee (2018). The items are based on the dimensions proposed by Kieslich et al (2021).…”

Exploring the roles of trust and social group preference on the legitimacy of algorithmic decision-making vs. human decision-making for allocating COVID-19 vaccinations

“…Research shows that citizens of many countries have rather favorable attitudes toward AI (Kelley et al 2019 ; Zhang and Dafoe 2019 ). However, the consequential decisions of AI may be perceived as threatening in certain contexts, such job recruitment, and loan origination (Kieslich et al 2021b ).…”

Using automated decision-making (ADM) to allocate Covid-19 vaccinations? Exploring the roles of trust and social group preference on the legitimacy of ADM vs. human decision-making

“…Research shows that many countries generally have rather favorable attitudes towards AI (Kelley et al 2019;Zhang and Dafoe 2019). However, in specific contexts, consequential decisions of AI may be perceived as threatening (Kieslich, Lünich, and Marcinkowski 2021).…”