Fear of Autonomous Robots and Artificial Intelligence: Evidence from National Representative Data with Probability Sampling

Liang, Yuhua; Lee, Seungcheol Austin

doi:10.1007/s12369-017-0401-3

Cited by 134 publications

(103 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Especially, when it comes to the influence of robots in the economy and the substitution of workforce, people express fear [7,9]. On a broader level, a recent study by Liang and Lee [8] inquiring about the fear of AI even found that a considerable amount of all Americans reported fears when it comes to autonomous robots and AI.…”

Section: Public Perceptions Of Recent Developments In Artificial Intelligencementioning

confidence: 99%

“…People might happily accept new technology, they might not care nor use it at all, or they may even show severe reactance towards it [6]. There is first empirical evidence suggesting that the general public itself shows some considerable restraint when it comes to the broad societal diffusion of AI applications or robots that might even border on actual fear of such technology [7][8][9]. However, as fear and respective threat perceptions are presuppositional theoretical constructs, they necessitate a more fine-grained approach that goes beyond broad claims of concerns or even fear regarding autonomous systems.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Threats of Artificial Intelligence Scale (TAI)

Kieslich

Lünich

Marcinkowski

2021

Int J of Soc Robotics

View full text Add to dashboard Cite

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.

show abstract

Section: Public Perceptions Of Recent Developments In Artificial Intelligencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Threats of Artificial Intelligence Scale (TAI)

Kieslich

Lünich

Marcinkowski

2021

Int J of Soc Robotics

View full text Add to dashboard Cite

show abstract

“…Thus, the results might differ from a controlled lab setting where users get to experience social robots firsthand. However, previous research has indicated that research on non-experts, such as ours, can be helpful to assess individuals' attitudes and fears of social robots, even if they have not used such technology themselves (Liang & Lee, 2017). Third, we could not assess contextual characteristics, such as users' cultural backgrounds or their social milieus.…”

Section: Limitationsmentioning

confidence: 93%

“…Yet, empirical evidence on privacy concerns and privacy implications of social robots among non-experts (i.e., individuals largely unfamiliar with robots) is scarce. While a few surveys have looked at trust in social robots (Alaiad & Zhou, 2014) and general attitudes toward them (Eurobarometer, 2012;Liang & Lee, 2017), privacy has mostly been discussed in conceptual terms (Calo, 2012(Calo, , 2016Lutz & Tamò, 2018; Rueben, Grimm, et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

The Robot Privacy Paradox: Understanding How Privacy Concerns Shape Intentions to Use Social Robots

Lutz

Tamò-Larrieux

2020

HMC

View full text Add to dashboard Cite

Conceptual research on robots and privacy has increased but we lack empirical evidence about the prevalence, antecedents, and outcomes of different privacy concerns about social robots. To fill this gap, we present a survey, testing a variety of antecedents from trust, technology adoption, and robotics scholarship. Respondents are most concerned about data protection on the manufacturer side, followed by social privacy concerns and physical concerns. Using structural equation modeling, we find a privacy paradox, where the perceived benefits of social robots override privacy concerns.

show abstract

“…A survey by the institute DemoSCOPE (N = 1007) has found that while 50% would accept information from a robot, only 16% would be willing to work in a team with one [2]. A considerable portion of the general population still fears robots and artificial intelligence, caused by a range of concerns about the negative impact on interpersonal relationships and potential job displacement [19,31]. This begs the question of what could aid in easing humans into collaboration with a robot.…”

Section: Trust and The Investment Gamementioning

confidence: 99%

Exploring Human-Robot Trust Through the Investment Game

Arts

Zörner

Bhatia

et al. 2020

Proceedings of the 8th International Conference on Human-Agent Interaction

View full text Add to dashboard Cite

As robots become more advanced and capable, developing trust is an important factor of human-robot interaction and cooperation. However, as multiple environmental and social factors can influence trust, it is important to develop more elaborate scenarios and methods to measure human-robot trust. A widely used measurement of trust in social science is the investment game. In this study, we propose a scaled-up, immersive, science fiction Human-Robot Interaction (HRI) scenario for intrinsic motivation on human-robot collaboration, built upon the investment game and aimed at adapting the investment game for human-robot trust. For this purpose, we utilise two Neuro-Inspired Companion (NICO)-robots and a projected scenery. We investigate the applicability of our space mission experiment design to measure trust and the impact of nonverbal communication. We observe a correlation of 0.43 (p = 0.02) between self-assessed trust and trust measured from the game and a positive impact of non-verbal communication on trust (p = 0.0008) and robot perception for anthropomorphism (p = 0.007) and animacy (p = 0.00002). We conclude that our scenario is an appropriate method to measure trust in human-robot interaction and also to study how non-verbal communication influences a human's trust in robots.

show abstract

Fear of Autonomous Robots and Artificial Intelligence: Evidence from National Representative Data with Probability Sampling

Cited by 134 publications

References 13 publications

The Threats of Artificial Intelligence Scale (TAI)

The Threats of Artificial Intelligence Scale (TAI)

The Robot Privacy Paradox: Understanding How Privacy Concerns Shape Intentions to Use Social Robots

Exploring Human-Robot Trust Through the Investment Game

Contact Info

Product

Resources

About