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. 2020 May 18;1:100014. doi: 10.1016/j.chbr.2020.100014

Table 3.

Factor loadings from the Exploratory Factor Analysis of General Attitudes towards Artificial Intelligence data.

Item Pos Neg U IRC Mean SD
I am interested in using artificially intelligent systems in my daily life 0.78 0.43 0.64 3.56 1.03
There are many beneficial applications of Artificial Intelligence 0.77 0.40 0.68 4.22 0.82
Artificial Intelligence is exciting 0.76 0.49 0.59 3.91 1.00
Artificial Intelligence can provide new economic opportunities for this country 0.70 0.48 0.64 3.75 1.01
I would like to use Artificial Intelligence in my own job 0.66 0.54 0.59 3.13 1.24
An artificially intelligent agent would be better than an employee in many routine jobs 0.60 0.66 0.50 3.08 1.17
I am impressed by what Artificial Intelligence can do 0.60 0.63 0.53 4.13 0.89
Artificial Intelligence can have positive impacts on people’s wellbeing 0.58 0.69 0.47 3.97 0.76
Artificially intelligent systems can help people feel happier 0.57 0.74 0.41 3.19 0.92
Artificially intelligent systems can perform better than humans 0.54 0.62 0.58 3.55 1.03
Much of society will benefit from a future full of Artificial Intelligence 0.49 0.63 0.57 3.55 1.03
For routine transactions, I would rather interact with an artificially intelligent system than with a human 0.47 0.79 0.39 3.15 1.22
I think Artificial Intelligence is dangerous 0.75 0.51 0.47 2.86 1.04
Organisations use Artificial Intelligence unethically 0.74 0.52 0.47 2.71 0.97
I find Artificial Intelligence sinister 0.65 0.45 0.63 3.42 1.09
Artificial Intelligence is used to spy on people 0.64 0.67 0.32 2.35 1.00
I shiver with discomfort when I think about future uses of Artificial Intelligence 0.62 0.43 0.66 3.06 1.34
Artificial Intelligence might take control of people 0.48 0.78 0.35 2.90 1.22
I think artificially intelligent systems make many errors 0.47 0.73 0.43 2.90 0.95
People like me will suffer if Artificial Intelligence is used more and more 0.41 0.59 0.60 3.23 1.20

Table 3 Note: Loadings for the retained 20 items, with factor loadings onto the positive (Pos) and negative (Neg) components, uniqueness (U, i.e. 1 minus Communality), item-rest correlation (IRC), mean, and standard deviation (SD). Note that negative items were reverse-scored in this analysis.