C Appendix C: Additional data analysis results

C.1 Support for developing AI

Table C.1 shows the regression results used to produce Figure 2.4.

Table C.1: Predicting support for developing AI using demographic characteristics: results from a multiple linear regression that includes all demographic variables; outcome standardized to have mean 0 and unit variance
Variables Coefficients (SEs)
(Intercept) -0.27 (0.09)**
Age 38-53 -0.16 (0.06)**
Age 54-72 -0.18 (0.06)**
Age 73 and older -0.16 (0.10)
Male 0.17 (0.05)***
Non-white -0.02 (0.05)
Some college -0.01 (0.06)
College+ 0.18 (0.06)**
Employed (full- or part-time) 0.03 (0.05)
Democrat 0.20 (0.06)**
Independent/Other -0.05 (0.06)
Income $30-70K 0.01 (0.06) Income$70-100K 0.13 (0.09)
Income more than 100K 0.16 (0.08)* Prefer not to say income -0.14 (0.07) No religious affiliation 0.16 (0.05)** Other religion 0.14 (0.08) Born-again Christian -0.04 (0.06) CS or engineering degree 0.05 (0.09) CS or programming experience 0.30 (0.06)*** N = 2000 F(19,1980) = 11.75; p-value: <0.001 C.2 Survey experiment and cross-national comparison: AI and/or robots should be carefully managed We present the percentage of “don’t know” or missing responses to the survey question (see Appendix B for the survey question text). Regression analysis shows that the varying the term used (i.e., AI, AI and robots, and robots) does not change responses to the statement that such technologies should be carefully managed. This finding is robust to a regression where we controlled for “don’t know” or missing responses. In Table C.6, we present the distribution of responses to the statement by country. Table C.2: Survey experiment attrition check: agreement with statement that AI and/or robots should be carefully managed Experimental condition Percent DK/missing Percent DK Percent missing AI 11.39 11.39 0 AI and robots 13.26 13.26 0 Robots 9.60 9.60 0 Table C.3: Survey experiment attrition check: agreement with statement that AI and/or robots should be carefully managed Variables Coefficients (SEs) (Intercept) 0.11 (0.01)*** AI and robots 0.02 (0.02) Robots -0.01 (0.02) N = 2000 F(2, 1997) = 1.03; p-value: 0.359 Table C.4: Survey experiment results: agreement with statement that AI and/or robots should be carefully managed Variables Coefficients (SEs) (Intercept) 1.49 (0.03)*** AI and robots -0.03 (0.04) Robots -0.09 (0.05) N = 2000 F(2, 1997) = 1.92; p-value: 0.146 Table C.5: Survey experiment results: agreement with statement that AI and/or robots should be carefully managed (controlling for DK/missing responses) Variables Coefficients (SEs) (Intercept) 1.46 (0.03)*** AI and robots 0.03 (0.05) Robots -0.07 (0.05) N = 2000 F(5, 1994) = 0.91; p-value: 0.471 Table C.6: Distribution of responses to statement that AI and robots should be carefully managed by country (in percentages); EU countries data from Eurobarometer Countries Totally disagree Tend to disagree Tend to agree Totally agree Don’t know Austria 3 7 43 43 4 Belgium 1 9 40 48 2 Bulgaria 1 2 24 65 8 Croatia 4 8 37 47 4 Cyprus 1 2 26 67 4 Czech Republic 2 7 37 50 4 Denmark 1 4 25 66 4 Estonia 0 4 39 51 6 European Union 2 5 35 53 5 Finland 1 4 29 63 3 France 1 3 31 62 3 Germany 2 4 32 59 3 Greece 1 3 23 71 2 Hungary 4 12 35 45 4 Ireland 1 4 37 54 4 Italy 3 8 43 40 6 Latvia 1 3 29 63 4 Lithuania 0 4 35 57 4 Luxembourg 1 4 33 58 4 Malta 2 4 46 38 10 Netherlands 1 2 22 74 1 Poland 2 8 44 42 4 Portugal 2 2 37 48 11 Romania 5 12 33 42 8 Slovakia 0 5 44 46 5 Slovenia 2 6 37 52 3 Spain 1 3 40 47 9 Sweden 1 2 18 75 4 United Kingdom 1 3 34 57 5 United States 1 5 30 52 12 C.3 Harmful consequences of AI in the context of other global risks Table C.7 summarizes responses to 15 potential global risks. Table C.7: Summary statistics: the American public’s perceptions of 15 potential global risks Potential risks Mean perceived likelihood Mean perceived impact N Failure to address climate change 56% 2.25 666 Failure of regional/global governance 55% 2.46 652 Conflict between major countries 60% 2.68 625 Weapons of mass destruction 49% 3.04 645 Large-scale involuntary migration 57% 2.65 628 Spread of infectious diseases 50% 2.69 620 Water crises 54% 2.90 623 Food crises 52% 2.76 1073 Harmful consequences of AI 45% 2.29 573 Harmful consequences of synthetic biology 45% 2.33 630 Cyber attacks 68% 2.85 650 Terrorist attacks 60% 2.62 635 Global recession 56% 2.61 599 Extreme weather events 65% 2.73 613 Natural disasters 69% 2.87 637 C.4 Survey experiment: what the public considers AI, automation, machine learning, and robotics We formally tested whether or not respondents think AI, automation, machine learning, and robotics are used in different applications. (See Appendix B for the survey question text.) For each technological application, we used an $$F$$-test to test whether any of terms randomly assigned to the respondents affect respondents’ selecting that application. Because we ran 10 $$F$$-tests, we used the Bonferroni correction to control the familywise error rate. The Bonferroni correction rejected the null hypothesis at alpha level $$\alpha/10$$, instead of $$\alpha$$. For instance, to test whether the $$F$$-static is significant at the 5% level, we set the alpha level at $$\alpha/10 = 0.005$$. Our results (in Table C.8) show that except for social robots, respondents think that AI, automation, machine learning, and robotics are used in each of the applications presented in the survey. Table C.8: Respondents distinguish between AI, automation, machine learning, and robotics Technological applications F-statistic p-value Significant Virtual assistants (e.g., Siri, Google Assistant, Amazon Alexa) F(3, 1996) = 18.12 <0.001 Yes Smart speakers (e.g., Amazon Echo, Google Home, Apple Homepod) F(3, 1996) = 24.76 <0.001 Yes Facebook photo tagging F(3, 1996) = 20.22 <0.001 Yes Google Search F(3, 1996) = 37.30 <0.001 Yes Recommendations for Netflix movies or Amazon ebooks F(3, 1996) = 33.69 <0.001 Yes Google Translate F(3, 1996) = 24.62 <0.001 Yes Driverless cars and trucks F(3, 1996) = 9.08 <0.001 Yes Social robots that can interact with humans F(3, 1996) = 1.05 0.369 No Industrial robots used in manufacturing F(3, 1996) = 55.72 <0.001 Yes Drones that do not require a human controller F(3, 1996) = 9.68 <0.001 Yes Next, we investigated the problem of respondents not selecting technological applications where it would be logical to pick them (e.g., not selecting industrial robots or social robots when presented with the term “robotics”). Our regression analysis shows that this type of non-response is correlated with respondents’ inattention. We used two measures as a proxy for inattention: 1. time to complete the survey 2. the absolute deviation from the median time to complete the survey. Because the distribution of completion times is heavily skewed right, we used absolute deviation from the median, as opposed to the mean. The median is 13 minutes whereas the mean is 105 minutes. We incorporated the second measure because we suspected that people who took very little time or a very long time to complete the survey were inattentive. We used three outcomes that measured non-response: 1. the number of items selected 2. not selecting “none of the above” 3. selecting items containing the word “robots” for respondents assigned to consider “robotics” Using multiple regression, we showed that inattention predicts non-response measured by the three outcomes above (see Tables C.9, C.10, and C.11). Table C.9: Correlation between survey completion time and number of selected items Variables Coefficients (SEs) (Intercept) 3.58 (0.17)*** Survey completion time (min) 0.14 (0.01)*** Absolute deviation from median survey completion time (min) -0.14 (0.01)*** Term: automation 0.98 (0.22)*** Term: machine learning -0.09 (0.22) Term: Robotics -0.51 (0.20)* N = 2000 F(5, 1994) = 47.47; p-value: <0.001 Table C.10: Correlation between survey completion time and not selecting ‘none of the above’ Variables Coefficients (SEs) (Intercept) 0.79 (0.02)*** Survey completion time (min) 0.01 (<0.01)*** Absolute deviation from median survey completion time (min) -0.01 (<0.01)*** Term: automation 0.05 (0.02)* Term: machine learning -0.04 (0.02) Term: Robotics 0.04 (0.02) N = 2000 F(5, 1994) = 13.16; p-value: <0.001 Table C.11: Correlation between survey completion time and selecting ‘robots’ when assigned the term ‘robotics’ Variables Coefficients (SEs) (Intercept) 0.87 (0.06)*** Survey completion time (min) 0.06 (0.01)*** Absolute deviation from median survey completion time (min) -0.06 (0.01)*** N = 486 F(2, 483) = 50.55; p-value: <0.001 C.5 AI governance challenges: prioritizing governance challenges We compared respondents’ perceived likelihood of each governance challenge impacting large numbers of people in the U.S. with respondents’ perceived likelihood of each governance challenge impacting large numbers of people around the world. (See Appendix B for the survey question text.) For each governance challenge, we used linear regression to estimate the difference between responses to the U.S. question and the world question. Because we ran 13 tests, we used the Bonferroni correction to control the familywise error rate. In our case, the Bonferroni correction rejected the null hypothesis at alpha level $$\alpha/13$$, instead of $$\alpha$$. To test whether the differences are significant at the 5% level, we set the alpha level at $$\alpha/13 = 0.004$$. According to Table C.12, Americans perceive that all governance challenges, except for protecting data privacy and ensuring safe autonomous vehicles, are more likely to impact people around the world than in the U.S. specifically. In particular, Americans think that autonomous weapons are 7.6 percentage points more likely to impact people around the world than in the U.S. Table C.12: Comparing perceived likelihood: in U.S. vs. around the world; each difference is the U.S. mean likelihood subtracted from the world mean likelihood Governance challenge U.S. mean likelihood Difference (SE) p-value Significant Hiring bias 59.8 2.5 (0.8) 0.001 Yes Criminal justice bias 55.6 2.5 (0.8) 0.003 Yes Disease diagnosis 60.4 2.1 (0.6) 0.001 Yes Data privacy 66.9 1.7 (0.6) 0.010 No Autonomous vehicles 61.8 -0.7 (0.8) 0.401 No Digital manipulation 68.6 2.6 (0.7) <0.001 Yes Cyber attacks 66.2 3.2 (0.9) <0.001 Yes Surveillance 69.0 2.2 (0.7) 0.002 Yes U.S.-China arms race 60.3 3.0 (0.7) <0.001 Yes Value alignment 60.4 3.6 (0.7) <0.001 Yes Autonomous weapons 54.7 7.6 (0.8) <0.001 Yes Technological unemployment 62.3 2.3 (0.7) <0.001 Yes Critical AI systems failure 55.2 3.1 (0.8) <0.001 Yes To highlight the differences between the responses of demographic subgroups regarding issue importance, we created an additional graph (Figure C.1). Here, we subtracted the overall mean of perceived issue importance across all responses from each subgroup-governance challenge mean.18 Table C.15 shows the results from a saturated regression predicting perceived issue importance using demographic variables, AI governance challenge, and interactions between the two types of variables. Table C.13: Perception of AI governance challenges in the U.S.: summary statistics table Governance challenge Mean likelihood Mean issue importance Product of likelihood and issue importance Surveillance 69% 2.56 1.77 Data privacy 67% 2.62 1.75 Digital manipulation 69% 2.53 1.74 Cyber attacks 66% 2.59 1.71 Autonomous vehicles 62% 2.56 1.58 Technological unemployment 62% 2.50 1.56 Value alignment 60% 2.55 1.54 Disease diagnosis 60% 2.52 1.52 U.S.-China arms race 60% 2.52 1.52 Hiring bias 60% 2.54 1.52 Autonomous weapons 55% 2.58 1.42 Criminal justice bias 56% 2.53 1.41 Critical AI systems failure 55% 2.47 1.36 Table C.14: Perception of AI governance challenges in the world: summary statistics table Governance challenge Mean likelihood Mean issue importance Product of likelihood and issue importance Surveillance 71% 2.56 1.82 Digital manipulation 71% 2.53 1.80 Cyber attacks 69% 2.59 1.80 Data privacy 69% 2.62 1.80 Value alignment 64% 2.55 1.63 Technological unemployment 65% 2.50 1.62 Autonomous weapons 62% 2.58 1.61 U.S.-China arms race 63% 2.52 1.60 Hiring bias 62% 2.54 1.58 Disease diagnosis 63% 2.52 1.58 Autonomous vehicles 61% 2.56 1.56 Criminal justice bias 58% 2.53 1.47 Critical AI systems failure 58% 2.47 1.44 Table C.15: Results from a saturated regression predicting perceived issue importance using demographic variables, AI governance challenge, and interactions between the two types of variables; the coefficients for the interactions variables are not shown due to space constraints Variables Coefficient (SEs) (Intercept) 2.25 (0.11)*** Age 38-53 0.11 (0.07) Age 54-72 0.35 (0.06)*** Age 73 and older 0.44 (0.07)*** Male 0.02 (0.05) Non-white -0.01 (0.05) Some college 0.03 (0.07) College+ 0.15 (0.07)* Employed (full- or part-time) -0.09 (0.06) Income30-70K 0.09 (0.08)
Income $70-100K 0.13 (0.10) Income more than$100K -0.01 (0.10)
Prefer not to say income 0.04 (0.08)
Democrat 0.13 (0.07)
Independent/Other 0.14 (0.07)
No religious affiliation -0.04 (0.06)
Other religion -0.05 (0.08)
Born-again Christian 0.07 (0.07)
CS or engineering degree -0.35 (0.10)***
CS or programming experience -0.01 (0.07)
Criminal justice bias 0.05 (0.13)
Disease diagnosis -0.06 (0.14)
Data privacy 0.16 (0.13)
Autonomous vehicles -0.07 (0.14)
Digital manipulation -0.14 (0.15)
Cyber attacks 0.05 (0.14)
Surveillance <0.01 (0.15)
U.S.-China arms race 0.04 (0.13)
Value alignment -0.06 (0.13)
Autonomous weapons 0.06 (0.14)
Technological unemployment -0.12 (0.14)
Critical AI systems failure -0.27 (0.15)
N = 10000 observations, 2000 respondents F(259,1999) = 3.36; p-value: <0.001

C.6 Trust in various actors to develop and manage AI in the interest of the public

Table C.16 displays the mean level of trust the public expresses in various actors to develop and manage AI in the interest of the public.

Table C.16: Trust in various actors to develop and manage AI in the interest of the public: mean responses
Actors Trust to develop AI Trust to manage AI
U.S. military 1.56 (MOE: +/-0.07); N = 638
U.S. civilian government 1.16 (MOE: +/-0.07); N = 671
NSA 1.28 (MOE: +/-0.07); N = 710
FBI 1.21 (MOE: +/-0.08); N = 656
CIA 1.21 (MOE: +/-0.07); N = 730
U.S. federal government 1.05 (MOE: +/-0.07); N = 743
U.S. state governments 1.05 (MOE: +/-0.07); N = 713
NATO 1.17 (MOE: +/-0.06); N = 695
Intergovernmental research organizations (e.g., CERN) 1.42 (MOE: +/-0.07); N = 645 1.27 (MOE: +/-0.06); N = 747
International organizations 1.10 (MOE: +/-0.06); N = 827
UN 1.06 (MOE: +/-0.06); N = 802
Tech companies 1.44 (MOE: +/-0.07); N = 674 1.33 (MOE: +/-0.07); N = 758
Google 1.34 (MOE: +/-0.08); N = 645 1.20 (MOE: +/-0.07); N = 767
Facebook 0.85 (MOE: +/-0.07); N = 632 0.91 (MOE: +/-0.07); N = 741
Apple 1.29 (MOE: +/-0.07); N = 697 1.20 (MOE: +/-0.07); N = 775
Microsoft 1.40 (MOE: +/-0.08); N = 597 1.24 (MOE: +/-0.07); N = 771
Amazon 1.33 (MOE: +/-0.07); N = 685 1.24 (MOE: +/-0.07); N = 784
Non-profit (e.g., OpenAI) 1.44 (MOE: +/-0.07); N = 659
University researchers 1.56 (MOE: +/-0.07); N = 666
Non-government scientific organization (e.g., AAAI) 1.35 (MOE: +/-0.06); N = 792
Partnership on AI 1.35 (MOE: +/-0.06); N = 780

C.7 Survey experiment: comparing perceptions of U.S. vs. China AI research and development

A substantial percentage of respondents selected “I don’t know” when answering this survey question. (See Appendix B for the survey question text.) Our regression analysis shows that there is a small but statistically significant difference between respondents’ perception of R&D in the U.S. as compared to in China, as seen in Tables C.19 and C.20.

Table C.17: Survey experiment attrition check: comparing U.S. and China’s AI research and development
Experimental condition Percent DK/missing Percent DK Percent missing
China 26.48 26.48 0
U.S. 22.77 22.77 0
Table C.18: Survey experiment attrition check: comparing U.S. and China’s AI research and development
Variables Coefficients (SEs)
(Intercept) 0.27 (0.01)***
U.S. -0.04 (0.02)
N = 2000 F(1, 1998) = 3.12; p-value: 0.078
Table C.19: Survey experiment results: comparing U.S. and China’s AI research and development
Variables Coefficients (SEs)
(Intercept) 1.74 (0.02)***
U.S. -0.08 (0.03)*
N = 2000 F(1, 1998) = 6.58; p-value: 0.01
Table C.20: Survey experiment results: comparing U.S. and China’s AI research and development (controlling for DK/missing responses)
Variables Coefficients (SEs)
(Intercept) 1.74 (0.02)***
U.S. -0.08 (0.03)**
N = 2000 F(3, 1996) = 6.14; p-value: <0.001

C.8 Survey experiment: U.S.-China arms race

We checked that “don’t know” or missing responses to both statements are not induced by the information treatments. (See Appendix B for the survey experiment text.) Next, we examined the correlation between responses to the two statements using a 2D bin count graph. The overall Pearson correlation coefficient is -0.05 but there exists considerable variation by experimental condition.

Table C.21: Survey experiment attrition check: agreement with statement that U.S. should invest more in AI military capabilities
Experimental condition Percent DK/missing Percent DK Percent missing
Control 13.53 13.53 0
Treatment 1: Pro-nationalist 12.28 12.28 0
Treatment 2: Risks of arms race 12.58 12.58 0
Treatment 3: One common humanity 13.62 13.62 0
Table C.22: Survey experiment attrition check: agreement with statement that U.S. should invest more in AI military capabilities
Variables Coefficients (SEs)
(Intercept) 0.13 (0.02)***
Treatment 1: Pro-nationalist <0.01 (0.02)
Treatment 2: Risks of arms race -0.01 (0.02)
Treatment 3: One common humanity >-0.01 (0.02)
N = 2000 F(3, 1996) = 0.08; p-value: 0.972
Table C.23: Survey experiment attrition check: agreement with statement that U.S. should work hard to cooperate with China to avoid dangers of AI arms race
Experimental condition Percent DK/missing Percent DK Percent missing
Control 14.12 13.92 0.2
Treatment 1: Pro-nationalist 14.26 14.26 0.0
Treatment 2: Risks of arms race 12.78 12.78 0.0
Treatment 3: One common humanity 12.80 12.80 0.0
Table C.24: Survey experiment attrition check: agreement with statement that U.S. should work hard to cooperate with China to avoid dangers of AI arms race
Variables Coefficients (SEs)
(Intercept) 0.14 (0.02)***
Treatment 1: Pro-nationalist 0.02 (0.02)
Treatment 2: Risks of arms race -0.01 (0.02)
Treatment 3: One common humanity -0.02 (0.02)
N = 2000 F(3, 1996) = 0.76; p-value: 0.516
Table C.25: Correlation between responses to the two statements
Experimental condition Pearson correlation
Overall -0.05
Control -0.06
Treatment 1: Pro-nationalist -0.03
Treatment 2: Risks of arms race -0.12
Treatment 3: One common humanity -0.01

C.9 Trend across time: job creation or job loss

There are many “don’t know” responses to this survey question (see Appendix B for the survey question text). Nevertheless, “don’t know” or missing responses are not affected by the experimental future time framing. $$F$$-tests reveal that there are no differences in responses to the three future time frames, as seen in Table C.30.

Table C.26: Survey experiment attrition check: future time frame
Experimental condition Percent DK/missing Percent DK Percent missing
No time frame 24.59 24.38 0.21
10 years 25.49 25.49 0.00
20 years 26.16 25.96 0.20
50 years 24.17 24.17 0.00
Table C.27: Survey experiment attrition check: future time frame
Variables Coefficients (SEs)
(Intercept) 0.25 (0.02)***
10 years 0.01 (0.03)
20 years 0.02 (0.03)
50 years -0.01 (0.03)
N = 2000 F(3, 1996) = 0.34; p-value: 0.795
Table C.28: Survey experiment results: future time frame
Variables Coefficients (SEs)
(Intercept) -0.52 (0.06)***
10 years -0.15 (0.08)
20 years -0.12 (0.08)
50 years -0.06 (0.08)
N = 2000 F(3, 1996) = 1.48; p-value: 0.219
Table C.29: Survey experiment results: future time frame (controlling for DK/missing responses)
Variables Coefficients (SEs)
(Intercept) -0.52 (0.06)***
10 years -0.15 (0.08)
20 years -0.12 (0.08)
50 years -0.06 (0.08)
N = 2000 F(7, 1992) = 1.68; p-value: 0.108
Table C.30: Testing coefficients for time frames are equivalent
Tests F-statistic p-value
10 years = 20 years F(1, 1992) = 0.15 0.70
10 years = 50 years F(1, 1992) = 1.41 0.24
20 years = 50 years F(1, 1992) = 0.63 0.43

C.10 High-level machine intelligence: forecasting timeline

Figure C.3 displays the mean predicted the likelihood of high-level machine intelligence for each year by demographic subgroup. Figure C.4 displays the median predicted probability of high-level machine intelligence for each year by demographic subgroup.

C.11 Support for developing high-level machine intelligence

We examined the correlation between support for developing AI and support for developing high-level machine intelligence using a 2D bin count graph. The overall Pearson correlation coefficient is 0.61, according to Figure C.5.

The mean level of support for developing high-level machine intelligence, compared with the mean level of support for developing AI, is 0.24 points (MOE = +/- 0.04) lower on a five-point scale (two-sided $$p$$-value $$<0.001$$), as shown in Table C.31.

Table C.32 displays the regression results used to produce Figure 6.5.

To identify subgroups that have diverging attitudes toward high-level machine intelligence versus AI, we performed multiple regression using both the demographic subgroups variables and respondents’ support for developing AI as predictors. The support for developing high-level machine intelligence outcome variable was standardized such that it has mean 0 and unit variance. The results are shown in Table C.33.

After controlling for one’s support for developing AI, significant predictors correlated with support for developing high level machine intelligence, including:

• Being a member of the Silent Generation (versus being a Millennial/post-Millennial)
• Having CS or programming experience (versus not having such experience)
• Having a high school degree or less (versus having at least a four-year college degree)
Table C.31: Difference between support for developing AI and support for developing high-level machine intelligence
Variables Coefficients (SEs)
(Intercept) 0.25 (0.03)***
High-level machine intelligence -0.24 (0.02)***
N = 2000
Table C.32: Predicting support for developing high-level machine intelligence using demographic characteristics: results from a multiple linear regression that includes all demographic variables; outcome standardized to have mean 0 and unit variance
Variables Coefficients (SEs)
(Intercept) -0.25 (0.09)**
Age 38-53 -0.12 (0.06)
Age 54-72 -0.03 (0.06)
Age 73 and older 0.12 (0.10)
Male 0.18 (0.05)***
Non-white 0.01 (0.05)
Some college -0.04 (0.06)
College+ <0.01 (0.07)
Employed (full- or part-time) 0.09 (0.05)
Democrat 0.11 (0.07)
Independent/Other -0.13 (0.07)*
Income $30-70K -0.01 (0.07) Income$70-100K 0.09 (0.09)
Income more than $100K 0.19 (0.09)* Prefer not to say income <0.01 (0.08) No religious affiliation 0.09 (0.06) Other religion 0.06 (0.08) Born-again Christian -0.07 (0.06) CS or engineering degree <0.01 (0.10) CS or programming experience 0.36 (0.06)*** N = 2000 F(19,1980) = 7.27; p-value: <0.001 Table C.33: Predicting support for developing high-level machine intelligence using demographic characteristics: results from a multiple linear regression that includes all demographic variables and respondents’ support for developing AI; outcome standardized to have mean 0 and unit variance Variables Coefficients (SEs) (Intercept) -0.23 (0.08)** Age 38-53 -0.02 (0.05) Age 54-72 0.09 (0.05) Age 73 and older 0.22 (0.09)* Male 0.08 (0.04) Non-white 0.02 (0.05) Some college -0.04 (0.05) College+ -0.11 (0.06) Employed (full- or part-time) 0.08 (0.04) Democrat -0.02 (0.06) Independent/Other -0.10 (0.05) Income$30-70K -0.01 (0.06)
Income $70-100K 0.01 (0.07) Income more than$100K 0.08 (0.07)
Prefer not to say income 0.09 (0.07)
No religious affiliation -0.02 (0.05)
Other religion -0.03 (0.07)
Born-again Christian -0.05 (0.05)
CS or engineering degree -0.03 (0.07)
CS or programming experience 0.17 (0.05)***
Support for developing AI 0.58 (0.02)***
N = 2000 F(20,1979) = 54.15; p-value: <0.001

C.12 Expected outcome of high-level machine intelligence

We examined the correlation between respondents’ expected outcome of high-level machine intelligence and support for developing high-level machine intelligence using a 2D bin count graph. The overall Pearson correlation coefficient is 0.69, as seen in Figure C.6.

1. Note that the perceived issue importance was measured on a four-point scale, where 0 meant “not at all important” and 3 meant “very important.” We only mean-centered the outcomes; we did not standardize such that the outcomes have unit variance.