Awareness and experience

To understand people’s awareness of and experience with each of the AI technologies included, participants were asked to indicate whether they had heard of each technology before and their self-reported personal experience with each. 

Image credit: Hannah Wei on Unsplash

The question on personal experience was not included for autonomous weapons, driverless cars, robotic care assistants and simulation technologies for advancing climate change research, where direct experience would be unlikely for most respondents. 

Overall, awareness of and experience with AI technologies varies substantially according to the specific use. 

Awareness of AI technologies is mixed. For 10 of the 17 technologies we asked about, over 50% of the British public say they have heard of them before. Awareness is highest for the use of facial recognition for unlocking mobile phones, with 93% having heard of this before. People are also largely aware of driverless cars (92%) and robotic vacuum cleaners (89%).

People are least aware of the use of AI for assessing eligibility for welfare benefits, with just 19% having heard of this before. People are also less aware of robotic care assistants (32%), using AI to detect risk of cancer from a scan (34%), and using AI to assess eligibility for jobs or risks relating to loan repayments (both 35%). It is important to note that people’s awareness of technologies for assessing risk and eligibility is relatively low. Some of these technologies are already being used in public services, and these results show that people may be largely unaware of the technologies that help make decisions which directly impact their lives. 

19%

Just 19% of people are aware of the use of AI for assessing eligibility for welfare benefits

Awareness of AI technologies differs somewhat according to age, with people aged 75 and over less likely to indicate they have heard of the use of facial recognition for unlocking mobile phones (69% reported being aware, compared to 95% of under 75s), border control (61% reported being aware, compared to 72% of under 75s), or for consumer social media adverts (68% reported being aware, compared to 89% of under 75s).

Our findings about people’s awareness of AI technologies align with those from other studies, which highlight gaps in awareness of AI that are less visible in day-to-day life or the media.

For example, a Centre for Data Ethics and Innovation (CDEI) 2022 mixed-methods study found that the public have high levels of awareness of more visible uses of AI, such as recommendation systems, and futuristic associations of AI based on media images such as robotics. In contrast, the same study found low levels of awareness of AI in technologies that are part of wider societal systems’, such as the prioritisation of social housing.

People report most experience with targeted online adverts for consumer products, with 81% reporting some or a lot of experience

People report mixed levels of personal experience with AI technologies.

Over 50% of the public report personal experience with four of the 13 technologies we asked about. People report most experience with targeted online adverts for consumer products (with 81% reporting some or a lot of experience), smart speakers (with 64% reporting some or a lot of experience), and facial recognition for unlocking mobile phones and at border control (with 62% and 59% respectively reporting some or a lot of experience).

People report least experience with AI for determining risk of cancer from a scan (8%), for calculating welfare eligibility (11%) and with facial recognition for police surveillance (12%).

Experience with some of the technologies differs according to age. People aged 75 and over report less experience with facial recognition to unlock mobile phones (23% report having some or a lot of experience compared to 67% of under 75s), facial recognition at border control (32% report having some or a lot of experience compared to 62% of under 75s), and social media advertisements for consumer products (51% vs 84%) and political parties (18% report having some or a lot of experience compared to 52% of under 75s).

Figure 1: Awareness and uses of AI

Before today, had you heard of the use of AI technologies for…’ 

Facial recognition for unlocking phones
Yes: 93%
Facial recognition for unlocking phones
Not sure/​prefer not to say: 1%
Facial recognition for unlocking phones
No: 6%
Driverless cars
Yes: 92%
Driverless cars
Not sure/​prefer not to say: 1%
Driverless cars
No: 7%
Robotic vacuum cleaners
Yes: 89%
Robotic vacuum cleaners
Not sure/​prefer not to say: 2%
Robotic vacuum cleaners
No: 9%
Targeted consumer advertising
Yes: 87%
Targeted consumer advertising
Not sure/​prefer not to say: 4%
Targeted consumer advertising
No: 9%
Smart speakers
Yes: 82%
Smart speakers
Not sure/​prefer not to say: 4%
Smart speakers
No: 14%
Facial recognition for policing
Yes: 80%
Facial recognition for policing
Not sure/​prefer not to say: 4%
Facial recognition for policing
No: 16%
Facial recognition for border control
Yes: 71%
Facial recognition for border control
Not sure/​prefer not to say: 3%
Facial recognition for border control
No: 26%
Targeted political advertising
Yes: 67%
Targeted political advertising
Not sure/​prefer not to say: 7%
Targeted political advertising
No: 26%
Virtual reality in education
Yes: 59%
Virtual reality in education
Not sure/​prefer not to say: 7%
Virtual reality in education
No: 34%
Autonomous weapons
Yes: 58%
Autonomous weapons
Not sure/​prefer not to say: 7%
Autonomous weapons
No: 35%
Virtual healthcare assistants
Yes: 44%
Virtual healthcare assistants
Not sure/​prefer not to say: 8%
Virtual healthcare assistants
No: 48%
Climate research simulations
Yes: 42%
Climate research simulations
Not sure/​prefer not to say: 9%
Climate research simulations
No: 49%
Assessing loan repayment risk
Yes: 35%
Assessing loan repayment risk
Not sure/​prefer not to say: 9%
Assessing loan repayment risk
No: 56%
Assessing job eligibility
Yes: 35%
Assessing job eligibility
Not sure/​prefer not to say: 5%
Assessing job eligibility
No: 60%
Assessing risk of cancer
Yes: 34%
Assessing risk of cancer
Not sure/​prefer not to say: 5%
Assessing risk of cancer
No: 61%
Robotic care assistants
Yes: 32%
Robotic care assistants
Not sure/​prefer not to say: 7%
Robotic care assistants
No: 61%
Assessing welfare eligibility
Yes: 19%
Assessing welfare eligibility
Not sure/​prefer not to say: 5%
Assessing welfare eligibility
No: 76%
AI technology use Yes Not sure/​prefer not to say No
Facial recognition for unlocking phones 93% 1% 6%
Driverless cars 92% 1% 7%
Robotic vacuum cleaners 89% 2% 9%
Targeted consumer advertising 87% 4% 9%
Smart speakers 82% 4% 14%
Facial recognition for policing 80% 4% 16%
Facial recognition for border control 71% 3% 26%
Targeted political advertising 67% 7% 26%
Virtual reality in education 59% 7% 34%
Autonomous weapons 58% 7% 35%
Virtual healthcare assistants 44% 8% 48%
Climate research simulations 42% 9% 49%
Assessing loan repayment risk 35% 9% 56%
Assessing job eligibility 35% 5% 60%
Assessing risk of cancer 34% 5% 61%
Robotic care assistants 32% 7% 61%
Assessing welfare eligibility 19% 5% 76%

Figure 2: Experience with AI

How much personal experience have you had with this technology?’ 

Targeted consumer advertising
A lot: 37%
Targeted consumer advertising
Some: 44%
Targeted consumer advertising
Not sure/​prefer not to say: 3%
Targeted consumer advertising
None: 16%
Smart speakers
A lot: 24%
Smart speakers
Some: 40%
Smart speakers
Not sure/​prefer not to say: 4%
Smart speakers
None: 32%
Facial recognition for unlocking phones
A lot: 34%
Facial recognition for unlocking phones
Some: 28%
Facial recognition for unlocking phones
Not sure/​prefer not to say: 1%
Facial recognition for unlocking phones
None: 37%
Facial recognition for border control
A lot: 18%
Facial recognition for border control
Some: 41%
Facial recognition for border control
Not sure/​prefer not to say: 2%
Facial recognition for border control
None: 39%
Targeted political advertising
A lot: 12%
Targeted political advertising
Some: 36%
Targeted political advertising
Not sure/​prefer not to say: 10%
Targeted political advertising
None: 42%
Virtual healthcare assistants
A lot: 4%
Virtual healthcare assistants
Some: 24%
Virtual healthcare assistants
Not sure/​prefer not to say: 7%
Virtual healthcare assistants
None: 65%
Assessing loan repayment risk
A lot: 3%
Assessing loan repayment risk
Some: 21%
Assessing loan repayment risk
Not sure/​prefer not to say: 10%
Assessing loan repayment risk
None: 66%
Virtual reality in education
A lot: 3%
Virtual reality in education
Some: 19%
Virtual reality in education
Not sure/​prefer not to say: 7%
Virtual reality in education
None: 71%
Robotic vacuum cleaners
A lot: 6%
Robotic vacuum cleaners
Some: 15%
Robotic vacuum cleaners
Not sure/​prefer not to say: 1%
Robotic vacuum cleaners
None: 78%
Assessing job eligibility
A lot: 3%
Assessing job eligibility
Some: 14%
Assessing job eligibility
Not sure/​prefer not to say: 7%
Assessing job eligibility
None: 76%
Facial recognition for policing
A lot: 2%
Facial recognition for policing
Some: 10%
Facial recognition for policing
Not sure/​prefer not to say: 5%
Facial recognition for policing
None: 83%
Assessing welfare eligibility
A lot: 2%
Assessing welfare eligibility
Some: 9%
Assessing welfare eligibility
Not sure/​prefer not to say: 7%
Assessing welfare eligibility
None: 82%
Assessing risk of cancer
A lot: 2%
Assessing risk of cancer
Some: 6%
Assessing risk of cancer
Not sure/​prefer not to say: 4%
Assessing risk of cancer
None: 88%
How much personal experience have you had with this technology?’ A lot Some Not sure/​prefer not to say None
Targeted consumer advertising 37% 44% 3% 16%
Smart speakers 24% 40% 4% 32%
Facial recognition for unlocking phones 34% 28% 1% 37%
Facial recognition for border control 18% 41% 2% 39%
Targeted political advertising 12% 36% 10% 42%
Virtual healthcare assistants 4% 24% 7% 65%
Assessing loan repayment risk 3% 21% 10% 66%
Virtual reality in education 3% 19% 7% 71%
Robotic vacuum cleaners 6% 15% 1% 78%
Assessing job eligibility 3% 14% 7% 76%
Facial recognition for policing 2% 10% 5% 83%
Assessing welfare eligibility 2% 9% 7% 82%
Assessing risk of cancer 2% 6% 4% 88%