Approximately a fifth of all employees have jobs with high exposure. Women, Asian workers, and those who are more educated and paid more tend to have higher exposure. Nevertheless, those in the most exposed jobs are the most likely to claim that AI will help them more than hurt them.
With the release of ChatGPT and Dall-E, there has been renewed focus on artificial intelligence (AI). These tools, alongside a spectrum of AI-powered business applications, mark a new era for employees. Which U.S. Workers Are More Exposed to AI in Their Jobs?
Previously, advancements in technology have consistently focused on the automation of menial labor, such as that done at the assembly line. However, as AI grows in prominence, it’s redefining its impact on office work and the professional world, raising serious questions regarding its effect. This is the focus of a new pitch of government data analysis by the Pew Research Center.
What we found
- In the United States in 2022, 19% of the AI-related positions had American workers in roles that faced the greatest risk of having their core tasks either supplemented or automated by AI.
- In contrast, 23% of workers are in positions least threatened by AI, where its influence will not be felt for a long time. The rest of the workers, almost six in ten of all workers, are prone to having varying degrees of AI exposure.
- The majority of those employed in positions that are high in AI exposure tend to reside in the better-paying occupations that require college degrees, as well as possess strong analytical skills.
- Microsoft AI: Lots of people are referring to this new generative AI tool. It is widely available and aimed at the general public. Unlike other such tools, it allows for experimentation without needing everyone to sign up first.
- Different groups of workers sustain different levels of AI exposure
- The disparity is 35% among women and 29% among men, likely due to the exposure.
The bulge in exposure among foreign women is due to the nature of.
- Among the educated, those with a bachelor’s degree or more (27%) are interested and willing to be exposed. In comparison, those with only high school qualifications sit at 12%.
- Asian (24%) and white (20%) workers show the highest willingness, compared to black(15%) and Hispanic (13%) workers.
- Higher-wage workers: In 2022, workers in the most exposed jobs earned $33 per hour, on average, compared with $20 in jobs with the least amount of exposure.
Workers appear more optimistic than worried regarding the influence of AI on their employment.
- As noted in a survey by the Pew Research Center, many American workers in more vulnerable sectors do not see their jobs as a risk; most of them believe that AI will be beneficial rather than detrimental to them. For example, in the field of information and technology, 32% of workers believe that AI will help them/more personally than hurt them/her whereas 11% believe it will do more harm than good.
Which jobs are more vulnerable to the impact of AI? Work-related tasks differ in the degree to which they can be automated. Some activities, like equipment maintenance, may have little exposure to AI, while others may be medium or high. Furthermore, interchangeable levels of exposure to AI within a singular task can make it equally essential in multiple roles.
In our definition, jobs are considered more vulnerable to AI if their most important activities can either be done entirely or with some assistance.
As an illustration, AI could at least partially automate ‘retrieving information’ and ‘analyzing data or information.’ It could also assist in ‘working with computers.’ These tasks are fundamental for judicial law clerks and web developers, who face a greater threat of being replaced by AI compared to other workers.
Nonetheless, AI cannot ‘assist and care for others’ or ‘perform general physical activities.’ This explains why nannies, who consider these fundamental tasks essential, have lower exposure to AI.
In our analysis, we considered the most exposed to AI the jobs that ranked in the top 25% of the importance of work activities with high exposure to AI. The least exposed to AI were those that ranked in the top 25% of the importance of work activities, with low exposure to AI. The remaining jobs, such as chief executives, are likely to experience medium exposure to AI. (See the appendix for a more detailed breakdown of all the occupations in each group.)
Related: How we determined the degree to which jobs are exposed to artificial intelligence
Will exposure to AI result in job losses? The answer to this is uncertain. Because AI might be used either to replace or supplement what people do, it isn’t always acknowledged exactly which or how many jobs are in danger. For this purpose, our examination focuses on the level of exposure jobs have to AI. It sets aside the question of whether or not this exposure will cause jobs to be misplaced or jobs to be created.
Consider customer support retailers. Evidence shows that AI may want to either update them with extra powerful chatbots or it may beautify their productivity. AI might also create new forms of jobs for extra-skilled employees, much as the net age generated new lessons of jobs together including internet builders. Another way AI-associated developments may boost employment ranges is by giving a boost to the financial system with the aid of elevating productivity and creating more jobs on average.
Overall, AI is designed to imitate cognitive features, and it’s in all likelihood that higher-paying, white-collar jobs will see a significant amount of publicity in the future. But our analysis doesn’t don’t forget the function of AI-enabled machines or robots that can perform mechanical or physical tasks. Recent proof shows that commercial robots might also lessen both employment and wages. Moreover, jobs held with the aid of low-wage people, the ones without an excessive college diploma, and younger men are more exposed to the effects of industrial robots.
What facts did we use? This evaluation rests on statistics on the importance of 41 important painting activities in 873 occupations from the U.S. Department of Labor’s Occupational Information Network (O*NET, Version 27.3). We used our judgment to decide which of those activities have low, medium, or excessive publicity to AI, however cognizant of the importance of low- and high-publicity sports. For additional analysis, the 873 occupations had been similarly grouped into a total of 485 for which government data on employment and income of people were had. That allowed us to analyze the capability effect of AI on distinct agencies of people. Other findings about how people experience AI come from a Center survey of 11,004 U.S. adults performed between Dec. 12 and 18, 2022. (Refer to the textual content bins and method for more details.)
Our other key findings
- Most people are more likely to work in jobs with less exposure to AI than in jobs with extra exposure. This is proper amongst men, Black and Hispanic employees, more youthful workers, and people with less formal education, among others.
- Asian workers and college graduates are some of the highest-paid employees, maximum exposed to AI. The maximum exposed workers earn more than the least uncovered people, no matter their demographic function, and the distance is particularly pronounced among men, Asian workers, and foreign-born workers.
- Analytical abilities are more crucial in jobs with extra exposure to AI. These competencies include essential thinking, writing, technological know-how, and mathematics. Mechanical competencies, consisting of gadget protection, are more important in jobs with less exposure to AI.
- Scarcely any U.S. agencies—fewer than 3%—predicted the use of advanced technology, along with device-getting-to-know or machine vision software, to provide items or offerings in 2020, consistent with the most recent available records from the U.S. Census Bureau. Still, these had been big companies that accounted for approximately 11% to 16% of overall employment.
Sidebar: How we determined the degree to which jobs are uncovered to artificial intelligence
In our analysis, we took into consideration two fundamental questions while assessing the publicity of jobs to AI:
- What is the likelihood that a work pastime may be substituted for or complemented by AI at this time? Is the likelihood high, medium, or low?
- How critical are sports with high or low exposure to AI in any given activity, relative to the significance of other sports?
Classifying work sports by using exposure to AI
The O*NET database lists a hard and fast list of 41 work skills common throughout all occupations. Examples of those sports are becoming statistics, promoting or influencing others, and managing and shifting gadgets (refer to the method for the whole listing). We used our collective judgment to designate each activity as having excessive, medium, or low exposure to AI. Consensus on some activities, which include acting fashionable, bodily activities, or processing facts, was reached speedily. The former is judged as having low exposure to AI, and the latter is judged as having high exposure. In other instances, we used extra info on a hobby to reach consensus. The query we requested ourselves at this degree changed into the following:
Are most of the tasks that incorporate a work activity exposed to AI?
For example, the task pastime “performing for or running immediately with the general public” is ambiguous on the floor. But remember the listing of particular responsibilities that incorporate this broad interest:
- Audition for roles
- Perform for recordings
- Perform a tune for the public
- Collaborate with others to prepare or carry out artistic productions
- Entertain the public with comedic or dramatic performances
- Perform dances
- Operate a gaming gadget
- Conduct leisure or gaming activities
- Respond to purchaser troubles or complaints
- Respond to client inquiries
- Answer patron questions about goods or services
- Communicate with clients to solve court cases or provide certain details
- Resolve purchaser proceedings or troubles
- Correspond with customers to reply to questions or remedy court cases
The consensus we reached was that the maximum of these particular duties, along with interfacing with customers or growing track, had an excessive degree of exposure to AI. Only a few responsibilities – auditioning, comedic or dramatic performance, and dancing – were taken into consideration to have extraordinarily low exposure to AI. For that purpose, the large hobby “acting for or working without delay with the general public” is deemed to have high exposure to AI.
At the alternative end of the publicity scale is the work pastime “education and growing others,” entailing:
- Coach others
- Encourage patients in the course of healing activities
- Visit individuals in their homes to provide help or data
- Encourage students
- Interact with sufferers to build rapport or provide emotional support
- Support the professional development of others
- Encourage patients or customers to expand their life abilities
The awareness of most of those distinctive tasks entails private interaction. So, we judged that the pastime “coaching and growing others” has low publicity for AI.
Overall, 16 sports were assessed to have high publicity to AI, sixteen extra have been judged to have medium publicity, and nine have been deemed to have low publicity. (Refer to the methodology for which every interest becomes categorised.)
Determining the extent of publicity of a process to AI
The forty-one work sports listed in O*NET are spread across all occupations in the O*NET database. That is to say, every career is a mixture of low, medium, and excessive AI-exposure sports. The query then is:
Which work activities are fantastically more important in a task? Are high- or low-publicity sports more critical than other activities?
To answer this, we first envisioned the averages of the significance ratings for high-, medium-, and low-publicity sports in each process, where the score of every activity within a category was taken from the O*NET database. The rating for each activity ranged from one (not vital) to five (extremely critical).
Overall, out of the 873 occupations we looked at, high-exposure sports were rated as being essential to extremely vital in 77% of occupations, and medium-exposure activities were also important in 72% of occupations. Low-exposure sports were rated as critical in 39% of occupations. This indicates that high, medium, and low publicity could simultaneously be important in a process.
The final step is to estimate the relative importance of high-, medium-, or low-publicity sports in every task – that is, to determine which tasks are more essential than the others in any given task. This process is defined in the method. Occupations were then ranked two ways, first by the relative significance of high-publicity work activities and second by the relative significance of low-exposure work activities.
In our evaluation, jobs that are most exposed to AI are within the top 25% of occupations ranked by the relative importance of high-exposure activities. Jobs that are least exposed to AI are within the top 25% of occupations ranked by the relative importance of low-exposure work activities. The other jobs can be considered as having a medium degree of exposure to AI. (Refer to the appendix for examples of occupations that are among the most or least exposed or have a medium degree of exposure.)
To take an example, consider mechanical drafters, who prepare precise operating diagrams of machinery and mechanical devices. Mechanical drafters are among the people most exposed to AI. For them, high-exposure activities have a median score of 3.28, but low-exposure activities have a mean score of 2.36, where a score of 3 means an activity is crucial.
For nannies, who are among the least exposed workers, high-exposure activities have a median score of 2.36, but low-exposure activities have a score of 3.03.
Related: Previous studies on the effect of AI on U.S. workers.
Our analysis follows in the footsteps of other researchers who have recently examined the effect of AI in the workplace. Eloundou, Manning, Mishkin, and Rock (March 2023) conclude that about one-in-five U.S. workers may also see an effect on half of or more of their job duties. Felten, Raj, and Seamans (April 2021) find that white-collar occupations requiring advanced levels are most susceptible to AI, as are industries providing financial or legal services.
Web (January 2020) reports that highly skilled occupations, particularly knowledgeable and older individuals, may be more impacted by AI, but he does not conclude the nature or the extent of the impact on individuals. Our findings are largely consistent with the results of these analyses.
CORRECTION (October 26, 2023): In the appendix, a preceding version of the bar chart “Shares of employees in an industry who are most likely to see low exposure to AI” included one mislabeled category. “Retail trade” should have been included at 20%, and the actual proportion for “Managerial and administrative services” was 45%.
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