Artificial intelligence: Men's jobs face higher risk of automation than women, low-paid workers also at risk

Updated

Men's jobs are easier to automate than women's jobs, a new analysis shows, and it's partly because women are more likely to work in occupations that need interpersonal, creative and decision-making skills.

A report from consultancy company AlphaBeta suggests machines will take over two hours a week of the most repetitive manual tasks by 2030.

An ABC News analysis has found the easiest to automate jobs are more likely to be performed by men — and by workers on lower incomes, too.

Let's deal with 'blokes vs robots' first

In the chart above, jobs that are easier to automate appear further to the right, and jobs with a higher proportion of male workers appear closer to the top.

Nearly all the jobs with lots of women are less susceptible to automation, including midwives, nurses and hairdressers. These are the orange circles in the chart.

There are only five job categories where more than 50 per cent of the work is susceptible to automation and where women represent the largest number of workers. These jobs, represented by the blue circles, are:

food preparation;

cleaning;

clothing factory work;

hospitality; and

keyboard operators.

Jobs where workers spend a lot of time on physical work are more susceptible to automation, including trades such as plastering and painting. These are clustered at the top-right of the chart — the green circles — because men dominate in those roles.

In pure number terms:

There are about 2 million Australian men working in jobs where more than half of the job is at risk of automation.

working in jobs where more than half of the job is at risk of automation. That compares with about 750,000 women in such roles.

Labourers in construction and mining spend the largest proportion of time, 86 per cent, on tasks that are automatable, according to AlphaBeta's analysis. And 98 per cent of the workers in those jobs are men.

Automation is already underway in Rio Tinto's mines in the Pilbara region of Western Australia, where large drills are operated remotely to drill the iron ore, which is then carted away in driverless trucks controlled more than a thousand kilometres away in Perth.

Why are women's jobs harder to automate?

Women are strongly represented in roles that involve social empathy, interpersonal skills and creativity, explains Hugh Durrant-Whyte from the Centre for Translational Data Science at the University of Sydney.

These roles, which include child carers, receptionists, midwives, nurses and hairdressers, are harder to automate, he says.

Of the top 10 jobs with the most women, only keyboard operators spend more than 50 per cent of their time on tasks that could be automated.

At the other end of the scale, in the top 10 jobs dominated by men, only electricians, and electronics and communications trades workers spend less than 50 per cent of their time on automatable tasks.

But there are exceptions too:

Cleaning and laundry work is one of the most automatable jobs of all, and women hold 60 per cent of those jobs.

More than 90 per cent of engineering professionals are men and only a tenth of their job is regarded as automatable.

Low-paid jobs are the easiest to automate

AlphaBeta's data also suggests that low-paid jobs are more at risk of automation.

The chart below shows that nearly all of the jobs with high incomes, around the $1,800 a week mark or more, have a low proportion of time spent on automatable tasks. Those are the pink circles in the chart.

Assembly line workers and cleaners are among the lowest-paid jobs and around three quarters of their jobs are susceptible to automation (orange circles).

On the other hand, less than 20 per cent of managers, chief executives and doctors' jobs are automatable, but they are among the highest-paid jobs in Australia (pink circles).

But there are exceptions — some hard-to-automate jobs such as child care and driving are badly paid (green circles).

Professor Durrant-Whyte says the risk is that hard-to-automate jobs in future will be poorly paid.

"What will happen is there will be more and more people in the service industries, self-employed, in the gig economy, Airtasker. So the unemployment rate won't change but that difference between professionals and workers will grow, it will be upstairs/downstairs all over again."

He says the job market is "hollowing out in the middle" with automation forcing people to move above or below their skill level.

"The jobs in the middle, which are traditionally middle-class, middle-management jobs, they are the ones that are going to go."

But that's changing, and middle-class jobs also face automation

The data shows that around a third of the tasks done by accountants, insurance clerks, actuaries and librarians is subject to automation.

Professor Durrant-Whyte says the challenge is to manage people with university degrees who are now doing jobs they are over-qualified for because their traditional jobs are disappearing.

"The technology is a looming social problem. Our social norms are not really aligned to the prospect, despite the fact we've been discussing for 50 years that we'll have more people than we have jobs."

Ailira, an artificial intelligence machine that can scan millions of pages of tax law and documents in seconds, was developed by Adelaide-based tax lawyer Adrian Carland.

The ABC put Ailira, which stands for "Artificially Intelligent Legal Information Resource Assistant", to the test against 22-year-old paralegal Christine Maibom.

Ailira answered the legal research question in just 30 seconds, at which point Ms Maibom was still rifling through the tax legislation, a job she estimated would have taken her an hour.

The AlphaBeta analysis shows that legal clerks, who fall under the category "Miscellaneous clerical and administrative workers", spend 30 per cent of their time on automatable tasks.

"What's different about the jobs lost to automation today is they are affecting more white collar workers in the service industries and creeping into the middle- and high-income bracket," economist and director of AlphaBeta Andrew Charlton said.

"That's the difference in the way that automation is affecting jobs today."

Job name Time on more automatable tasks (%) Number employed Average weekly ordinary earnings ($) Women (%) Construction and Mining Labourers 86 183570 1407.7 2.2 Glaziers, Plasterers and Tilers 85 84030 1278.9 1.7 Floor Finishers and Painting Trades Workers 84 58510 NA 3.6 Food Preparation Assistants 84 190200 1084.5 53.0 Cleaners and Laundry Workers 77 275340 944.6 60.9 Fabrication Engineering Trades Workers (eg. Sheet metal workers) 76 79660 1238 1.0 Horticultural Trades Workers (eg. Florists) 75 95420 1097.9 16.9 Packers and Product Assemblers 70 90840 924.8 48.8 Automotive Electricians and Mechanics 69 114880 1315 1.2 Mobile Plant Operators (eg. Forklift drivers) 68 135470 1268.1 3.5 Stationary Plant Operators (eg. Crane operators) 68 104960 1896.6 5.4 Panelbeaters, and Vehicle Body Builders, Trimmers and Painters 67 39400 1178.2 2.1 Printing Trades Workers 66 18020 1170.6 18.6 Wood Trades Workers (eg. cabinetmakers) 66 37590 1035.5 4.8 Textile, Clothing and Footwear Trades Workers (eg. upholsterers) 64 12730 996.8 51.5 Storepersons 63 120050 1014.3 18.2 Machine Operators (eg. Sewing machinists) 62 58190 1110.5 28.0 Food Process Workers 62 68660 1122.6 28.1 Plumbers 60 86430 1518.6 0.9 Hospitality Workers 58 298050 1035.8 69.2 Miscellaneous Technicians and Trades Workers (eg. Signwriters) 56 61450 1792.1 31.4 Farm, Forestry and Garden Workers 56 121620 909.7 24.9 Bricklayers, and Carpenters and Joiners 55 146990 1416.8 0.8 Mechanical Engineering Trades Workers 55 128930 1741.3 1.5 Keyboard Operators 52 59050 1125.6 85.0 Arts Professionals (eg. Photographers) 51 42190 1671.2 44.5 Miscellaneous Factory Process Workers 49 50230 1016.4 23.5 Logistics Clerks 49 124730 1293.1 40.8 Truck Drivers 48 193030 1261 3.3 Miscellaneous Labourers (eg. Caretakers) 46 151880 1116.7 17.7 Child Carers 46 149850 903.5 95.9 Information and Communications Technology and Telecommunications Technicians 45 63270 1478.2 19.9 Freight Handlers and Shelf Fillers 44 89530 1023.5 32.9 Clerical and Office Support Workers (eg. Switchboard operators) 44 84740 1094.4 47.3 Prison and Security Officers 44 78300 1252.4 18.0 Natural and Physical Science Professionals (eg. Geologists) 44 115520 1916.8 44.6 Automobile, Bus and Rail Drivers 41 114030 1527.7 9.3 Electricians 41 155650 1656.3 1.3 Food Trades Workers (eg. Chefs, cooks) 40 186740 1108.6 32.2 Database and Systems Administrators, and Information and Communications Technology Security Specialists 39 34310 2023.1 21.5 Personal Assistants and Secretaries 39 102540 1344 97.8 Electronics and Telecommunications Trades Workers 36 78850 1428.4 3.1 Sales Assistants and Salespersons 35 730570 1001 65.1 Receptionists 35 158980 961.2 95.5 Financial and Insurance Clerks 34 113990 1267.5 68.4 Education Aides 34 91780 1197.8 91.7 Call or Contact Centre Information Clerks 32 115280 1137.4 69.2 Miscellaneous Clerical and Administrative Workers (eg. Legal clerks) 31 111710 1311.3 63.4 Personal Carers and Assistants 30 289690 1132.7 82.7 Accountants, Auditors and Company Secretaries 29 207960 1667.7 48.3 Delivery Drivers 29 43720 905.6 10.8 Agricultural, Medical and Science Technicians 29 50710 1347.1 63.3 Sales, Marketing and Public Relations Professionals 28 130240 1870.2 49.0 Accounting Clerks and Bookkeepers 28 270460 1235.1 85.5 Information and Organisation Professionals (eg. Economists, librarians) 28 145640 1931.7 49.3 Health Diagnostic and Promotion Professionals (eg. Pharmacists) 25 97830 1725.8 59.0 Information and Communication Technology Network and Support Professionals 25 48440 2043.2 13.9 Personal Service and Travel Workers (eg. Beauty therapists) 25 106210 1134.9 74.8 Retail Managers 23 229780 1403.1 47.6 Human Resource and Training Professionals 22 93040 1746 65.5 School Teachers 22 401470 1673 75.1 General Clerks 21 240310 1106.7 86.4 Defence Force Members, Fire Fighters and Police 21 74070 1815.6 19.3 Air and Marine Transport Professionals 21 22160 2409.1 6.4 Medical Practitioners 21 106710 3397.7 39.5 Accommodation and Hospitality Managers 21 107430 1297.2 50.4 Health and Welfare Support Workers (eg. Paramedics) 20 116310 1390.5 71.2 Media Professionals (eg. Journalists) 20 64070 1644.3 47.2 Farmers and Farm Managers 20 143470 NA 28.4 Miscellaneous Education Professionals (eg. Private tutors) 19 68940 1599.5 71.5 Animal Attendants and Trainers, and Shearers 18 39230 942.6 65.3 Hairdressers 18 53530 974.1 86.1 Health Therapy Professionals (eg. Dentists) 18 90160 1522.5 67.0 Sports and Fitness Workers 18 92010 1477.5 49.7 Tertiary Education Teachers 18 75960 2229.1 48.5 Business and Systems Analysts, and Programmers (eg. Web developers) 17 142470 1950.4 19.7 Architects, Designers, Planners and Surveyors 17 139530 1553.9 41.7 Midwifery and Nursing Professionals 16 325200 1837.1 90.5 Chief Executives, General Managers and Legislators 15 107800 2666.2 23.0 Financial Brokers and Dealers, and Investment Advisers 15 99180 2245.3 30.6 Social and Welfare Professionals (eg. Psychologists) 14 141760 1456.3 68.6 Office and Practice Managers 14 157780 1479 86.4 Advertising, Public Relations and Sales Managers 14 146490 2340.4 37.5 Business Administration Managers 14 138640 2597.2 50.1 Miscellaneous Hospitality, Retail and Service Managers (eg. Fitness centre managers) 14 178260 1758.9 39.9 Education, Health and Welfare Services Managers (eg. School principals) 13 75750 2147 68.1 Legal Professionals (eg. Solicitors) 13 87160 1950 46.3 Construction, Distribution and Production Managers 13 256160 2300.5 13.0 Checkout Operators and Office Cashiers 13 130550 919 77.4 Building and Engineering Technicians (eg. Safety inspectors) 13 138850 1889.2 10.6 Miscellaneous Sales Support Workers (eg. Telemarketers) 13 51070 1211.2 69.6 Miscellaneous Specialist Managers 12 56050 2483 32.7 Information and Communications Technology Managers 12 63180 2539.7 21.1 Engineering Professionals 10 136400 2289.6 9.4 Real Estate Sales Agents 9 87630 1274.4 48.1 Insurance Agents and Sales Representatives 7 89240 1458.8 35.1 Contract, Program and Project Administrators 7 121080 1651.1 58.3

Notes on the data

The new report by strategic consulting company AlphaBeta and commissioned by Google, worked out how much time is spent on tasks that could be done by a machine for nearly 100 Australian jobs.

They calculated the time spent on three task groups that are hard to automate: interpersonal, creative and decision-making and information synthesis; and three that are easier to automate: information analysis, unpredictable physical and predictable physical.

Jobs are listed by the Australian and New Zealand Standard Classification of Occupations three-digit codes.

Proportions of men and women and the age of people working in each job came from the 2011 census data.

Numbers employed in each job was from the ABS labour force detailed quarterly data, May 2017.

Weekly earnings came from the ABS employee earnings and hours data, May 2016 and represents the average weekly ordinary time earnings (excluding overtime) of full-time non-managerial employees paid at the adult rate.

Topics: robots-and-artificial-intelligence, work, business-economics-and-finance, australia

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