40% of time spent on chores could be automated within 10 years – AI experts
- Grocery shopping is the most ‘automatable’ household task
- Childcare is the least automatable domestic task
- Experts in UK and Japan divided on how much time will be saved.
Four in ten hours currently devoted to unpaid housework and care responsibilities could be automated within the decade, according to research from the University of Oxford and Ochanomizu University, published today in the journal PLOS ONE.
Dr Lulu Shi, Postdoctoral Researcher, Oxford Internet Institute and Departmental Lecturer, Department of Education, University of Oxford, said:
“Our research suggests that on average around 39% of our time spent on doing domestic work can be automated in the next ten years. The degree of automation varies substantially across different types of work, however: Only 28% of care work, including activities such as teaching your child, accompanying your child, or taking care of an elderly family member, is predicted to be automated. Yet 44% of housework, including cooking, cleaning, and shopping, are expected to be automatable”.
The findings are based on responses from AI experts in the UK and Japan, when asked what difference automation was going to make to housework and other unpaid work. The researchers found the estimates were influenced by the personal background of the experts.
Ekaterina Hertog, associate professor in AI and Society, Oxford Internet Institute and Ethics in AI Institute, explains, ‘We found male and female experts had different expectations about automation of domestic work, potentially reflecting the differences in their lived experiences with technology as well as their involvement in housework and care work.’
The research found male UK experts tended to be more optimistic about domestic automation compared with their female counterparts. This is in line with previous studies, which show men tend to be more optimistic about technology than women. But this was reversed for Japanese male and female experts – and the authors speculate the Japanese gender disparity in household tasks could play a role in these results.
Nobuko Nagase, Professor of Labour Economics and Social Policy at Ochanomizu University said,
“In Japan, most AI experts are male. As male experts in Japan expect domestic automation to save much less time compared to their counterparts in the UK this may mean that research and development in this area are not receiving the attention they deserve and household automation may be delayed in Japan. This is alarming for me as a woman, and I say we need to expand the number of female AI experts.”
According to the study, the general level of optimism in respect of domestic automation also varied by country. On average, UK-based experts thought automation could reduce domestic work time by 42%, compared with a 36% reduction expected by Japanese respondents. The authors suggest this may be because technology is associated more with labour replacement in the UK. In Japan, meanwhile, new smart technologies are expected to work alongside humans rather than replace them.
Previous studies show working-age people in the UK spend nearly 50% of all their work and study time on unpaid domestic work such as cooking, cleaning, and care. The new findings suggest a large potential increase in leisure time as domestic tasks get automated.
The effects are likely to affect women more than men, however: In the UK, working age men spend around half as much time on domestic unpaid work as working age women. In Japan, the difference in time spent on domestic tasks is even more striking, with Japanese men spending just 18% of the time spent by women on domestic tasks. Technologies that save time currently spent on domestic work could, therefore, result in greater gender equality at home, according to the researchers.
Download the full paper, ‘ The Future(s) of unpaid work: How susceptible do experts from different backgrounds think the domestic sphere is to automation?’
Authors: Professor Vili Lehdonvirta, Oxford Internet Institute, University of Oxford; Dr Lulu Shi, Postdoctoral Researcher, Oxford Internet Institute and Departmental Lecturer, Department of Education, University of Oxford; Ekaterina Hertog, Associate Professor in AI and Society, Oxford Internet Institute and Ethics in AI Institute, University of Oxford; and Professor Nobuko Nagase and Professor Yuji Ohta, Graduate School of Humanities and Sciences, Ochanomizu University.
This project has been funded by ESRC in the UK and JST-RISTEX in Japan.
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