AI could help us cut down 39% of our chores within the decade

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Photo by Annie Spratt on Unsplash
Photo by Annie Spratt on Unsplash

Artificial intelligence could remove 39% of the time we spend on household chores within the next 10 years, according to predictions from a selection of UK and Japanese AI experts. Researchers asked 60 experts across the two countries to estimate how well technology could automate various common domestic tasks. The experts predicted the most automatable task to be grocery shopping, while childcare was the least automatable. The researchers also compared how gender and country impacted the experts' predictions, with the UK experts more optimistic about how much domestic labour could be automated. While UK men were more optimistic than their female counterparts, the opposite was the case for Japanese men and women. 

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From: PLOS

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AI experts suggest 39 percent of time currently spent on chores could be automated within the next decade

Grocery shopping was perceived as the most automatable task, with childcare

as the least automatable

On average, 39 percent of time currently spent on unpaid domestic work could be automated within the next decade, suggest AI experts from the UK and Japan. The findings are published February 22, 2023 in the open-access journal PLOS ONE by a team led by Ekaterina Hertog at the University Oxford, UK, and colleagues in Japan.

According to previous studies, people in the UK aged 15 to 64 spend about 43 percent of all their work and study time on unpaid domestic work (housework like cooking and cleaning, as well as child or elder care, that could theoretically be delegated to a paid worker or replaced by market goods). In the UK, working-age men spend around half as much time as working-age women do on such work, and in Japan, the same figure is just 18 percent. However, few studies to date have examined automation in relation to unpaid domestic work, or how predictions about automation differ depending on the AI experts consulted. The authors of the present study asked 29 male and female AI experts from the UK and 36 experts from Japan to estimate how automatable 17 housework and care work tasks might be over the next decade.

The experts predicted that on average 39 percent of the time that people currently spend on any given domestic work task could be automated within the next ten years. Their estimates varied significantly between tasks, with the most automatable task predicted to be grocery shopping (59 percent). The least automatable task was physical childcare (21 percent). UK-based experts believed automation might replace more domestic labor (42 percent) than Japanese experts (36 percent). The authors suggest this may be because in the UK, technology is associated more with labor replacement compared to in Japan.

UK male experts tended to be more optimistic about domestic automation compared to UK female experts, which falls in line with previous studies showing that men tend to be more optimistic about technology than women in general. However, this trend was reversed for Japanese experts, with female experts being slightly more optimistic; the authors consider if the Japanese gender disparity in household tasks plays a role in these results.

Though the study’s diverse sample is not statistically representative of the field and is too small to generalize the findings to all AI experts, the authors note that examining experts’ backgrounds may contextualize their forecasting predictions. They also emphasize how these predictions don’t just anticipate the future of work, but also shape it, such that bringing greater cultural and gender diversity to future research is important.

The authors add: “Our study with technology experts in the UK and Japan finds that in 10 years’ time domestic automation could reduce the amount of time spent on current housework and care work tasks by 39%.”

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Organisation/s: University of Oxford, UK
Funder: This research was supported by a UKJapan collaborative grant jointly awarded by UK Research and Innovation (grant number ES/ T007265/1; PI Ekaterina Hertog) and by the Research Institute of Science and Technology for Society (RISTEX) of the Japan Science and Technology Agency (grant number JPMJRX19H4; PI Nobuko Nagase). This project also benefited from funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme under grant agreement No 681546 (FAMSIZEMATTERS) and grant agreement No 771736 (GENTIME). The funders did not play any role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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