Unpaid household work is a time-consuming activity that impacts economic and social well-being, is vital for human reproduction, and enables all other forms of work. Recent debates about the “future of work” have engaged with the impact of technology on paid labour but have yet to address unpaid labour. My research project team looks at the transformative power of domestic automation. We look at this from several different angles:
First, we attempt to measure the impact of ‘smart’ technological automation on unpaid work. We focus on two questions: (a) what is the likelihood that various types of unpaid work will be automated? And (b) what is the possible impact of such automation on time currently spent on domestic work and on gender equality regarding participation in unpaid and paid work? We use three established estimates of the automation likelihood for paid work occupations as proxies for the likelihood of automation of similar housework and care work activities. We match paid work occupations with a harmonized list of 19 housework and care work activities in Japanese and UK national time use data. This matching enables us to estimate several plausible scenarios of how automating a variety of unpaid work activities may impact unpaid workloads across gender and age groups in Japan and the UK.
Second, we carry out a forecasting exercise in which 64 AI experts from the UK and Japan estimate how automatable 17 housework and care work tasks are. Unlike previous studies, which take expert predictions as objective fact, we draw on sociological and feminist literature to understand how experts’ diverse backgrounds may shape their visions. On average the experts predict that 39 percent of the time spent on a domestic task is automatable within ten years. Japanese male experts are notably pessimistic about the potentials of domestic automation, a result we interpret through gender disparities in the Japanese household. Without dismissing the practice of forecasting entirely, we demonstrate how predictions are socially contingent. This is important, as regardless of how accurate predictions about automation turn out to be, they are influencing present-day policies.
Finally, using in-depth, semi-structured interviews with 16 Alexa users, we look at the ways users interact with their domestic voice assistants as a case study of human-technology interaction in the context of low trust and information asymmetry. We uncover three different strategies adopted by individuals to manage the presence of what they typically perceive as a surveillance device in their homes and discuss consumers’ ability to rationally manage the risks of domestic technology in daily life.
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